Efferent activation of the cervical vagus nerve (cVN) dampens systemic inflammatory processes, potentially modulating a wide-range of inflammatory pathological conditions. In contrast, afferent cVN activation amplifies systemic inflammatory processes, leading to activation of the hypothalamic-pituitary-adrenal (HPA) axis, the sympathetic nervous system through the greater splanchnic nerve (GSN), and elevation of pro-inflammatory cytokines. Ideally, to clinically implement anti-inflammatory therapy via cervical vagus nerve stimulation (cVNS) one should selectively activate the efferent pathway. Unfortunately, current implementations, in animal and clinical investigations, activate both afferent and efferent pathways. We paired cVNS with kilohertz electrical stimulation (KES) nerve block to preferentially activate efferent pathways while blocking afferent pathways. Selective efferent cVNS enhanced the anti-inflammatory effects of cVNS. Our results demonstrate that: (i) afferent, but not efferent, cVNS synchronously activates the GSN in a dose-dependent manner; (ii) efferent cVNS enabled by complete afferent KES nerve block enhances the anti-inflammatory benefits of cVNS; and (iii) incomplete afferent KES nerve block exacerbates systemic inflammation. Overall, these data demonstrate the utility of paired efferent cVNS and afferent KES nerve block for achieving selective efferent cVNS, specifically as it relates to neuromodulation of systemic inflammation.

Fibroblasts play a significant role in the development of electrical and mechanical dysfunction of the heart; however, the underlying mechanisms are only partially understood. One widely studied mechanism suggests that fibroblasts produce excess extracellular matrix, resulting in collagenous septa that slow propagation, cause zig-zag conduction paths, and decouple cardiomyocytes, resulting in a substrate for cardiac arrhythmia. An emerging mechanism suggests that fibroblasts promote arrhythmogenesis through direct electrical interactions with cardiomyocytes via gap junction (GJ) channels. In the heart, three major connexin (Cx) isoforms, Cx40, Cx43, and Cx45, form GJ channels in cell-type-specific combinations. Because each Cx is characterized by a unique time- and transjunctional voltage-dependent profile, we investigated whether the electrophysiological contributions of fibroblasts would vary with the specific composition of the myocyte-fibroblast (M-F) GJ channel. Due to the challenges of systematically modifying Cxs in vitro, we coupled native cardiomyocytes with in silico fibroblast and GJ channel electrophysiology models using the dynamic-clamp technique. We found that there is a reduction in the early peak of the junctional current during the upstroke of the action potential (AP) due to GJ channel gating. However, effects on the cardiomyocyte AP morphology were similar regardless of the specific type of GJ channel (homotypic Cx43 and Cx45, and heterotypic Cx43/Cx45 and Cx45/Cx43). To illuminate effects at the tissue level, we performed multiscale simulations of M-F coupling. First, we developed a cell-specific model of our dynamic-clamp experiments and investigated changes in the underlying membrane currents during M-F coupling. Second, we performed two-dimensional tissue sheet simulations of cardiac fibrosis and incorporated GJ channels in a cell type-specific manner. We determined that although GJ channel gating reduces junctional current, it does not significantly alter conduction velocity during cardiac fibrosis relative to static GJ coupling. These findings shed more light on the complex electrophysiological interplay between cardiac fibroblasts and myocytes.

We used the Dynamic Clamp technique for i) comparative validation of conflicting computational models of the hyperpolarization-activated funny current, If, and ii) quantification of the role of If in mediating autonomic modulation of heart rate. Experimental protocols based on the injection of a real-time recalculated synthetic If current in sinoatrial rabbit cells were developed.

Repolarization and its stability are exquisitely sensitive to IKr features. Information on the relative importance of specific IKr abnormalities is missing and would assist in the evaluation of arrhythmogenic risk.

The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.

Background — Transient outward K currents (Ito) have been reported both to suppress and facilitate early afterdepolarizations (EADs) when repolarization reserve is reduced. Here we used the dynamic clamp technique to analyze how Ito accounts for these paradoxical effects on EADs by influencing the dynamic evolution of repolarization reserve during the action potential.

One often-overlooked factor when selecting a platform for large-scale electrophysiology is whether or not a particular data acquisition system is ‘open’ or ‘closed’: that is, whether or not the system’s schematics and source code are available to end users. Open systems have a reputation for being difficult to acquire, poorly documented, and hard to maintain. With the arrival of more powerful and compact integrated circuits, rapid prototyping services, and web-based tools for collaborative development, these stereotypes must be reconsidered. We discuss some of the reasons why multichannel extracellular electrophysiology could benefit from open-source approaches and describe examples of successful community-driven tool development within this field. In order to promote the adoption of open-source hardware and to reduce the need for redundant development efforts, we advocate a move toward standardized interfaces that connect each element of the data processing pipeline. This will give researchers the flexibility to modify their tools when necessary, while allowing them to continue to benefit from the high-quality products and expertise provided by commercial vendors.

In the present work Action-Potential clamp (APC) and Dynamic clamp (DC) were used in combination in order to optimize the Luo–Rudy (LRd) mathematical formulation of the guinea-pig rapid delayed rectifier K+ current (IKr), and to validate the optimized model. To this end, IKr model parameters were adjusted to fit the experimental E4031-sensitive current (IE4031) recorded under APC in guinea-pig myocytes. Currents generated by LRd model (ILRd) and the optimized one (IOpt) were then compared by testing their suitability to replace IE4031 under DC.

Stellate cells (SCs) of the medial entorhinal cortex exhibit robust spontaneous membrane-potential oscillations (MPOs) in the theta (4–12 Hz) frequency band as well as theta-frequency resonance in their membrane impedance spectra. Past experimental and modeling work suggests that these features may contribute to the phase-locking of SCs to the entorhinal theta rhythm and may be important for forming the hexagonally tiled grid cell place fields exhibited by these neurons in vivo. Among the major biophysical mechanisms contributing to MPOs is a population of persistent (non-inactivating or slowly inactivating) sodium channels. The resulting persistent sodium conductance (GNaP) gives rise to an apparent increase in input resistance as the cell approaches threshold. In this study, we used dynamic clamp to test the hypothesis that this increased input resistance gives rise to voltage-dependent, and thus MPO phase-dependent, changes in the amplitude of excitatory and inhibitory post-synaptic potential (PSP) amplitudes. We find that PSP amplitude depends on membrane potential, exhibiting a 5–10% increase in amplitude per mV depolarization. The effect is larger than—and sums quasi-linearly with—the effect of the synaptic driving force, V - Esyn. Given that input-driven MPOs 10 mV in amplitude are commonly observed in MEC stellate cells in vivo, this voltage- and phase-dependent synaptic gain is large enough to modulate PSP amplitude by over 50% during theta-frequency MPOs. Phase-dependent synaptic gain may therefore impact the phase locking and phase precession of grid cells in vivo to ongoing network oscillations. © 2014 Wiley Periodicals, Inc.

A rich set of genetic tools and extensive anatomical data make the olfactory system of the fruit fly a neural circuit of choice for studying function in sensory systems. Though a substantial amount of work has been published on the neural coding of olfactory sensory neurons (OSNs) of the fruit fly, yet little is known how projection neurons (PNs) encode time-varying odor stimuli [1]. Here we address this question with in vivo experiments coupled with a phenomenological characterization of the spiking activity of PNs.

The injection of computer-simulated conductances through the dynamic clamp technique has allowed researchers to probe the intercellular and intracellular dynamics of cardiac and neuronal systems with great precision. By coupling computational models to biological systems, dynamic clamp has become a proven tool in electrophysiology with many applications, such as generating hybrid networks in neurons or simulating channelopathies in cardiomyocytes. While its applications are broad, the approach is straightforward: synthesizing traditional patch clamp, computational modeling, and closed-loop feedback control to simulate a cellular conductance. Here, we present two example applications: artificial blocking of the inward rectifier potassium current in a cardiomyocyte and coupling of a biological neuron to a virtual neuron through a virtual synapse. The design and implementation of the necessary software to administer these dynamic clamp experiments can be difficult. In this chapter, we provide an overview of designing and implementing a dynamic clamp experiment using the Real-Time eXperiment Interface (RTXI), an open-source software system tailored for real-time biological experiments. We present two ways to achieve this using RTXI’s modular format, through the creation of a custom user-made module and through existing modules found in RTXI’s online library.

Cardiac alternans, a putative trigger event for cardiac reentry, is a beat-to-beat alternation in membrane potential and calcium transient. Alternans was originally attributed to instabilities in transmembrane ion channel dynamics (i.e., the voltage mechanism). As of this writing, the predominant view is that instabilities in subcellular calcium handling are the main underlying mechanism. That being said, because the voltage and calcium systems are bidirectionally coupled, theoretical studies have suggested that both mechanisms can contribute. To date, to our knowledge, no experimental evidence of such a dual role within the same cell has been reported. Here, a combined electrophysiological and calcium imaging approach was developed and used to illuminate the contributions of voltage and calcium dynamics to alternans. An experimentally feasible protocol, quantification of subcellular calcium alternans and restitution slope during cycle-length ramping alternans control, was designed and validated. This approach allows simultaneous illumination of the contributions of voltage and calcium-driven instability to total cellular instability as a function of cycle-length. Application of this protocol in in vitro guinea-pig left-ventricular myocytes demonstrated that both voltage- and calcium-driven instabilities underlie alternans, and that the relative contributions of the two systems change as a function of pacing rate.

