The dynamic clamp comes of age

Computational modeling to study the impact of changes in Nav1.8 sodium channel on neuropathic pain

Objective

Nav1.8 expression is restricted to sensory neurons; it was hypothesized that aberrant expression and function of this channel at the site of injury contributed to pathological pain. However, the specific contributions of Nav1.8 to neuropathic pain are not as clear as its role in inflammatory pain. The aim of this study is to understand how Nav1.8 present in peripheral sensory neurons regulate neuronal excitability and induce various electrophysiological features on neuropathic pain.

Methods

To study the effect of changes in sodium channel Nav1.8 kinetics, Hodgkin-Huxley type conductance-based models of spiking neurons were constructed using the NEURON v8.2 simulation software. We constructed a single-compartment model of neuronal soma that contained Nav1.8 channels with the ionic mechanisms adapted from some existing small DRG neuron models. We then validated and compared the model with our experimental data from in vivo recordings on soma of small dorsal root ganglion (DRG) sensory neurons in animal models of neuropathic pain (NEP).

Results

We show that Nav1.8 is an important parameter for the generation and maintenance of abnormal neuronal electrogenesis and hyperexcitability. The typical increased excitability seen is dominated by a left shift in the steady state of activation of this channel and is further modulated by this channel's maximum conductance and steady state of inactivation. Therefore, modified action potential shape, decreased threshold, and increased repetitive firing of sensory neurons in our neuropathic animal models may be orchestrated by these modulations on Nav1.8.

Conclusion

Computational modeling is a novel strategy to understand the generation of chronic pain. In this study, we highlight that changes to the channel functions of Nav1.8 within the small DRG neuron may contribute to neuropathic pain.

Single-compartment model of a pyramidal neuron, fitted to recordings with current and conductance injection

For single neuron models, reproducing characteristics of neuronal activity such as the firing rate, amplitude of spikes, and threshold potentials as functions of both synaptic current and conductance is a challenging task. In the present work, we measure these characteristics of regular spiking cortical neurons using the dynamic patch-clamp technique, compare the data with predictions from the standard Hodgkin-Huxley and Izhikevich models, and propose a relatively simple five-dimensional dynamical system model, based on threshold criteria. The model contains a single sodium channel with slow inactivation, fast activation and moderate deactivation, as well as, two fast repolarizing and slow shunting potassium channels. The model quantitatively reproduces characteristics of steady-state activity that are typical for a cortical pyramidal neuron, namely firing rate not exceeding 30 Hz; critical values of the stimulating current and conductance which induce the depolarization block not exceeding 80 mV and 3, respectively (both values are scaled by the resting input conductance); extremum of hyperpolarization close to the midpoint between spikes. The analysis of the model reveals that the spiking regime appears through a saddle-node-on-invariant-circle bifurcation, and the depolarization block is reached through a saddle-node bifurcation of cycles. The model can be used for realistic network simulations, and it can also be implemented within the so-called mean-field, refractory density framework.

Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus

Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages in mouse hippocampal slices to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs' encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time-dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.

Bursting Dynamics Based on the Persistent Na+ and Na+/K+ Pump Currents: A Dynamic Clamp Approach

Life-supporting rhythmic motor functions like heart-beating in invertebrates and breathing in vertebrates require an indefatigable generation of a robust rhythm by specialized oscillatory circuits, central pattern generators (CPGs). These CPGs should be sufficiently flexible to adjust to environmental changes and behavioral goals. Continuous self-sustained operation of bursting neurons requires intracellular Na+ concentration to remain in a functional range and to have checks and balances of the Na+ fluxes met on a cycle-to-cycle basis during bursting. We hypothesize that at a high excitability state, the interaction of the Na+/K+ pump current, Ipump, and persistent Na+ current, INaP, produces a mechanism supporting functional bursting. INaP is a low voltage-activated inward current that initiates and supports the bursting phase. This current does not inactivate and is a significant source of Na+ influx. Ipump is an outward current activated by [Na+]i and is the major source of Na+ efflux. Both currents are active and counteract each other between and during bursts. We apply a combination of electrophysiology, computational modeling, and dynamic clamp to investigate the role of Ipump and INaP in the leech heartbeat CPG interneurons (HN neurons). Applying dynamic clamp to introduce additional Ipump and INaP into the dynamics of living synaptically isolated HN neurons in real time, we show that their joint increase produces transition into a new bursting regime characterized by higher spike frequency and larger amplitude of the membrane potential oscillations. Further increase of Ipump speeds up this rhythm by shortening burst duration (BD) and interburst interval (IBI).