We used phase resetting methods to predict firing patterns of rat subthalamic nucleus (STN) neurons when their rhythmic firing was densely perturbed by noise. We applied sequences of contiguous brief (0.5–2 ms) current pulses with amplitudes drawn from a Gaussian distribution (10–100 pA standard deviation) to autonomously firing STN neurons in slices. Current noise sequences increased the variability of spike times with little or no effect on the average firing rate. We measured the infinitesimal phase resetting curve (PRC) for each neuron using a noise-based method. A phase model consisting of only a firing rate and PRC was very accurate at predicting spike timing, accounting for more than 80% of spike time variance and reliably reproducing the spike-to-spike pattern of irregular firing. An approximation for the evolution of phase was used to predict the effect of firing rate and noise parameters on spike timing variability. It quantitatively predicted changes in variability of interspike intervals with variation in noise amplitude, pulse duration and firing rate over the normal range of STN spontaneous rates. When constant current was used to drive the cells to higher rates, the PRC was altered in size and shape and accurate predictions of the effects of noise relied on incorporating these changes into the prediction. Application of rate-neutral changes in conductance showed that changes in PRC shape arise from conductance changes known to accompany rate increases in STN neurons, rather than the rate increases themselves. Our results show that firing patterns of densely perturbed oscillators cannot readily be distinguished from those of neurons randomly excited to fire from the rest state. The spike timing of repetitively firing neurons may be quantitatively predicted from the input and their PRCs, even when they are so densely perturbed that they no longer fire rhythmically.

For over 60 years, real-time control has been an important technique in the study of excitable cells. Two such control-based technologies are reviewed here. First, voltage-clamp methods revolutionized the study of excitable cells. In this family of techniques, membrane potential is controlled, allowing one to parameterize a powerful class of models that describe the voltage-current relationship of cell membranes simply, flexibly, and accurately. Second, dynamic-clamp methods allow the addition of new, “virtual” membrane mechanisms to living cells. Dynamic clamp allows researchers unprecedented ways of testing computationally based hypotheses in biological preparations. The review ends with predictions of how control-based technologies will be improved and adapted for new uses in the near future.

The subiculum, considered to be the output structure of the hippocampus, modulates information flow from the hippocampus to various cortical and sub-cortical areas such as the nucleus accumbens, lateral septal region, thalamus, nucleus gelatinosus, medial nucleus and mammillary nuclei. Tonic inhibitory current plays an important role in neuronal physiology and pathophysiology by modulating the electrophysiological properties of neurons. While the alterations of various electrical properties due to tonic inhibition have been studied in neurons from different regions, its influence on intrinsic subthreshold resonance in pyramidal excitatory neurons expressing hyperpolarization-activated cyclic nucleotide-gated (HCN) channels is not known. Using pharmacological agents, we show the involvement of α5βγ GABAA receptors in the picrotoxin-sensitive tonic current in subicular pyramidal neurons. We further investigated the contribution of tonic conductance in regulating subthreshold electrophysiological properties using current clamp and dynamic clamp experiments. We demonstrate that tonic GABAergic inhibition can actively modulate subthreshold properties, including resonance due to HCN channels, which can potentially alter the response dynamics of subicular pyramidal neurons in an oscillating neuronal network.

Rewarding stimuli in associative learning can transform the irregularly and infrequently generated motor patterns underlying motivated behaviors into output for accelerated and stereotyped repetitive action. This transition to compulsive behavioral expression is associated with modified synaptic and membrane properties of central neurons, but establishing the causal relationships between cellular plasticity and motor adaptation has remained a challenge.

Dynamic clamp is an electrophysiological technique for introducing simulated electrical components into biological cells using a real-time closed loop between the cell and a computer or another electronic device. Classic dynamic clamp protocols build a voltage-dependent current injection cycle to implement artificial membrane or synaptic conductances in the cell membrane of biological neurons. These protocols are employed to assess a large variety of neuronal computational properties and are widely applied for studying the physiology of neural systems at the cellular and circuit levels.

Transcranial Alternating Current Stimulation (tACS) is a novel non-invasive brain stimulation modality. tACS passes weak, oscillating electric currents through the scalp to generate electric fields in the cerebral cortex. tACS has been shown to modulate both cortical oscillations and cognitive function in humans. These findings suggest that tACS is a promising neurotherapeutic for the treatment of psychiatric illnesses with aberrant underlying cortical dynamics such as schizophrenia. Due to the complexity of the network dynamics in brains of patients with neuropsychiatric disorders, it is unlikely that the feedforward application of tACS without neuronal feedback will be sufficient to effectively control the pathological network dynamics. To our knowledge, no successful EEG-based feedback brain stimulation with tACS has been reported to date. We here present an EEG feedback-controlled tACS system that controls cortical state dynamics. We used occipitoparietal stimulation in the gamma frequency band (40 Hz) in response to the endogenous alpha frequency activity in the visual cortex. We found that EEG feedback-controlled 40 Hz tACS (FB tACS) was more effective at controlling the amplitude of alpha-band cortical oscillations than dose-matched randomly-administered 40 Hz tACS (RA tACS). Our novel system therefore represents an important step towards the development of individualized therapeutic brain stimulation that is based on feedback control.

We studied single neuron dynamics during anoxic depolarizations, which are often observed in cases of neuronal energy depletion. Anoxic and similar depolarizations play an important role in several pathologies, notably stroke, migraine, and epilepsy. One of the effects of energy depletion was experimentally simulated in slices of rat cortex by blocking the sodium-potassium pumps with ouabain. The membrane voltage of pyramidal cells was measured. Five different kinds of dynamical behavior of the membrane voltage were observed during the resulting depolarizations. Using bifurcation analysis of a single cell model, we show that these voltage dynamics all are responses of the same cell, with normally functioning ion channels, to particular courses of the intra- and extracellular concentrations of sodium and potassium.

OBJECTIVE: To demonstrate the applicability of optimal control theory for designing minimum energy charge-balanced input waveforms for single periodically-firing in vitro neurons from brain slices of Long-Evans rats.

Phase response curves (PRCs) are a simple model of how a neuron’s spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron’s PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onset of a spike, used to make the neuron fire at different rates, and 2) a pulse to emulate a synaptic input. A model of the applied constant current and the history is made to predict the interspike interval (ISI). A second model is then made to fit the modulation of the spike time from the expected ISI by the pulsatile stimulus. A polynomial with two independent variables, the stimulus phase and the expected ISI, is used to model the pPRC. The pPRC is validated in a computational model and applied to pyramidal neurons from the CA1 region of the hippocampal slices from rat. The pPRC can be used to model the effect of changing firing rates on network synchrony. It can also be used to characterize the effects of neuromodulators and genetic mutations (among other manipulations) on network synchrony. It can also easily be extended to account for more variables.

Understanding central processing requires precise monitoring of neural activity across populations of identified neurons in the intact brain. In the present study, we used recently optimized variants of the genetically encoded calcium sensor GCaMP (GCaMP3 and GCaMPG5G) to image activity among genetically and anatomically defined neuronal populations in the olfactory bulb (OB), including two types of GABAergic interneurons (periglomerular [PG] and short axon [SA] cells) and OB output neurons (mitral/tufted [MT] cells) projecting to the piriform cortex. We first established that changes in neuronal spiking can be related accurately to GCaMP fluorescence changes via a simple quantitative relationship over a large dynamic range. We next used in vivo two-photon imaging from individual neurons and epifluorescence signals reflecting population-level activity to investigate the spatiotemporal representation of odorants across these neuron types in anesthetized and awake mice. Under anesthesia, individual PG and SA cells showed temporally simple responses and little spontaneous activity, whereas MT cells were spontaneously active and showed diverse temporal responses. At the population level, response patterns of PG, SA, and MT cells were surprisingly similar to those imaged from sensory inputs, with shared odorant-specific topography across the dorsal OB and inhalation-coupled temporal dynamics. During wakefulness, PG and SA cell responses increased in magnitude but remained temporally simple, whereas those of MT cells changed to complex spatiotemporal patterns reflecting restricted excitation and widespread inhibition. These results suggest multiple circuit elements with distinct roles in transforming odor representations in the OB and provide a framework for further study of early olfactory processing using optical and genetic tools.

Studies of behavioral and neural responses to distorted auditory feedback (DAF) can help shed light on the neural mechanisms of animal vocalizations. We describe an apparatus for generating real-time acoustic feedback. The system can very rapidly detect acoustic features in a song and output acoustic signals if the detected features match the desired acoustic template. The system uses spectrogram-based detection of acoustic elements. It is low-cost and can be programmed for a variety of behavioral experiments requiring acoustic feedback or neural stimulation. We use the system to study the effects of acoustic feedback on birds’ vocalizations and demonstrate that such an acoustic feedback can cause both immediate and long-term changes to birds’ songs.

Recent studies have shown that stellate cells (SCs) of the medial entorhinal cortex become hyper-excitable in animal models of temporal lobe epilepsy. These studies have also demonstrated the existence of recurrent connections among SCs, reduced levels of recurrent inhibition in epileptic networks as compared to control ones, and comparable levels of recurrent excitation among SCs in both network types. In this work, we investigate the biophysical and dynamic mechanism of generation of the fast time scale corresponding to hyper-excitable firing and the transition between theta and fast firing frequency activity in SCs. We show that recurrently connected minimal networks of SCs exhibit abrupt, threshold-like transition between theta and hyper-excitable firing frequencies as the result of small changes in the maximal synaptic (AMPAergic) conductance. The threshold required for this transition is modulated by synaptic inhibition. Similar abrupt transition between firing frequency regimes can be observed in single, self-coupled SCs, which represent a network of recurrently coupled neurons synchronized in phase, but not in synaptically isolated SCs as the result of changes in the levels of the tonic drive. Using dynamical systems tools (phase-space analysis), we explain the dynamic mechanism underlying the genesis of the fast time scale and the abrupt transition between firing frequency regimes, their dependence on the intrinsic SC’s currents and synaptic excitation. This abrupt transition is mechanistically different from others observed in similar networks with different cell types. Most notably, there is no bistability involved. ‘In vitro’ experiments using single SCs self-coupled with dynamic clamp show the abrupt transition between firing frequency regimes, and demonstrate that our theoretical predictions are not an artifact of the model. In addition, these experiments show that high-frequency firing is burst-like with a duration modulated by an M-current.

Oscillatory activity in neuronal networks correlates with different behavioral states throughout the nervous system, and the frequency-response characteristics of individual neurons are believed to be critical for network oscillations. Recent in vivo studies suggest that neurons experience periods of high membrane conductance, and that action potentials are often driven by membrane potential fluctuations in the living animal. To investigate the frequency-response characteristics of CA1 pyramidal neurons in the presence of high conductance and voltage fluctuations, we performed dynamic-clamp experiments in rat hippocampal brain slices. We drove neurons with noisy stimuli that included a sinusoidal component ranging, in different trials, from 0.1 to 500 Hz. In subsequent data analysis, we determined action potential phase-locking profiles with respect to background conductance, average firing rate, and frequency of the sinusoidal component. We found that background conductance and firing rate qualitatively change the phase-locking profiles of CA1 pyramidal neurons versus frequency. In particular, higher average spiking rates promoted bandpass profiles, and the high-conductance state promoted phase-locking at frequencies well above what would be predicted from changes in the membrane time constant. Mechanistically, spike rate adaptation and frequency resonance in the spike-generating mechanism are implicated in shaping the different phase-locking profiles. Our results demonstrate that CA1 pyramidal cells can actively change their synchronization properties in response to global changes in activity associated with different behavioral states.

The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

In complex nervous systems patterns of neuronal activity and measures of intrinsic neuronal excitability are often used as criteria for identifying and/or classifying neurons. We asked how well identification of neurons by conventional measures of intrinsic excitability compares with a measure of neuronal excitability derived from a neuron’s behavior in a dynamic clamp constructed two-cell network. We used four cell types from the crab stomatogastric ganglion: the pyloric dilator, lateral pyloric, gastric mill, and dorsal gastric neurons. Each neuron was evaluated for six conventional measures of intrinsic excitability (intrinsic properties, IPs). Additionally, each neuron was coupled by reciprocal inhibitory synapses made with the dynamic clamp to a Morris–Lecar model neuron and the resulting network was assayed for four measures of network activity (network activity properties, NAPs). We searched for linear combinations of IPs that correlated with each NAP, and combinations of NAPs that correlated with each IP. In the process we developed a method to correct for multiple correlations while searching for correlating features. When properly controlled for multiple correlations, four of the IPs were correlated with NAPs, and all four NAPs were correlated with IPs. Neurons were classified into cell types by training a linear classifier on sets of properties, or using k-medoids clustering. The IPs were modestly successful in classifying the neurons, and the NAPs were more successful. Combining the two measures did better than either measure alone, but not well enough to classify neurons with perfect accuracy, thus reiterating that electrophysiological measures of single-cell properties alone are not sufficient for reliable cell identification.

Computational studies as well as in vivo and in vitro results have shown that many cortical neurons fire in a highly irregular manner and at low average firing rates. These patterns seem to persist even when highly rhythmic signals are recorded by local field potential electrodes or other methods that quantify the summed behavior of a local population. Models of the 30-80 Hz gamma rhythm in which network oscillations arise through ‘stochastic synchrony’ capture the variability observed in the spike output of single cells while preserving network-level organization. We extend upon these results by constructing model networks constrained by experimental measurements and using them to probe the effect of biophysical parameters on network-level activity. We find in simulations that gamma-frequency oscillations are enabled by a high level of incoherent synaptic conductance input, similar to the barrage of noisy synaptic input that cortical neurons have been shown to receive in vivo. This incoherent synaptic input increases the emergent network frequency by shortening the time scale of the membrane in excitatory neurons and by reducing the temporal separation between excitation and inhibition due to decreased spike latency in inhibitory neurons. These mechanisms are demonstrated in simulations and in vitro current-clamp and dynamic-clamp experiments. Simulation results further indicate that the membrane potential noise amplitude has a large impact on network frequency and that the balance between excitatory and inhibitory currents controls network stability and sensitivity to external inputs.

How stable synchrony in neuronal networks is sustained in the presence of conduction delays is an open question. The Dynamic Clamp was used to measure phase resetting curves (PRCs) for entorhinal cortical cells, and then to construct networks of two such neurons. PRCs were in general Type I (all advances or all delays) or weakly type II with a small region at early phases with the opposite type of resetting. We used previously developed theoretical methods based on PRCs under the assumption of pulsatile coupling to predict the delays that synchronize these hybrid circuits. For excitatory coupling, synchrony was predicted and observed only with no delay and for delays greater than half a network period that cause each neuron to receive an input late in its firing cycle and almost immediately fire an action potential. Synchronization for these long delays was surprisingly tight and robust to the noise and heterogeneity inherent in a biological system. In contrast to excitatory coupling, inhibitory coupling led to antiphase for no delay, very short delays and delays close to a network period, but to near-synchrony for a wide range of relatively short delays. PRC-based methods show that conduction delays can stabilize synchrony in several ways, including neutralizing a discontinuity introduced by strong inhibition, favoring synchrony in the case of noisy bistability, and avoiding an initial destabilizing region of a weakly type II PRC. PRCs can identify optimal conduction delays favoring synchronization at a given frequency, and also predict robustness to noise and heterogeneity.

The variations of the intracellular concentration of calcium ion ([Ca2+]i) are at the heart of intracellular signaling, and their imaging is therefore of enormous interest. However, passive [Ca2+]i imaging provides no control over these variations, meaning that a full exploration of the functional consequences of [Ca2+]i changes is difficult to attain. The tools designed so far to modify [Ca2+]i, even qualitatively, suffer drawbacks that undermine their widespread use. Here, we describe an electro-optical technique to quantitatively set [Ca2+]i, in real time and with sub-cellular resolution, using two-photon Ca2+ uncaging and dynamic-clamp. We experimentally demonstrate, on neurons from acute olfactory bulb slices of Long Evans rats, various capabilities of this technique previously difficult to achieve, such as the independent control of the membrane potential and [Ca2+]i variations, the functional knocking-in of user-defined virtual voltage-dependent Ca2+ channels, and the standardization of [Ca2+]i patterns across different cells. Our goal is to lay the groundwork for this technique and establish it as a new and versatile tool for the study of cell signaling.

Sudden cardiac death (SCD) due to ventricular fibrillation (VF) is a major world-wide health problem. Common triggers of VF are abnormal repolarizations of the cardiac action potential, known as early afterdepolarizations (EADs). Here we used a hybrid biological-computational approach to investigate the dependence of EADs on the biophysical properties of the L-type Ca(2+) current (I(Ca,L)) and to explore how modifications of these properties could be designed to suppress EADs. EADs were induced in isolated rabbit ventricular myocytes by exposure to 600 ?mol/L H(2)O(2) (oxidative stress) or lowering the external [K(+)] from 5.4 to 2.0-2.7 mmol/L (hypokalemia). The role of I(Ca,L) in EAD formation was directly assessed using the dynamic clamp technique: the paced myocyte’s Vm was input to a myocyte model with tunable biophysical parameters, which computed a virtual I(Ca,L), that was injected into the myocyte in real time, replacing the endogenous I(Ca,L) which was suppressed with nifedipine. Injecting a current with the native I(Ca,L) biophysical properties restored EAD occurrence in myocytes challenged by H(2)O(2) or hypokalemia. A mere 5 mV depolarizing shift in the voltage dependence of activation or a hyperpolarizing shift in the steady-state inactivation curve completely abolished EADs in myocytes while maintaining a normal Ca(i) transient. We propose that modifying I(Ca,L) biophysical properties has potential as a powerful therapeutic strategy for suppressing EADs and EAD-mediated arrhythmias.

Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation. Phase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits. PRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab. This complex is comprised of the Anterior Burster (AB) neuron and two Pyloric Dilator (PD) neurons that are all electrically coupled. Artificial excitatory synaptic conductance pulses of different strengths and durations were injected into one of the AB or PD somata using the Dynamic Clamp. Previously, we characterized the inhibitory PRCs by assuming a single slow process that enabled synaptic inputs to trigger switches between an up state in which spiking occurs and a down state in which it does not. Excitation produced five different PRC shapes, which could not be explained with such a simple model. A separate dendritic compartment was required to separate the mechanism that generates the up and down phases of the bursting envelope (1) from synaptic inputs applied at the soma, (2) from axonal spike generation and (3) from a slow process with a slower time scale than burst generation. This study reveals that due to the nonlinear properties and compartmentalization of ionic channels, the response to excitation is more complex than inhibition.

We describe General Network (GenNet), a software plugin for the real time experimental interface (RTXI) dynamic clamp system that allows for straightforward and flexible implementation of hybrid network experiments. This extension to RTXI allows for hybrid networks that contain an arbitrary number of simulated and real neurons, significantly improving upon previous solutions that were limited, particularly by the number of cells supported. The benefits of this system include the ability to rapidly and easily set up and perform scalable experiments with hybrid networks and the ability to scan through ranges of parameters. We present instructions for installing, running and using GenNet for hybrid network experiments and provide several example uses of the system.