Effects of NALCN-Encoded Na+ Leak Currents on the Repetitive Firing Properties of SCN Neurons Depend on K+-Driven Rhythmic Changes in Input Resistance

Neurons in the suprachiasmatic nucleus (SCN) generate circadian changes in the rates of spontaneous action potential firing that regulate and synchronize daily rhythms in physiology and behavior. Considerable evidence suggests that daily rhythms in the repetitive firing rates (higher during the day than at night) of SCN neurons are mediated by changes in subthreshold potassium (K+) conductance(s). An alternative "bicycle" model for circadian regulation of membrane excitability in clock neurons, however, suggests that an increase in NALCN-encoded sodium (Na+) leak conductance underlies daytime increases in firing rates. The experiments reported here explored the role of Na+ leak currents in regulating daytime and nighttime repetitive firing rates in identified adult male and female mouse SCN neurons: vasoactive intestinal peptide-expressing (VIP+), neuromedin S-expressing (NMS+) and gastrin-releasing peptide-expressing (GRP+) cells. Whole-cell recordings from VIP+, NMS+, and GRP+ neurons in acute SCN slices revealed that Na+ leak current amplitudes/densities are similar during the day and at night, but have a larger impact on membrane potentials in daytime neurons. Additional experiments, using an in vivo conditional knockout approach, demonstrated that NALCN-encoded Na+ currents selectively regulate daytime repetitive firing rates of adult SCN neurons. Dynamic clamp-mediated manipulation revealed that the effects of NALCN-encoded Na+ currents on the repetitive firing rates of SCN neurons depend on K+ current-driven changes in input resistances. Together, these findings demonstrate that NALCN-encoded Na+ leak channels contribute to regulating daily rhythms in the excitability of SCN neurons by a mechanism that depends on K+ current-mediated rhythmic changes in intrinsic membrane properties.SIGNIFICANCE STATEMENT Elucidating the ionic mechanisms responsible for generating daily rhythms in the rates of spontaneous action potential firing of neurons in the suprachiasmatic nucleus (SCN), the master circadian pacemaker in mammals, is an important step toward understanding how the molecular clock controls circadian rhythms in physiology and behavior. While numerous studies have focused on identifying subthreshold K+ channel(s) that mediate day-night changes in the firing rates of SCN neurons, a role for Na+ leak currents has also been suggested. The results of the experiments presented here demonstrate that NALCN-encoded Na+ leak currents differentially modulate daily rhythms in the daytime/nighttime repetitive firing rates of SCN neurons as a consequence of rhythmic changes in subthreshold K+ currents.

Low-dimensional models of single neurons: a review

The classical Hodgkin-Huxley (HH) point-neuron model of action potential generation is four-dimensional. It consists of four ordinary differential equations describing the dynamics of the membrane potential and three gating variables associated to a transient sodium and a delayed-rectifier potassium ionic currents. Conductance-based models of HH type are higher-dimensional extensions of the classical HH model. They include a number of supplementary state variables associated with other ionic current types, and are able to describe additional phenomena such as subthreshold oscillations, mixed-mode oscillations (subthreshold oscillations interspersed with spikes), clustering and bursting. In this manuscript we discuss biophysically plausible and phenomenological reduced models that preserve the biophysical and/or dynamic description of models of HH type and the ability to produce complex phenomena, but the number of effective dimensions (state variables) is lower. We describe several representative models. We also describe systematic and heuristic methods of deriving reduced models from models of HH type.

Computational and experimental modulation of a noisy chaotic neuronal system

In this work, we study the interplay between chaos and noise in neuronal state transitions involving period doubling cascades. Our approach involves the implementation of a neuronal mathematical model under the action of neuromodulatory input, with and without noise, as well as equivalent experimental work on a biological neuron in the stomatogastric ganglion of the crab Cancer borealis. Our simulations show typical transitions between tonic and bursting regimes that are mediated by chaos and period doubling cascades. While this transition is less evident when intrinsic noise is present in the model, the noisy computational output displays features akin to our experimental results. The differences and similarities observed in the computational and experimental approaches are discussed.

Recent advances for using human induced-pluripotent stem cells as pain-in-a-dish models of neuropathic pain

Neuropathic pain is amongst the most common non-communicable disorders and the poor effectiveness of current treatment is an unmet need. Although pain is a universal experience, there are significant inter-individual phenotypic differences. Developing models that can accurately recapitulate the clinical pain features is crucial to better understand underlying pathophysiological mechanisms and find innovative treatments. Current data from heterologous expression systems that investigate properties of specific molecules involved in pain signaling, and from animal models, show limited success with their translation into the development of novel treatments for pain. This is in part because they do not recapitulate the native environment in which a particular molecule functions, and due to species-specific differences in the properties of several key molecules that are involved in pain signaling. The limited availability of post-mortem tissue, in particular dorsal root ganglia (DRG), has hampered research using human cells in pre-clinical studies. Human induced-pluripotent stem cells (iPSCs) have emerged as an exciting alternative platform to study patient-specific diseases. Sensory neurons that are derived from iPSCs (iPSC-SNs) have provided new avenues towards elucidating peripheral pathophysiological mechanisms, the potential for development of personalized treatments, and as a cell-based system for high-throughput screening for discovering novel analgesics. Nevertheless, reprogramming and differentiation protocols to obtain nociceptors have mostly yielded immature homogenous cell populations that do not recapitulate the heterogeneity of native sensory neurons. To close the gap between native human tissue and iPSCs, alternative strategies have been developed. We will review here recent developments in differentiating iPSC-SNs and their use in pre-clinical translational studies. Direct conversion of stem cells into the cells of interest has provided a more cost- and time-saving method to improve reproducibility and diversity of sensory cell types. Furthermore, multi-cellular strategies that mimic in vivo microenvironments for cell maturation, by improving cell contact and communication (co-cultures), reproducing the organ complexity and architecture (three-dimensional organoid), and providing iPSCs with the full spatiotemporal context and nutrients needed for acquiring a mature phenotype (xenotransplantation), have led to functional sensory neuron-like systems. Finally, this review touches on novel prospective strategies, including fluorescent-tracking to select the differentiated neurons of relevance, and dynamic clamp, an electrophysiological method that allows direct manipulation of ionic conductances that are missing in iPSC-SNs.

Discovering sparse control strategies in neural activity

Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology. Though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could eventually help characterize collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations—ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms—to neural states and their impact on collective neural activity. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, “pivotal” neurons that account for most of the system’s sensitivity, suggesting a sparse mechanism of collective control.

A dynamic clamp protocol to artificially modify cell capacitance

Dynamics of excitable cells and networks depend on the membrane time constant, set by membrane resistance and capacitance. Whereas pharmacological and genetic manipulations of ionic conductances of excitable membranes are routine in electrophysiology, experimental control over capacitance remains a challenge. Here, we present capacitance clamp, an approach that allows electrophysiologists to mimic a modified capacitance in biological neurons via an unconventional application of the dynamic clamp technique. We first demonstrate the feasibility to quantitatively modulate capacitance in a mathematical neuron model and then confirm the functionality of capacitance clamp in in vitro experiments in granule cells of rodent dentate gyrus with up to threefold virtual capacitance changes. Clamping of capacitance thus constitutes a novel technique to probe and decipher mechanisms of neuronal signaling in ways that were so far inaccessible to experimental electrophysiology.

A design principle of spindle oscillations in mammalian sleep

Neural oscillations are mainly regulated by molecular mechanisms and network connectivity of neurons. Large-scale simulations of neuronal networks have driven the population-level understanding of neural oscillations. However, cell-intrinsic mechanisms, especially a design principle, of neural oscillations remain largely elusive. Herein, we developed a minimal, Hodgkin-Huxley-type model of groups of neurons to investigate molecular mechanisms underlying spindle oscillation, which is synchronized oscillatory activity predominantly observed during mammalian sleep. We discovered that slowly inactivating potassium channels played an essential role in characterizing the firing pattern. The detailed analysis of the minimal model revealed that leak sodium and potassium channels, which controlled passive properties of the fast variable (i.e., membrane potential), competitively regulated the base value and time constant of the slow variable (i.e., cytosolic calcium concentration). Consequently, we propose a theoretical design principle of spindle oscillations that may explain intracellular mechanisms behind the flexible control over oscillation density and calcium setpoint.

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A minimal, Hodgkin-Huxley-type model of spindle oscillations is developed

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The property of delayed rectifier K⁺ channels characterizes spindle oscillations

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The combination of bifurcations specifies spindle oscillations

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Spindle oscillations are controlled by the balance of inward and outward currents

Synchronous inhibitory pathways create both efficiency and diversity in the retina

Complex connections in neural circuits make it difficult to quantitatively assign even the most basic neural computations to the actions of specific neurons. Retinal ganglion cells are most sensitive to changes in intensity across space and over time. This property, caused by a region known as the receptive field surround, improves information transmission about natural scenes. We dynamically manipulated individual interneurons to directly measure their effect on retinal receptive fields, finding that two inhibitory neuron types, horizontal cells and amacrine cells, synchronously create the same contribution to the receptive field surround at different spatial scales. By analyzing large populations of ganglion cells, we show that this arrangement increases diversity in retinal signaling while preserving maximal information transmission about natural scenes.