During reinforcement and sequence learning, dopaminergic neurons fire bursts of action potentials. Dopaminergic neurons in vivo receive strong background excitatory and inhibitory inputs, suggesting that one mechanism by which bursts may be produced is disinhibition. Unfortunately, these inputs are lost during slice preparation and are not precisely controlled during in vivo experiments. Here we show that dopaminergic neurons can be shifted into a balanced state in which constant synaptic NMDA and GABAA conductances are mimicked either pharmacologically or using the dynamic clamp. From this state, a disinhibition burst can be evoked by removing the background inhibitory conductance. We demonstrate three functional characteristics of network-based disinhibition that promote high frequency, short latency bursting in dopaminergic neurons. First, we found that increasing the total background NMDA and GABAA synaptic conductances increased the intraburst firing frequency and reduced its latency. Second, we found that the disinhibition burst is sensitive to the proportion of background inhibitory input that is removed. In particular, we found that high frequency, short latency bursts were enhanced by increasing the degree of disinhibition. Third, the time course over which inhibition is removed had a large effect on the burst, namely that synchronous removal of weak inhibitory inputs produces bursts of high intraburst frequency and shorter latency. Our results suggest that fast, more precisely timed bursts can be evoked by complete and synchronous disinhibition of dopaminergic neurons in a high conductance state.

We explain the mechanism that elicits the mixed mode oscillations (MMOs) and the subprimary firing range that we recently discovered in mouse spinal motoneurons. In this firing regime, high-frequency subthreshold oscillations appear a few millivolts below the spike voltage threshold and precede the firing of a full blown spike. By combining intracellular recordings in vivo (including dynamic clamp experiments) in mouse spinal motoneurons and modeling, we show that the subthreshold oscillations are due to the spike currents and that MMOs appear each time the membrane is in a low excitability state. Slow kinetic processes largely contribute to this low excitability. The clockwise hysteresis in the I-F relationship, frequently observed in mouse motoneurons, is mainly due to a substantial slow inactivation of the sodium current. As a consequence, less sodium current is available for spiking. This explains why a large subprimary range with numerous oscillations is present in motoneurons displaying a clockwise hysteresis. In motoneurons whose I-F curve exhibits a counterclockwise hysteresis, it is likely that the slow inactivation operates on a shorter time scale and is substantially reduced by the de-inactivating effect of the afterhyperpolarization (AHP) current, thus resulting in a more excitable state. This accounts for the short subprimary firing range with only a few MMOs seen in these motoneurons. Our study reveals a new role for the AHP current that sets the membrane excitability level by counteracting the slow inactivation of the sodium current and allows or precludes the appearance of MMOs.

We describe an algorithm to control synchrony between two periodically firing neurons. The control scheme operates in real-time using a dynamic clamp platform. This algorithm is a low impact stimulation method that brings the neurons toward the desired level of synchrony over the course of several neuron firing periods. As a proof of principle, we demonstrate the versatility of the algorithm using real- time conductance models, and then show its performance with biological neurons of hippocampal region CA1 and entorhinal cortex.

Some electrophysiology experiments require periodically firing neurons. One example is when measuring a neuron’s phase response curve (PRC) where a neuron is stimulated with a synaptic input and the perturbation in the neuron’s period is measured as a function of when the stimulus is applied. However, even regular spiking cells have considerable variations in their period. These variations can be categorized into two types: jitter, which characterizes the rapid changes in interspike intervals (ISIs) from spike to spike, and drift, which is a slow change in firing rate over seconds. The jitter is removed by averaging the phase advance of a synaptic input applied at a particular phase several times. The drift over long time scales results in a systematic change in the period over the duration of the experiment which cannot be removed by averaging. To compensate for the drift of the neuron over minutes, we designed a linear proportional-integral (PI) controller to slowly adjust the applied current to a neuron to maintain the average firing rate at a desired ISI. The parameters of the controller were calculated based on a first-order discrete model to describe the relationship between ISI and current. The algorithm is demonstrated on pyramidal cells in the hippocampal formation showing ISIs from the neuron in an open loop (constant applied current) and a closed loop (current adjusted by a spike rate controller). The advantages of using the controller can be summarized as: (1) there is a reduction in the transient time to reach a desired ISI, (2) the drift in the ISI is removed allowing for long experiments at a desired spiking rate and (3) the variance is diminished by removing the slow drift. Furthermore, we implemented an auto-tuning algorithm that estimates in real time the coefficients for each clamped neuron. We also show how the controller can improve the PRC estimation. The program runs on Real-Time eXperiment Interface (RTXI), which is Linux-based software for real-time data acquisition and control applications.

It has been shown previously that background synaptic noise modulates the response gain of neocortical neurons. However, the role of the statistical properties of the noise in modulating firing rate is not known. Here, the dependence of firing rate on the statistical properties of the excitatory to inhibitory balance (EI) in cortical pyramidal neurons was investigated. Excitatory glutamatergic and inhibitory GABA-ergic synaptic conductances were simulated as two stochastic processes and injected into individual neurons in vitro through use of the dynamic-clamp system. Response gain was significantly modulated as a function of the statistical interactions between excitatory and inhibitory synaptic conductances. Firing rates were compared for noisy synaptic conductance steps by varying either the EI correlation or the relative delay between correlated E and I. When inhibitory synaptic conductances exhibited a short temporal delay (5 ms) relative to correlated excitatory synaptic conductances, the response gain was increased compared to noise with no temporal delay but with an equivalent degree of correlation. The dependence of neuronal firing rate on the EI delay of the noisy background synaptic conductance suggests that individual excitatory pyramidal neurons are sensitive to the EI balance of the synaptic conductance. Therefore, the statistical EI interactions encoded within the synaptic subthreshold membrane fluctuations are able to modulate neuronal firing properties.

Throughout the brain, neurons encode information in fundamental units of spikes. Each spike represents the combined thresholding of synaptic inputs and intrinsic neuronal dynamics. Here, we address a basic question of spike train formation: how do perithreshold synaptic inputs perturb the output of a spiking neuron? We recorded from single entorhinal principal cells in vitro and drove them to spike steadily at ?5?Hz (theta range) with direct current injection, then used a dynamic-clamp to superimpose strong excitatory conductance inputs at varying rates. Neurons spiked most reliably when the input rate matched the intrinsic neuronal firing rate. We also found a striking tendency of neurons to preserve their rates and coefficients of variation, independently of input rates. As mechanisms for this rate maintenance, we show that the efficacy of the conductance inputs varied with the relationship of input rate to neuronal firing rate, and with the arrival time of the input within the natural period. Using a novel method of spike classification, we developed a minimal Markov model that reproduced the measured statistics of the output spike trains and thus allowed us to identify and compare contributions to the rate maintenance and resonance. We suggest that the strength of rate maintenance may be used as a new categorization scheme for neuronal response and note that individual intrinsic spiking mechanisms may play a significant role in forming the rhythmic spike trains of activated neurons; in the entorhinal cortex, individual pacemakers may dominate production of the regional theta rhythm.

Cardiac action potential duration alternans is characterized by a beat-to-beat alternation in action potential waveform. Its presence has been putatively linked to the onset of lethal cardiac arrhythmias. Previous studies, which have been limited to cardiac Purkinje fibers, have shown that closed-loop alternans control techniques, which apply a succession of externally administered cycle perturbations, provide ineffectual spatial alternans elimination. A more complete understanding of alternans control in the more clinically relevant ventricular tissue is needed. Here, we study the spatial dynamics of alternans and alternans control in arterially perfused canine right ventricular preparations using optical mapping. We quantified the spatial efficacy of alternans control across 2.5 cm of tissue, focusing primarily on differences in spatial control within several sub-regions of tissue. Our results provide a basis for future investigations into multi-electrode-based control interventions of alternans in cardiac tissue.

The Real-time Experiment Interface (RTXI) is a fast and versatile real-time biological experimentation system based on Real-Time Linux. RTXI is open source and free, can be used with an extensive range of experimentation hardware, and can be run on Linux or Windows computers (when using the Live CD). RTXI is currently used extensively for two experiment types: dynamic patch clamp and closed-loop stimulation pattern control in neural and cardiac single cell electrophysiology. RTXI includes standard modules for implementing commonly used electrophysiology protocols with synchronized stimulation, event detection, and online analysis. These and other user-contributed modules can be found on the website (http://www.rtxi.org).

Recent theoretical and experimental work indicates that neurons tune themselves to maintain target levels of excitation by modulating ion channel expression and synaptic strengths. As a result, functionally equivalent circuits can produce similar activity despite disparate underlying network and cellular properties. To experimentally test the extent to which synaptic and intrinsic conductances can produce target activity in the presence of variability in neuronal intrinsic properties, we used the dynamic clamp to create hybrid two-cell circuits built from four types of stomatogastric neurons coupled to the same model Morris–Lecar neuron by reciprocal inhibition. We measured six intrinsic properties (input resistance, minimum membrane potential, firing rate in response to +1 nA of injected current, slope of the frequency–current curve, spike height, and spike voltage threshold) of dorsal gastric, gastric mill, lateral pyloric, and pyloric dilator neurons from male crabs of the species Cancer borealis. The intrinsic properties varied twofold to sevenfold in each cell type. We coupled each biological neuron to the Morris–Lecar model with seven different values of inhibitory synaptic conductance and also used the dynamic clamp to add seven different values of an artificial h-conductance, thus creating 49 different circuits for each biological neuron. Despite the variability in intrinsic excitability, networks formed from each neuron produced similar circuit performance at some values of synaptic and h-conductances. This work experimentally confirms results from previous modeling studies; tuning synaptic and intrinsic conductances can yield similar circuit outputs from neurons with variable intrinsic excitability.