Sensory receptive fields combine features that originate in different neural pathways. Retinal ganglion cell receptive fields compute intensity changes across space and time using a peripheral region known as the surround, a property that improves information transmission about natural scenes. The visual features that construct this fundamental property have not been quantitatively assigned to specific interneurons. Here, we describe a generalizable approach using simultaneous intracellular and multielectrode recording to directly measure and manipulate the sensory feature conveyed by a neural pathway to a downstream neuron. By directly controlling the gain of individual interneurons in the circuit, we show that rather than transmitting different temporal features, inhibitory horizontal cells and linear amacrine cells synchronously create the linear surround at different spatial scales and that these two components fully account for the surround. By analyzing a large population of ganglion cells, we observe substantial diversity in the relative contribution of amacrine and horizontal cell visual features while still allowing individual cells to increase information transmission under the statistics of natural scenes. Established theories of efficient coding have shown that optimal information transmission under natural scenes allows a diverse set of receptive fields. Our results give a mechanism for this theory, showing how distinct neural pathways synthesize a sensory computation and how this architecture both generates computational diversity and achieves the objective of high information transmission.

Contributions of NaV1.8 and NaV1.9 to excitability in human induced pluripotent stem-cell derived somatosensory neurons

The inhibition of voltage-gated sodium (Na_V) channels in somatosensory neurons presents a promising novel modality for the treatment of pain. However, the precise contribution of these channels to neuronal excitability, the cellular correlate of pain, is unknown; previous studies using genetic knockout models or pharmacologic block of Na_V channels have identified general roles for distinct sodium channel isoforms, but have never quantified their exact contributions to these processes. To address this deficit, we have utilized dynamic clamp electrophysiology to precisely tune in varying levels of Na_V1.8 and Na_V1.9 currents into induced pluripotent stem cell-derived sensory neurons (iPSC-SNs), allowing us to quantify how graded changes in these currents affect different parameters of neuronal excitability and electrogenesis. We quantify and report direct relationships between Na_V1.8 current density and action potential half-width, overshoot, and repetitive firing. We additionally quantify the effect varying Na_V1.9 current densities have on neuronal membrane potential and rheobase. Furthermore, we examined the simultaneous interplay between Na_V1.8 and Na_V1.9 on neuronal excitability. Finally, we show that minor biophysical changes in the gating of Na_V1.8 can render human iPSC-SNs hyperexcitable, in a first-of-its-kind investigation of a gain-of-function Na_V1.8 mutation in a human neuronal background.

Neuronal networks: Degeneracy unleashed

Degeneracy in neuronal networks is emerging as a parallel to accepted degeneracy in neuronal and synaptic properties. A new study leverages detailed understanding of neuronal networks and their modulation in the crustacean stomatogastric nervous system to show that network degeneracy is conditional.

Robust spike timing in an excitable cell with delayed feedback

The initiation and regeneration of pulsatile activity is a ubiquitous feature observed in excitable systems with delayed feedback. Here, we demonstrate this phenomenon in a real biological cell. We establish a critical role of the delay resulting from the finite propagation speed of electrical impulses in the emergence of persistent multiple-spike patterns. We predict the coexistence of a number of such patterns in a mathematical model and use a biological cell subject to dynamic clamp to confirm our predictions in a living mammalian system. Given the general nature of our mathematical model and experimental system, we believe that our results capture key hallmarks of physiological excitability that are fundamental to information processing.

State-space optimal feedback control of optogenetically driven neural activity

Objective.The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse.Approach.Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch.Main results.This model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications.Significance.To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches laid out and tested here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.

Patch Clamp Technology in the Twenty-First Century

In the almost four decades since its inception, the patch clamp technique has transitioned from a specialist skill to a method commonly used among many others in a lab. Development of patch clamp instrumentation has not been steady: A boost of product releases in rapid succession by multiple manufacturers in the 1990s had slowed to a trickle by the mid-2000s. In 2016, Sutter Instrument's entry into the market of turnkey patch clamp amplifier systems, defined as an amplifier with matching data acquisition hardware and software, caused a fresh breeze in a field in danger of going stale. Sutter has meanwhile completed the product line, culminating in the flagship dPatch^® Ultra-fast, Low-noise Digital Amplifier. The dPatch System constitutes a contemporary, digital design that features many firsts, including digital signal compensation, an extremely high bandwidth and fully integrated dynamic clamp capability, paired with the increasingly popular SutterPatch^® Software.This chapter compares feature sets of the new Sutter instrumentation with the established platforms by the other two providers of turnkey systems, Axon Instruments by Molecular Devices and HEKA Elektronik by Harvard Bioscience. A variety of products from other manufacturers, who rely on combination with components from other sources rather than offering turnkey systems, are listed, but for their conceptual diversity not compared at a great level of detail. The chapter further covers architectural considerations for patch clamp systems, headstage design, data acquisition strategies and efficient structuring of the recorded data, controlling and monitoring periphery, advanced technologies, such as software lock-in amplifier capability and dynamic clamp features, and application modules for efficient analysis of action potentials and postsynaptic events.