Dopaminergic neurons are subject to a significant background GABAergic input in vivo. The presence of this GABAergic background might be expected to inhibit dopaminergic neuron firing. However, dopaminergic neurons are not all silent but instead fire in single-spiking and burst firing modes. Here we present evidence that phasic changes in the tonic activity of GABAergic afferents are a potential extrinsic mechanism that triggers bursts and pauses in dopaminergic neurons. We find that spontaneous single-spiking is more sensitive to activation of GABA receptors than phasic N-methyl-D-aspartate (NMDA)-mediated burst firing in rat slices (P15-P31). Because tonic activation of GABA(A) receptors has previously been shown to suppress burst firing in vivo, our results suggest that the activity patterns seen in vivo are the result of a balance between excitatory and inhibitory conductances that interact with the intrinsic pacemaking currents observed in slices. Using the dynamic clamp technique, we applied balanced, constant NMDA and GABA(A) receptor conductances into dopaminergic neurons in slices. Bursts could be produced by disinhibition (phasic removal of the GABA(A) receptor conductance), and these bursts had a higher frequency than bursts produced by the same NMDA receptor conductance alone. Phasic increases in the GABA(A) receptor conductance evoked pauses in firing. In contrast to NMDA receptor, application of constant AMPA and GABA(A) receptor conductances caused the cell to go into depolarization block. These results support a bidirectional mechanism by which GABAergic inputs, in balance with NMDA receptor-mediated excitatory inputs, control the firing pattern of dopaminergic neurons.

The brain contains an astonishing diversity of neurons, each expressing only one set of ion channels out of the billions of potential channel combinations. Simple organizing principles are required for us to make sense of this abundance of possibilities and wealth of related data. We suggest that energy minimization subject to functional constraints may be one such unifying principle. We compared the energy needed to produce action potentials singly and in trains for a wide range of channel densities and kinetic parameters and examined which combinations of parameters maximized spiking function while minimizing energetic cost. We confirmed these results for sodium channels using a dynamic current clamp in neocortical fast spiking interneurons. We find further evidence supporting this hypothesis in a wide range of other neurons from several species and conclude that the ion channels in these neurons minimize energy expenditure in their normal range of spiking.

Many neurons exhibit postinhibitory rebound (PIR), in which neurons display enhanced excitability following inhibition. PIR can strongly influence the timing of spikes on rebound from an inhibitory input. We studied PIR in the lateral pyloric (LP) neuron of the stomatogastric ganglion of the crab Cancer borealis. The LP neuron is part of the pyloric network, a central pattern generator that normally oscillates with a period of approximately 1 s. We used the dynamic clamp to create artificial rhythmic synaptic inputs of various periods and duty cycles in the LP neuron. Surprisingly, we found that the strength of PIR increased slowly over multiple cycles of synaptic input. Moreover, this increased excitability persisted for 10-20 s after the rhythmic inhibition was removed. These effects are considerably slower than the rhythmic activity typically observed in LP. Thus this slow postinhibitory rebound allows the neuron to adjust its level of excitability to the average level of inhibition over many cycles, and is another example of an intrinsic “short-term memory” mechanism.

The vast repertoire of electrical activity displayed by neurons, cardiac myocytes, and various endocrine and sensory cells is the result of membrane-bound ion channels each producing a distinct conductance that facilitates current flux through the membrane. These conductances may be static, or their magnitudes may be voltage or ligand dependent. Dynamic clamp, a term for the various combinations of software and hardware that simulate these conductances, has proven to be a valuable tool for electrophysiologists studying excitable cells. One can think of dynamic clamp as a tool for immersing neurons in virtual reality-inspired experiments. Virtual synapses may be simulated, allowing a biological neuron to communicate with or receive inputs from real and simulated cells. Dynamic clamp can also be used to introduce virtual voltage-gated channels into biological neurons, thus altering the neurons integrative properties. This Toolbox article includes a brief description of how conductances are simulated as well as a discussion of the neurophysiological applications of dynamic clamp and its potential pitfalls. The origins of dynamic clamp, its implementation, and its applications to cardiac and endocrine electrophysiology have been reviewed elsewhere (Sharp et al., 1993; Prinz et al., 2003; Goaillard and Marder, 2006; Wilders, 2006; Destexhe and Bal, 2009).

Neocortical networks are composed of diverse populations of cells that differ in their chemical content, electrophysiological characteristics, and connectivity. Gamma-frequency oscillatory activity of inhibitory subnetworks has been hypothesized to regulate information processing in the cortex as a whole. Inhibitory neurons in these subnetworks synchronize their firing and selectively innervate the perisomatic compartments of their target neurons, generating both tonic and rapidly fluctuating inhibition. How do different types of cortical neurons respond to changes in the level and structure of perisomatic inhibition? What accounts for response heterogeneity between cell types, and are these response properties fixed or flexible? To answer these questions, we use in vitro whole-cell recording and dynamic-clamp somatic current injection to study six distinct types of cortical neurons. We demonstrate that different types of neurons systematically vary in their receptiveness to fast changes in the structure of inhibition and the range over which changes in inhibitory tone affect their output. Using simple neuron models and model neuron hybrids (dynamic clamp), we determine which intrinsic differences between cell types lead to these variations in receptiveness. These results suggest important differences in the way cell types are affected by gamma-frequency inhibition, which may have important circuit level implications. Although intrinsic differences observed in vitro are useful for the elucidation of basic cellular properties and differences between cell types, we also investigate how the integrative properties of neurons are likely to be rapidly modulated in the context of active networks in vivo.

Gain modulation is an important feature of neural activity. Previous work has focused on the ability of background synaptic noise to modulate the slope (i.e., gain) of the frequency–current (f–I) relationship in neurons. To date, demonstrations of gain control that are independent of synaptic noise have been limited. We investigated the effects of increasing somatic conductance in the form of tonic inhibition on the initial and steady-state f–I relationship of CA1 pyramidal cells. We find that increasing membrane conductance reduces the gain of the steady-state f–I relationship through a graded increase in the magnitude of spike frequency adaptation. Increased adaptation arises through a conductance-induced depolarization of spike voltage threshold. Thus, by increasing the magnitude of spike frequency adaptation, added conductance can reduce the gain of the steady-state f–I relationship in the absence of random background membrane fluctuations.

Dopaminergic neurons of the ventral midbrain fire high-frequency bursts when animals are presented with unexpected rewards, or stimuli that predict reward. To identify the afferents that can initiate bursting and establish therapeutic strategies for diseases affected by altered bursting, a mechanistic understanding of bursting is essential. Our results show that bursting is initiated by a specific interaction between the voltage sensitivity of NMDA receptors and voltage-gated ion channels that results in the activation of an intrinsic, action potential-independent, high-frequency membrane potential oscillation. We further show that the NMDA receptor is uniquely suited for this because of the rapid kinetics and voltage dependence imparted to it by Mg2+ ion block and unblock. This mechanism explains the discrete nature of bursting in dopaminergic cells and demonstrates how synaptic signals may be reshaped by local intrinsic properties of a neuron before influencing action potential generation.

Interspecies differences can limit the translational value of excitable cells isolated from model organisms. It can be difficult to extrapolate from a drug- or mutation-induced phenotype in mice to human pathophysiology because mouse and human cardiac electrodynamics differ greatly. We present a hybrid computational-experimental technique, the cell-type transforming clamp, which is designed to overcome such differences by using a calculated compensatory current to convert the macroscopic electrical behavior of an isolated cell into that of a different cell type. We demonstrate the technique’s utility by evaluating drug arrhythmogenicity in murine cardiomyocytes that are transformed to behave like human myocytes. Whereas we use the cell-type transforming clamp in this work to convert between mouse and human electrodynamics, the technique could be adapted to convert between the action potential morphologies of any two cell types of interest.

Cardiac repolarization alternans is an arrhythmogenic rhythm disturbance, manifested in individual myocytes as a beat-to-beat alternation of action potential durations and intracellular calcium transient magnitudes. Recent experimental studies have reported “subcellular alternans,” in which distinct regions of an individual cell are seen to have counterphase calcium alternations, but the mechanism by which this occurs is not well understood. Although previous theoretical work has proposed a possible dynamical mechanism for subcellular alternans formation, no direct evidence for this mechanism has been reported in vitro. Rather, experimental studies have generally invoked fixed subcellular heterogeneities in calcium-cycling characteristics as the mechanism of subcellular alternans formation.In this study, we have generalized the previously proposed dynamical mechanism to predict a simple pacing algorithm by which subcellular alternans can be induced in isolated cardiac myocytes in the presence or absence of fixed subcellular heterogeneity. We aimed to verify this hypothesis using computational modeling and to confirm it experimentally in isolated cardiac myocytes. Furthermore, we hypothesized that this dynamical mechanism may account for previous reports of subcellular alternans seen in statically paced, intact tissue.Using a physiologically realistic computational model of a cardiac myocyte, we show that our predicted pacing algorithm induces subcellular alternans in a manner consistent with theoretical predictions. We then use a combination of real-time electrophysiology and fluorescent calcium imaging to implement this protocol experimentally and show that it robustly induces subcellular alternans in isolated guinea pig ventricular myocytes. Finally, we use computational modeling to demonstrate that subcellular alternans can indeed be dynamically induced during static pacing of 1D fibers of myocytes during tissue-level spatially discordant alternans.Here we provide the first direct experimental evidence that subcellular alternans can be dynamically induced in cardiac myocytes. This proposed mechanism may contribute to subcellular alternans formation in the intact heart.

The infinitesimal phase response curve (PRC) of a neural oscillator to a weak input is a powerful predictor of network dynamics; however, many networks have strong coupling and require direct measurement of the PRC for strong inputs under the assumption of pulsatile coupling. We incorporate measured noise levels in firing time maps constructed from PRCs to predict phase-locked modes of activity, phase difference, and locking strength in 78 heterogeneous hybrid networks of 2 neurons constructed using the dynamic clamp. We show that noise may either destroy or stabilize a phase-locked mode of activity.