Dynamic Clamp on a Windows PC

Dynamic clamp is a powerful tool for interfacing computational models and real cells. We describe here how to set up and carry out dynamic clamp experiments using a patch clamp amplifier, a National Instruments data acquisition card, and the freely available QuB software that operates on a PC running MS Windows.

Theta resonance and synaptic modulation scale activity patterns in the medial entorhinal cortex stellate cells

Stellate cells (SCs) of the medial entorhinal cortex (MEC) are rich in hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, which are known to effectively shape their activity patterns. The explanatory mechanisms, however, have remained elusive. One important but previously unassessed possibility is that HCN channels control the gain of synaptic inputs to these cells. Here, we test this possibility in rat brain slices, while subjecting SCs to a stochastic synaptic bombardment using the dynamic clamp. We show that in the presence of synaptic noise, HCN channels mainly exert their influence by increasing the relative signal gain in the theta frequency through the theta modulation of stochastic synaptic inputs. This subthreshold synaptic modulation then translates into a spiking resonance, which steepens with excitation in the presence of HCN channels. We present here a systematic assessment of synaptic theta modulation and trace its implications to the suprathreshold control of firing rate motifs. Such analysis was yet lacking in the SC literature. Furthermore, we assess the impact of noise statistics on this gain modulation and indicate possible mechanisms for the emergence of membrane theta oscillations and synaptic ramps, as observed in vivo. We support the data with a computational model that further unveils a competing role of inhibition, suggesting important implications for MEC computations.

The Human Dynamic Clamp Reveals the Fronto-Parietal Network Linking Real-Time Social Coordination and Cognition

How does the brain allow us to interact with others? Social neuroscience has already provided some answers to these questions but has tended to treat high-level, cognitive interpretations of social behavior separately from the sensorimotor mechanisms upon which they rely. The goal here is to identify the underlying neural processes and mechanisms linking sensorimotor coordination and intention attribution. We combine the human dynamic clamp, a novel paradigm for studyingrealistic social behavior, with high-resolution electroencephalography. The collection of humanness and intention attribution reports, kinematics, and neural data affords an opportunity to relate brain activity to the ongoing social behavior. Behavioral results demonstrate that sensorimotor coordination influences the judgments of cooperativeness and humanness. Analysis of brain dynamics reveals two distinct networks related to the integration of visuo-motor information from self and other which overlap over the right parietal region. Furthermore, judgment of humanness and cooperation of others modulate the functional connectivity between this right parietal hub and the prefrontal cortex. These results reveal how distributed neural dynamics integrates information from "low-level" sensorimotor mechanisms and "high-level" social cognition to support the realistic social behaviors that play out in real time during interactive scenarios.

Clusters of cooperative ion channels enable a membrane-potential-based mechanism for short-term memory

Across biological systems, cooperativity between proteins enables fast actions, supra-linear responses, and long-lasting molecular switches. In the nervous system, however, the function of cooperative interactions between voltage-dependent ionic channels remains largely unknown. Based on mathematical modeling, we here demonstrate that clusters of strongly cooperative ion channels can plausibly form bistable conductances. Consequently, clusters are permanently switched on by neuronal spiking, switched off by strong hyperpolarization, and remain in their state for seconds after stimulation. The resulting short-term memory of the membrane potential allows to generate persistent firing when clusters of cooperative channels are present together with non-cooperative spike-generating conductances. Dynamic clamp experiments in rodent cortical neurons confirm that channel cooperativity can robustly induce graded persistent activity - a single-cell based, multistable mnemonic firing mode experimentally observed in several brain regions. We therefore propose that ion channel cooperativity constitutes an efficient cell-intrinsic implementation for short-term memories at the voltage level.

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks

Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, real-time drift adaptation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates closed-loop communication among living and artificial model neurons and connections, and contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

Shaw and Shal voltage-gated potassium channels mediate circadian changes in Drosophila clock neuron excitability

As in mammals, Drosophila circadian clock neurons display rhythms of activity with higher action potential firing rates and more positive resting membrane potentials during the day. This rhythmic excitability has been widely observed but, critically, its regulation remains unresolved. We have characterized and modelled the changes underlying these electrical activity rhythms in the lateral ventral clock neurons (LNvs). We show that currents mediated by the voltage-gated potassium channels Shaw (Kv3) and Shal (Kv4) oscillate in a circadian manner. Disruption of these channels, by expression of dominant negative (DN) subunits, leads to changes in circadian locomotor activity and shortens lifespan. LNv whole-cell recordings then show that changes in Shaw and Shal currents drive changes in action potential firing rate and that these rhythms are abolished when the circadian molecular clock is stopped. A whole-cell biophysical model using Hodgkin-Huxley equations can recapitulate these changes in electrical activity. Based on this model and by using dynamic clamp to manipulate clock neurons directly, we can rescue the pharmacological block of Shaw and Shal, restore the firing rhythm, and thus demonstrate the critical importance of Shaw and Shal. Together, these findings point to a key role for Shaw and Shal in controlling circadian firing of clock neurons and show that changes in clock neuron currents can account for this. Moreover, with dynamic clamp we can switch the LNvs between morning-like and evening-like states of electrical activity. We conclude that changes in Shaw and Shal underlie the daily oscillation in LNv firing rate.

Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics

OBJECTIVE

Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties.

METHODS

Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data.

RESULTS

We reproduced the response properties of five different types of chemical synapses, including both excitatory ( AMPA, NMDA) and inhibitory ( GABA_A, GABA_C, glycine) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons.

CONCLUSION

Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips.

SIGNIFICANCE

A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.

Asymmetrical voltage response in resonant neurons shaped by nonlinearities

The conventional impedance profile of a neuron can identify the presence of resonance and other properties of the neuronal response to oscillatory inputs, such as nonlinear response amplifications, but it cannot distinguish other nonlinear properties such as asymmetries in the shape of the voltage response envelope. Experimental observations have shown that the response of neurons to oscillatory inputs preferentially enhances either the upper or lower part of the voltage envelope in different frequency bands. These asymmetric voltage responses arise in a neuron model when it is submitted to high enough amplitude oscillatory currents of variable frequencies. We show how the nonlinearities associated to different ionic currents or present in the model as captured by its voltage equation lead to asymmetrical response and how high amplitude oscillatory currents emphasize this response. We propose a geometrical explanation for the phenomenon where asymmetries result not only from nonlinearities in their activation curves but also from nonlinearites captured by the nullclines in the phase-plane diagram and from the system's time-scale separation. In addition, we identify an unexpected frequency-dependent pattern which develops in the gating variables of these currents and is a product of strong nonlinearities in the system as we show by controlling such behavior by manipulating the activation curve parameters. The results reported in this paper shed light on the ionic mechanisms by which brain embedded neurons process oscillatory information.

Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study

One of the central goals of today's neuroscience is to achieve the conceivably most accurate classification of neuron types in the mammalian brain. As part of this research effort, electrophysiologists commonly utilize current clamp techniques to gain a detailed characterization of the neurons' physiological properties. While this approach has been useful, it is not well understood whether neurons that share physiological properties of a particular phenotype would also operate consistently under the action of natural synaptic inputs. We approached this problem by simulating a biophysically diverse population of model neurons based on 3 generic phenotypes. We exposed the model neurons to two types of stimulation to investigate their voltage responses under conventional current step protocols and under simulated synaptic bombardment. We extracted standard physiological parameters from the voltage responses elicited by current step stimulation and spike arrival times descriptive of the model's firing behavior under synaptic inputs. The biophysical phenotypes could be reliably identified using classification based on the 'static' physiological properties, but not the interspike interval-based parameters. However, the model neurons associated with the biophysically different phenotypes retained cell type specific features in the fine structure of their spike responses that allowed their accurate classification.

Estimating neuronal conductance model parameters using dynamic action potential clamp

BACKGROUND

Parameterization of neuronal membrane conductance models relies on data acquired from current clamp (CC) or voltage clamp (VC) recordings. Although the CC approach provides key information on a neuron's firing properties, it is often difficult to disentangle the influence of multiple conductances that contribute to the excitation properties of a real neuron. Isolation of a single conductance using pharmacological agents or heterologous expression simplifies analysis but requires extensive VC evaluation to explore the complete state behavior of the channel of interest.

NEW METHOD

We present an improved parameterization approach that uses data derived from dynamic action potential clamp (DAPC) recordings to extract conductance equation parameters. We demonstrate the utility of the approach by applying it to the standard Hodgkin-Huxley conductance model although other conductance models could be easily incorporated as well.

RESULTS

Using a fully simulated setup we show that, with as few as five action potentials previously recorded in DAPC mode, sodium conductance equation parameters can be determined with average parameter errors of less than 4% while action potential firing accuracy approaches 100%. In real DAPC experiments, we show that by "training" our model with five or fewer action potentials, subsequent firing lasting for several seconds could be predicted with ˜96% mean firing rate accuracy and 94% temporal overlap accuracy.