Phase response curves (PRCs) for a single neuron are often used to predict the synchrony of mutually coupled neurons. Previous theoretical work on pulse-coupled oscillators used single-pulse perturbations. We propose an alternate method in which functional PRCs (fPRCs) are generated using a train of pulses applied at a fixed delay after each spike, with the PRC measured when the phasic relationship between the stimulus and the subsequent spike in the neuron has converged. The essential information is the dependence of the recovery time from pulse onset until the next spike as a function of the delay between the previous spike and the onset of the applied pulse. Experimental fPRCs in Aplysia pacemaker neurons were different from single-pulse PRCs, principally due to adaptation. In the biological neuron, convergence to the fully adapted recovery interval was slower at some phases than that at others because the change in the effective intrinsic period due to adaptation changes the effective phase resetting in a way that opposes and slows the effects of adaptation. The fPRCs for two isolated adapting model neurons were used to predict the existence and stability of 1: 1 phase-locked network activity when the two neurons were coupled. A stability criterion was derived by linearizing a coupled map based on the fPRC and the existence and stability criteria were successfully tested in two-simulated-neuron networks with reciprocal inhibition or excitation. The fPRC is the first PRC-based tool that can account for adaptation in analyzing networks of neural oscillators.

Recent work argues that similar network performance can result from highly variable sets of network parameters, raising the question of whether neuromodulation can be reliable across individuals with networks with different sets of synaptic strengths and intrinsic membrane conductances. To address this question, we used the dynamic clamp to construct 2-cell reciprocally inhibitory networks from gastric mill (GM) neurons of the crab stomatogastric ganglion. When the strength of the artificial inhibitory synapses (g(syn)) and the conductance of an artificial I(h) (g(h)) were varied with the dynamic clamp, a variety of network behaviors resulted, including regions of stable alternating bursting. Maps of network output as a function of g(syn) and g(h) were constructed in normal saline and again in the presence of serotonin or oxotremorine. Both serotonin and oxotremorine depolarize and excite isolated individual GM neurons, but by different cellular mechanisms. Serotonin and oxotremorine each increased the size of the parameter regions that supported alternating bursting, and, on average, increased burst frequency. Nonetheless, in both cases some parameter sets within the sample space deviated from the mean population response and decreased in frequency. These data provide insight into why pharmacological treatments that work in most individuals can generate anomalous actions in a few individuals, and they have implications for understanding the evolution of nervous systems.

Phase-locked activity is thought to underlie many high-level functions of the nervous system, the simplest of which are produced by central pattern generators (CPGs). It is not known whether we can define a theoretical framework that is sufficiently general to predict phase-locking in actual biological CPGs, nor is it known why the CPGs that have been characterized are dominated by inhibition. Previously, we applied a method based on phase response curves measured using inputs of biologically realistic amplitude and duration to predict the existence and stability of 1:1 phase-locked modes in hybrid networks of one biological and one model bursting neuron reciprocally connected with artificial inhibitory synapses. Here we extend this analysis to excitatory coupling. Using the pyloric dilator neuron from the stomatogastric ganglion of the American lobster as our biological cell, we experimentally prepared 86 networks using five biological neurons, four model neurons, and heterogeneous synapse strengths between 1 and 10,000 nS. In 77% of networks, our method was robust to biological noise and accurately predicted the phasic relationships. In 3%, our method was inaccurate. The remaining 20% were not amenable to analysis because our theoretical assumptions were violated. The high failure rate for excitation compared with inhibition was due to differential effects of noise and feedback on excitatory versus inhibitory coupling and suggests that CPGs dominated by excitatory synapses would require precise tuning to function, which may explain why CPGs rely primarily on inhibitory synapses.

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh–Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

The dynamic clamp is a widely used method for integrating mathematical models with electrophysiological experiments. This method involves measuring the membrane voltage of a cell, using it to solve computational models of ion channel dynamics in real-time, and injecting the calculated current(s) back into the cell. Limitations of this technique include those associated with single electrode current clamping and the sampling effects caused by the dynamic clamp. In this study, we show that the combination of these limitations causes transient instabilities under certain conditions. Through physical experiments and simulations, we show that dynamic clamp instability is directly related to the sampling delay and the maximum simulated conductance being injected. It is exaggerated by insufficient electrode series resistance and capacitance compensation. Increasing the sampling rate of the dynamic clamp system increases dynamic clamp stability; however, this improvement, is constrained by how well the electrode series resistance and capacitance are compensated. At present, dynamic clamp sampling rates are justified solely on the temporal dynamics of the models being simulated; here we show that faster rates increase the stable range of operation for the dynamic clamp system. In addition, we show that commonly accepted levels of resistance compensation nevertheless significantly compromise the stability of a dynamic clamp system.

Data from neurons in vivo have shown that spike output can often sustain episodes of high variability. Theoretical studies have indicated that the high conductance state of neurons brought on by synaptic activity can contribute to an increase in the variability of spike output by decreasing the integration timescale of the neuron. In the present work, we were interested in understanding how background synaptic conductance activity alters the interspike interval (ISI) variability of layer III pyramidal cells of the medial entorhinal cortex. We compared ISI variability in pyramidal cells as a result of synaptic current- or conductance-mediated membrane fluctuations. We found that the effects of background synaptic conductance activity on ISI variability depend on the neuron type. In pyramidal cells lacking spike frequency adaptation, the variability increased in relation to a comparable synaptic current stimulus. In contrast, in pyramidal cells displaying spike frequency adaptation, the synaptic conductance stimulus produced lower ISI variability. To understand this result, we constructed a phenomenological model that reproduced the basic properties of these neurons under control and increased leak conductance. We found that leak can change the properties of the neuron by acting as a bifurcation parameter that reduces the afterdepolarization (ADP) and decreases the slope (gain) of the frequency-current relationship, particularly for transient stimuli. A lower gain with the added leak causes a reduction in ISI variability. We conclude that the ability of a high conductance state to increase ISI variability cannot be generalized and can depend on the spike ADP dynamics expressed by the neuron.

Neural integrators and working memory rely on persistent activity, a widespread neural phenomenon potentially involving persistent sodium conductances. Using a unique combination of voltage-clamp, dynamic-clamp, and frequency-domain techniques, we have investigated the role of voltage-dependent conductances on the dendritic electrotonic structure of neurons of the prepositus hypoglossi nucleus (PHN), which is known to be involved in oculomotor integration. The PHN contains two main neuronal populations: type B neurons with a double afterhyperpolarization and type D neurons, which not only are oscillatory but also have a greater electrotonic length than that of type B neurons. The persistent sodium conductance is present in all PHN neurons, although its effect on the dynamic electrotonic structure is shown to significantly differ in the two major cell types present in the nucleus. The electrotonic differences are such that the persistent sodium conductance can be almost perfectly manipulated in a type B neuron using an on-line dynamic clamp to add or subtract virtual sodium ion channels. The dynamic-clamp results are confirmed by data-fitted models, which suggest that the persistent sodium conductance has two different roles depending on its somatic versus dendritic location: perisomatic conductances could play a major role in maintaining action potential discharge and dendritic conductances would be more involved in other computational properties, such as those involving remote synaptic processing or bistable events.

In the dynamic clamp technique, a typically nonlinear feedback system delivers electrical current to an excitable cell that represents the actions of “virtual” ion channels (e.g., channels that are gated by local membrane potential or by electrical activity in neighboring biological or virtual neurons). Since the conception of this technique, there have been a number of different implementations of dynamic clamp systems, each with differing levels of flexibility and performance. Embedded hardware-based systems typically offer feedback that is very fast and precisely timed, but these systems are often expensive and sometimes inflexible. PC-based systems, on the other hand, allow the user to write software that defines an arbitrarily complex feedback system, but real-time performance in PC-based systems can be deteriorated by imperfect real-time performance. Here, we systematically evaluate the performance requirements for artificial dynamic clamp knock-in of transient sodium and delayed rectifier potassium conductances. Specifically, we examine the effects of controller time step duration, differential equation integration method, jitter (variability in time step), and latency (the time lag from reading inputs to updating outputs). Each of these control system flaws is artificially introduced in both simulated and real dynamic clamp experiments. We demonstrate that each of these errors affect dynamic clamp accuracy in a way that depends on the time constants and stiffness of the differential equations being solved. In simulations, time steps above 0.2 ms lead to catastrophic alteration of spike shape, but the frequency–current relationship is much more robust. Latency (the part of the time step that occurs between measuring membrane potential and injecting re-calculated membrane current) is a crucial factor as well. Experimental data are substantially more sensitive to inaccuracies than simulated data.

Previous work has established that stellate cells of the medial entorhinal cortex produce prominent intrinsic subthreshold oscillations in the voltage response concentrated within the theta range (3–7 Hz). It has been speculated that these oscillations play an important role in vivo in establishing network behavior both in the entorhinal cortex and hippocampus. Consequently, it is important to investigate under what conditions theta oscillations in stellate cells can be generated and whether the spike-train power spectral density (PSD) also carries power at theta. We investigated the ability of stellate cells to generate theta oscillations in the presence of generic in vivo-like patterns of stimulation. Inputs were Poisson process-driven excitatory and inhibitory synaptic conductances or currents, introduced via dynamic clamp. We analyzed the subthreshold membrane oscillations and spike-train behavior in the presence of comparable synaptic conductance- or current-mediated membrane fluctuations. In the presence of conductance-based synapses, subthreshold oscillations are highly attenuated or entirely eliminated. Conversely, with current-based synapses stellate cells retain their ability to generate subthreshold oscillations in the theta band. These results also extend into the spiking regime, where only under current-based synapses does the PSD of the spike train show a prominent peak at theta. Furthermore, the peak in the spike-train PSD and spike clustering results from an increased probability of firing after a spike afterhyperpolarization and not directly from subthreshold oscillatory dynamics as has been previously suggested. Our results suggest that subthreshold oscillations may contribute less to in vivo response properties than has been hypothesized.