COMPARISON WITH EXISTING METHODS

Our DAPC-based approach surpasses the accuracy of VC-based approaches for extracting conductance equation parameters with a significantly reduced temporal overhead.

CONCLUSION

DAPC-based approach will facilitate the rapid and systematic characterization of neuronal channelopathies.

Intrinsic temporal tuning of neurons in the optic tectum is shaped by multisensory experience

For a biological neural network to be functional, its neurons need to be connected with synapses of appropriate strength, and each neuron needs to appropriately respond to its synaptic inputs. This second aspect of network tuning is maintained by intrinsic plasticity; yet it is often considered secondary to changes in connectivity and mostly limited to adjustments of overall excitability of each neuron. Here we argue that even nonoscillatory neurons can be tuned to inputs of different temporal dynamics and that they can routinely adjust this tuning to match the statistics of their synaptic activation. Using the dynamic clamp technique, we show that, in the tectum of Xenopus tadpole, neurons become selective for faster inputs when animals are exposed to fast visual stimuli but remain responsive to longer inputs in animals exposed to slower, looming, or multisensory stimulation. We also report a homeostatic cotuning between synaptic and intrinsic temporal properties of individual tectal cells. These results expand our understanding of intrinsic plasticity in the brain and suggest that there may exist an additional dimension of network tuning that has been so far overlooked.NEW & NOTEWORTHY We use dynamic clamp to show that individual neurons in the tectum of Xenopus tadpoles are selectively tuned to either shorter (more synchronous) or longer (less synchronous) synaptic inputs. We also demonstrate that this intrinsic temporal tuning is strongly shaped by sensory experiences. This new phenomenon, which is likely to be mediated by changes in sodium channel inactivation, is bound to have important consequences for signal processing and the development of local recurrent connections.

Dendritic computations captured by an effective point neuron model

Modeling single-neuron dynamics is the first step to quantitatively understand brain computation. Yet, the existing point neuron models fail to capture dendritic effects, which are crucial for neuronal information processing. We derive an effective point neuron model, which incorporates an additional synaptic integration current arising from the nonlinear interaction between synaptic currents across spatial dendrites. Our model captures the somatic voltage response of a neuron with complex dendrites and is capable of performing rich dendritic computations. Besides its computational efficiency in simulations, our model suggests reexamination of previous studies involving the decomposition of excitatory and inhibitory synaptic inputs based on the existing point neuron framework, e.g., the inhibition is often underestimated in experiment.

Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework.

Resurgent Na+ Current Offers Noise Modulation in Bursting Neurons

Neurons utilize bursts of action potentials as an efficient and reliable way to encode information. It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features. Here we examined the contribution of the persistent and resurgent components of voltage-gated Na+ currents in modulating the burst discharge in sensory neurons. Using mathematical modeling, theory and dynamic-clamp electrophysiology, we show that, distinct from the persistent Na+ component which is important for membrane resonance and burst generation, the resurgent Na+ can help stabilize burst timing features including the duration and intervals. Moreover, such a physiological role for the resurgent Na+ offered noise tolerance and preserved the regularity of burst patterns. Model analysis further predicted a negative feedback loop between the persistent and resurgent gating variables which mediate such gain in burst stability. These results highlight a novel role for the voltage-gated resurgent Na+ component in moderating the entropy of burst-encoded neural information.

Neural Evolution: Homology in Neuronal Networks

Do behaviors and neuronal networks that control them evolve together in lockstep, or do separate selection processes lead to their evolution? New research, dissecting the swim motor networks in two related nudibranch species, seeks to answer this question.

Dynamic clamping human and rabbit atrial calcium current: narrowing ICaL window abolishes early afterdepolarizations

Key points

Early‐afterdepolarizations (EADs) are abnormal action potential oscillations and a known cause of cardiac arrhythmias. Ventricular EADs involve reactivation of a Ca²⁺ current (I_CaL) in its ‘window region’ voltage range. However, electrical mechanisms of atrial EADs, a potential cause of atrial fibrillation, are poorly understood.

Atrial cells were obtained from consenting patients undergoing heart surgery, as well as from rabbits. I_CaL was blocked with nifedipine and then a hybrid patch clamp/mathematical‐modelling technique, ‘dynamic clamping’, was used to record action potentials at the same time as injecting an artificial, modifiable, I_CaL (I_CaL,D‐C).

Progressively widening the I_CaL,D‐C window region produced EADs of various types, dependent on window width. EAD production was strongest upon moving the activation (vs. inactivation) side of the window.

EADs were then induced by a different method: increasing I_CaL,D‐C amplitude and/or K⁺ channel‐blockade (4‐aminopyridine). Narrowing of the I_CaL,D‐C window by ∼10 mV abolished these EADs.