Why do motoneurons possess two persistent inward currents (PICs), a fast sodium current and a slow calcium current? To answer this question, we replaced the natural PICs with dynamic clamp-imposed artificial PICs at the soma of spinal motoneurons of anesthetized cats. We investigated how PICs with different kinetics (1–100 ms) amplify proprioceptive inputs. We showed that their action depends on the presence or absence of a resonance created by the Ih current. In resonant motoneurons, a fast PIC enhances the resonance and amplifies the dynamic component of Ia inputs elicited by ramp-and-hold muscle stretches. This facilitates the recruitment of these motoneurons, which likely innervate fast contracting motor units developing large forces, e.g., to restore balance or produce ballistic movements. In nonresonant motoneurons, in contrast, a fast PIC easily triggers plateau potentials, which leads to a dramatic amplification of the static component of Ia inputs. This likely facilitates the recruitment of these motoneurons, innervating mostly slowly contracting and fatigue-resistant motor units, during postural activities. Finally, a slow PIC may switch a resonant motoneuron to nonresonant by counterbalancing Ih, thus changing the action of the fast PIC. A modeling study shows that Ih needs to be located on the dendrites to create the resonance, and it predicts that dendritic PICs amplify synaptic input in the same manner as somatic PICs.

Real-time, closed-loop intervention is an emerging experiment-control method that promises to provide invaluable new insight into cardiac electrophysiology. One example is the investigation of closed-loop feedback control of cardiac activity (e.g., alternans) as a possible method of preventing arrhythmia onset. To date, such methods have been investigated only in vitro using microelectrode systems, which are hindered by poor spatial resolution and are not well suited for atrial or ventricular tissue preparations. We have developed a system that uses optical mapping techniques and an electrical stimulator as the sensory and effector arms, respectively, of a closed-loop, real-time control system. The system consists of a 2,048 x 1 pixel line-scan charge-coupled device camera that records optical signals from the tissue. Custom-image processing and control software, which is implemented on top of a hard real-time operation system (RTAI Linux), process the data and make control decisions with a deterministic delay of <1 ms. The system is tested in two ways: 1) it is used to control, in real time, simulated optical signals of electrical alternans; and 2) it uses precisely timed, feedback-controlled initiation of antitachycardia pacing to terminate reentrant arrhythmias in an arterially perfused swine right ventricle stained with voltage-sensitive fluorescent dye 4{beta-[2-(di-n-butylamino)-6-napathy]vinyl}pyridinium (di-4-ANEPPS). Thus real-time control of cardiac activity using optical mapping techniques is feasible. Such a system is attractive because it offers greater measurement resolution than the electrode-based systems with which real-time control has been used previously.

The responsiveness of cortical neurons is strongly and rapidly influenced by changes in the level of local network activity. In rodent somatosensory cortex, increases in network activity increase neuronal responsiveness to the intracellular injection of brief conductance stimuli but paradoxically decrease responsiveness to brief whisker deflections. However, whisker stimulation frequently evokes long-lasting changes in the level of local circuit activity. The ability of stimuli to successfully evoke prolonged increases in circuit activity is associated with both an increase in the amount of conductance evoked by a whisker stimulus and an increase in action potential responsiveness to whisker stimulation. In addition, brief whisker stimuli presented during periods of high network activity evoke postsynaptic potentials containing a greater proportion of inhibition, consistent with an increased efficiency in the activation of inhibitory mechanisms during the Up state. In contrast, during prolonged and variable whisker stimulation, increased network activity is associated with an increase in overall responsiveness, dynamic range, output gain, and correlation between action potential response and speed of whisker movement. We conclude that stimulus-evoked or spontaneous alterations in cortical state can influence neuronal responsiveness in a complex manner, resulting in large changes in which, and how, sensory stimuli are represented.

The bushy cells of the anterior ventral cochlear nucleus (AVCN) preserve or improve the temporal coding of sound information arriving from auditory nerve fibers (ANF). The critical cellular mechanisms entailed in this process include the specialized nerve terminals, the endbulbs of Held, and the membrane conductance configuration of the bushy cell. In one strain of mice (DBA/2J), an early-onset hearing loss can cause a reduction in neurotransmitter release probability, and a smaller and slower spontaneous miniature excitatory postsynaptic current (EPSC) at the endbulb synapse. In the present study, by using a brain slice preparation, we tested the hypothesis that these changes in synaptic transmission would degrade the transmission of timing information from the ANF to the AVCN bushy neuron. We show that the electrical excitability of bushy cells in hearing-impaired old DBA mice was different from that in young, normal-hearing DBA mice. We found an increase in the action potential (AP) firing threshold with current injection; a larger AP afterhyperpolarization; and an increase in the number of spikes produced by large depolarizing currents. We also tested the temporal precision of bushy cell responses to high-frequency stimulation of the ANF. The standard deviation of spikes (spike jitter) produced by ANF-evoked excitatory postsynaptic potentials (EPSPs) was largely unaffected in old DBA mice. However, spike entrainment during a 100-Hz volley of EPSPs was significantly reduced. This was not a limitation of the ability of bushy cells to fire APs at this stimulus frequency, because entrainment to trains of current pulses was unaffected. Moreover, the decrease in entrainment is not attributable to increased synaptic depression. Surprisingly, the spike latency was 0.46 ms shorter in old DBA mice, and was apparently attributable to a faster conduction velocity, since the evoked excitatory postsynaptic current (EPSC) latency was shorter in old DBA mice as well. We also tested the contribution of the low-voltage-activated K+ conductance (g (KLV)) on the spike latency by using dynamic clamp. Alteration in g (KLV) had little effect on the spike latency. To test whether these changes in DBA mice were simply a result of continued postnatal maturation, we repeated the experiments in CBA mice, a strain that shows normal hearing thresholds through this age range. CBA mice exhibited no reduction in entrainment or increased spike jitter with age. We conclude that the ability of AVCN bushy neurons to reliably follow ANF EPSPs is compromised in a frequency-dependent fashion in hearing-impaired mice. This effect can be best explained by an increase in spike threshold.

Cells that produce intrinsic theta oscillations often contain the hyperpolarization-activated current I(h). In this article, we use models and dynamic clamp experiments to investigate the synchronization properties of two such cells (stellate cells of the entorhinal cortex and O-LM cells of the hippocampus) in networks with fast-spiking (FS) interneurons. The model we use for stellate cells and O-LM cells is the same, but the stellate cells are excitatory and the O-LM cells are inhibitory, with inhibitory postsynaptic potential considerably longer than those from FS interneurons. We use spike time response curve methods (STRC), expanding that technique to three-cell networks and giving two different ways in which the analysis of the three-cell network reduces to that of a two-cell network. We show that adding FS cells to a network of stellate cells can desynchronize the stellate cells, while adding them to a network of O-LM cells can synchronize the O-LM cells. These synchronization and desynchronization properties critically depend on I(h). The analysis of the deterministic system allows us to understand some effects of noise on the phase relationships in the stellate networks. The dynamic clamp experiments use biophysical stellate cells and in silico FS cells, with connections that mimic excitation or inhibition, the latter with decay times associated with FS cells or O-LM cells. The results obtained in the dynamic clamp experiments are in a good agreement with the analytical framework.

Dynamic clamp is a collection of closely related techniques that have been employed in cardiac electrophysiology to provide direct answers to numerous research questions regarding basic cellular mechanisms of action potential formation, action potential transfer and action potential synchronization in health and disease. Building on traditional current clamp, dynamic clamp was initially used to create virtual gap junctions between isolated myocytes. More recent applications include the embedding of a real pacemaking myocyte in a simulated network of atrial or ventricular cells and the insertion of virtual ion channels, either simulated in real time or simultaneously recorded from an expression system, into the membrane of an isolated myocyte. These applications have proven that dynamic clamp, which is characterized by the real-time evaluation and injection of simulated membrane current, is a powerful tool in cardiac electrophysiology. Here, each of the three different experimental configurations used in cardiac electrophysiology is reviewed. Also, directions are given for the implementation of dynamic clamp in the cardiac electrophysiology laboratory. With the growing interest in the application of dynamic clamp in cardiac electrophysiology, it is anticipated that dynamic clamp will also prove to be a powerful tool in basic research on biological pacemakers and in identification of specific ion channels as targets for drug development.

Synaptic waveforms, constructed from excitatory and inhibitory presynaptic Poisson trains, are presented to living and computational neurons. We review how the average output of a neuron e.g., the firing rate is set by the difference between excitatory and inhibitory event rates while neuronal variability is set by their sum. We distinguish neuronal variability from reproducibility. Variability quantifies how much an output measure is expected to vary; for example, the interspike interval coefficient of variation quantifies the typical range of interspike intervals. Reproducibility quantifies the similarity of neuronal outputs in response to repeated presentations of identical stimuli. Although variability and reproducibility are conceptually distinct, we show that, for ideal current source synapses, reproducibility is defined entirely by variability. For physiologically realistic conductance-based synapses, however, reproducibility is distinct from variability and average output, set by the Poisson rate and the degree of synchrony within the synaptic waveform.

The mechanism underlying spike timing-dependent neuromodulation (STDN) was investigated in the opisthobranch mollusc Tritonia diomedea. The serotonergic dorsal swim interneurons (DSIs) dynamically modulated the synaptic output of ventral swim interneuron B (VSI); immediately after DSI stimulation, there was a potentiation of VSI synaptic strength followed by a longer-lasting synaptic depression. The potentiation phase of STDN was unaffected by spike broadening produced by the potassium channel blocker 4-aminopyridine (4-AP). In contrast, the depression phase was eliminated by 4-AP. Bath-applied serotonin (5-HT) decreased VSI spike duration and increased the magnitude of the A-current (I-A), a voltage-dependent, transient, outward current. 4-AP preferentially blocked I-A and prevented the spike narrowing caused by 5-HT, uncovering the full extent of 5-HT-induced synaptic potentiation. A consistent correlation was observed between IA and spike duration, but the correlation between synaptic strength and spike duration differed between preparations. Conductance-based simulations showed that the magnitude of A-current conductance could affect spike duration and gave an estimation of the change needed to produce spike narrowing. An artificial IA introduced into the VSI in the presence of 4-AP by means of the dynamic-clamp technique restored spike duration and gave a further approximation of the magnitude of modulation needed for spike narrowing. Together, these results suggest a mechanism for STDN: the DSIs release 5-HT, which causes a spike duration-independent enhancement of synaptic strength and a longer-lasting enhancement of I-A that narrows the VSI spike and hence decreases VSI synaptic strength. Thus, STDN arises from the dynamics of independent intracellular signaling events.

Networks of oscillators produce vital activity in diverse natural systems. The dynamics of these networks are frequently studied via computational models that assume weak coupling, yet this assumption has not been experimentally validated. We applied weak stimuli to neuronal oscillators in Aplysia californica and deconvolved infinitesimal phase response curves (IPRCs) that describe the phase response of a neuron. We show that these IPRCs reliably predict the phase response for weak stimuli, independent of the stimulus waveform used. These weak stimuli are in the range of normal synaptic activity for these neurons, suggesting that weak coupling is a likely mechanism.

Previous experimental and computational work (for review, see White et al., 2000Go) has suggested that channel noise, generated by the stochastic flicker of voltage-gated ion channels, can be a major contributor to electrical membrane noise in neurons. In spiny stellate neurons of the entorhinal cortex, we remove the primary source of channel noise by pharmacologically blocking the native persistent Na+ conductance. Via the dynamic-clamp technique (Robinson and Kawai, 1993Go; Sharp et al., 1993Go), we then introduce virtual persistent Na+ channels into the membranes of the stellate neurons. By altering the mathematical properties of these virtual “knock-ins,” we demonstrate that stochastic flicker of persistent Na+ channels is necessary for the existence of slow perithreshold oscillations that characterize stellate neurons. Channel noise also alters the ability of stellate neurons to phase lock to weak sinusoidal stimuli. These results provide the first direct demonstration that physiological levels of channel noise can produce qualitative changes in the integrative properties of neurons.

GABAergic interneurons in many areas of the neocortex are mutually connected via chemical and electrical synapses. Previous computational studies have explored how these coupling parameters influence the firing patterns of interneuronal networks. These models have predicted that the stable states of such interneuronal networks will be either synchrony (near zero phase lag) or antisynchrony (phase lag near one-half of the interspike interval), depending on network connectivity and firing rates. In certain parameter regimens, the network can be bistable, settling into either stable state depending on the initial conditions. Here, we investigated how connectivity parameters influence spike patterns in paired recordings from layer I interneurons in brain slices from juvenile mice. Observed properties of chemical and electrical synapses were used to simulate connections between uncoupled cells via dynamic clamp. In uncoupled pairs, action potentials induced by constant depolarizing currents had randomly distributed phase differences between the two cells. When coupled with simulated chemical (inhibitory) synapses, however, these pairs exhibited a bimodal firing pattern, tending to fire either in synchrony or in antisynchrony. Combining electrical with chemical synapses, prolonging tau(Decay) of inhibitory connections, or increasing the firing rate of the network all resulted in enhanced stability of the synchronous state. Thus, electrical and inhibitory synaptic coupling constrain the relative timing of spikes in a two-cell network to, at most, two stable states, the stability and precision of which depend on the exact parameters of coupling.

Understanding the mechanistic bases of neuronal synchronization is a current challenge in quantitative neuroscience. We studied this problem in two putative cellular pacemakers of the mammalian hippocampal theta rhythm: glutamatergic stellate cells (SCs) of the medial entorhinal cortex and GABAergic oriens-lacunosum-moleculare (O-LM) interneurons of hippocampal region CA1. We used two experimental methods. First, we measured changes in spike timing induced by artificial synaptic inputs applied to individual neurons. We then measured responses of free-running hybrid neuronal networks, consisting of biological neurons coupled (via dynamic clamp) to biological or virtual counterparts. Results from the single-cell experiments predicted network behaviors well and are compatible with previous model-based predictions of how specific membrane mechanisms give rise to empirically measured synchronization behavior. Both cell types phase lock stably when connected via homogeneous excitatory-excitatory (E-E) or inhibitory-inhibitory (I-I) connections. Phase-locked firing is consistently synchronous for either cell type with E-E connections and nearly anti-synchronous with I-I connections. With heterogeneous connections (e.g., excitatory-inhibitory, as might be expected if members of a given population had heterogeneous connections involving intermediate interneurons), networks often settled into phase locking that was either stable or unstable, depending on the order of firing of the two cells in the hybrid network. Our results imply that excitatory SCs, but not inhibitory O-LM interneurons, are capable of synchronizing in phase via monosynaptic mutual connections of the biologically appropriate polarity. Results are largely independent of synaptic strength and synaptic kinetics, implying that our conclusions are robust and largely unaffected by synaptic plasticity.

The dynamic clamp uses computer simulation to introduce artificial membrane or synaptic conductances into biological neurons and to create hybrid circuits of real and model neurons. In the ten years since it was first developed, the dynamic clamp has become a widely used tool for the study of neural systems at the cellular and circuit levels. This review describes recent state-of-the-art implementations of the dynamic clamp and summarizes insights gained through its use, ranging from the role of voltage-dependent conductances in shaping neuronal activity to the effects of synaptic dynamics on network behavior and the impact of in vivo-like input on neuronal information processing.

We present a real-time simulation system that enables modeled dynamical systems to interact with physical experimental systems, and is specifically aimed towards execution of the dynamic clamp protocol. Model reference current injection (MRCI) operates under Real-Time Linux (RT-Linux or RTL) and provides a simple equation-oriented language for describing dynamical system models. Features include scripting of commands to implement repeatable protocols, the ability to output pre-computed waveforms through any variable or parameter of the model, the means to conduct time measurements and assess the computational performance of the real-time system, and an installation program that installs the software and accompanying device drivers with minimal input from the user. Tested models operate as fast as 30 kHz, with actual maximum rates dependent on model complexity. We present sample models that exhibit the main features of the modeling language. Experiments demonstrate the abilities of the system by creating a hybrid network of real and simulated neurons, and playing a pre-defined synaptic waveform into a synaptic conductance variable. We conclude by introducing a waveform reconstruction technique that is useful for establishing the presence of significant experimental error in implementations of the dynamic clamp protocol.

We present a novel approach to implementing the dynamic-clamp protocol (Sharp et al., 1993), commonly used in neurophysiology and cardiac electrophysiology experiments. Our approach is based on real-time extensions to the Linux operating system. Conventional PC-based approaches have typically utilized single-cycle computational rates of 10 kHz or slower. In thispaper, we demonstrate reliable cycle-to-cycle rates as fast as 50 kHz. Our system, which we call model reference current injection (MRCI); pronounced mercí is also capable of episodic logging of internal state variables and interactive manipulation of model parameters. The limiting factor in achieving high speeds was not processor speed or model complexity, but cycle jitter inherent in the CPU/motherboard performance. We demonstrate these high speeds and flexibility with two examples: 1) adding action-potential ionic currents to a mammalian neuron under whole-cell patch-clamp and 2) altering a cell’s intrinsic dynamics via MRCI while simultaneously coupling it via artificial synapses to an internal computational model cell. These higher rates greatly extend the applicability of this technique to the study of fast electrophysiological currents such fast Na+ currents and fast excitatory/inhibitory synapses.

We describe a system for real-time control of biological and other experiments. This device, based around the Real-Time Linux operating system, was tested specifically in the context of dynamic clamping, a demanding real-time task in which a computational system mimics the effects of nonlinear membrane conductances in living cells. The system is fast enough to represent dozens of nonlinear conductances in real time at clock rates well above 10 kHz. Conductances can be represented in deterministic form, or more accurately as discrete collections of stochastically gating ion channels. Tests were performed using a variety of complex models of nonlinear membrane mechanisms in excitable cells, including simulations of spatially extended excitable structures, and multiple interacting cells. Only in extreme cases does the computational load interfere with high-speed “hard” real-time processing (i.e., real-time processing that never falters). Freely available on the worldwide web, this experimental control system combines good performance. immense flexibility, low cost, and reasonable ease of use. It is easily adapted to any task involving real-time control, and excels in particular for applications requiring complex control algorithms that must operate at speeds over 1 kHz.

Many biomedical experiments require a precisely timed real-time (RT) computer interface. Because commonly used desktop operating systems are inherently non-real-time, real-time laboratory computer systems are often based on outdated DOS software or expensive proprietary real-time operating systems. Here we discuss a real-time computing system, based on the free RT-Linux operating system, which we have developed for adaptive pacing control in a clinical cardiac electrophysiology laboratory. This powerful, flexible, and inexpensive system demonstrates that RT-Linux is well suited for real-time biomedical experiment interface.