Atrial I_CaL window narrowing is worthy of further testing as a potential anti‐atrial fibrillation drug mechanism.

Abstract

Atrial early‐afterdepolarizations (EADs) may contribute to atrial fibrillation (AF), perhaps involving reactivation of L‐type Ca²⁺ current (I_CaL) in its window region voltage range. The present study aimed (i) to validate the dynamic clamp technique for modifying the I_CaL contribution to atrial action potential (AP) waveform; (ii) to investigate the effects of widening the window I_CaL on EAD‐propensity; and (iii) to test whether EADs from increased I_CaL and AP duration are supressed by narrowing the window I_CaL. I_CaL and APs were recorded from rabbit and human atrial myocytes by whole‐cell‐patch clamp. During AP recording, I_CaL was inhibited (3 µm nifedipine) and replaced by a dynamic clamp model current, I_CaL,D‐C (tuned to native I_CaL characteristics), computed in real‐time (every 50 µs) based on myocyte membrane potential. I_CaL,D‐C‐injection restored the nifedipine‐suppressed AP plateau. Widening the window I_CaL,D‐C, symmetrically by stepwise simultaneous equal shifts of half‐voltages (V_0.5) of I_CaL,D‐C activation (negatively) and inactivation (positively), generated EADs (single, multiple or preceding repolarization failure) in a window width‐dependent manner, as well as AP alternans. A stronger EAD‐generating effect resulted from independently shifting activation V_0.5 (asymmetrical widening) than inactivation V_0.5; for example, a 15 mV activation shift produced EADs in nine of 17 (53%) human atrial myocytes vs. 0 of 18 from inactivation shift (P < 0.05). In 11 rabbit atrial myocytes in which EADs were generated either by increasing the conductance of normal window width I_CaL,D‐C or subsequent 4‐aminopyridine (2 mm), window I_CaL,D‐C narrowing (10 mV) abolished EADs of all types (P < 0.05). The present study validated the dynamic clamp for I_CaL, which is novel in atrial cardiomyocytes, and showed that EADs of various types are generated by widening (particularly asymmetrically) the window I_CaL, as well as abolished by narrowing it. Window I_CaL narrowing is a potential therapeutic mechanism worth pursuing in the search for improved anti‐AF drugs.

Early‐afterdepolarizations (EADs) are abnormal action potential oscillations and a known cause of cardiac arrhythmias. Ventricular EADs involve reactivation of a Ca²⁺ current (I_CaL) in its ‘window region’ voltage range. However, electrical mechanisms of atrial EADs, a potential cause of atrial fibrillation, are poorly understood.

Atrial cells were obtained from consenting patients undergoing heart surgery, as well as from rabbits. I_CaL was blocked with nifedipine and then a hybrid patch clamp/mathematical‐modelling technique, ‘dynamic clamping’, was used to record action potentials at the same time as injecting an artificial, modifiable, I_CaL (I_CaL,D‐C).

Progressively widening the I_CaL,D‐C window region produced EADs of various types, dependent on window width. EAD production was strongest upon moving the activation (vs. inactivation) side of the window.

EADs were then induced by a different method: increasing I_CaL,D‐C amplitude and/or K⁺ channel‐blockade (4‐aminopyridine). Narrowing of the I_CaL,D‐C window by ∼10 mV abolished these EADs.

Atrial I_CaL window narrowing is worthy of further testing as a potential anti‐atrial fibrillation drug mechanism.

Thermal block of action potentials is primarily due to voltage-dependent potassium currents: a modeling study

OBJECTIVE

Thermal block of action potential conduction using infrared lasers is a new modality for manipulating neural activity. It could be used for analysis of the nervous system and for therapeutic applications. We sought to understand the mechanisms of thermal block.

APPROACH

To analyze the mechanisms of thermal block, we studied both the original Hodgkin/Huxley model, and a version modified to more accurately match experimental data on thermal responses in the squid giant axon.

MAIN RESULTS

Both the original and modified models suggested that thermal block, especially at higher temperatures, is primarily due to a depolarization-activated hyperpolarization as increased temperature leads to faster activation of voltage-gated potassium ion channels. The minimum length needed to block an axon scaled with the square root of the axon's diameter.

SIGNIFICANCE

The results suggest that voltage-dependent potassium ion channels play a major role in thermal block, and that relatively short lengths of axon could be thermally manipulated to selectively block fine, unmyelinated axons, such as C fibers, that carry pain and other sensory information.

Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations

Several neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these issues by investigating a minimal network model consisting of (i) a non-oscillatory linear resonator (band-pass filter) with 2D dynamics, (ii) a passive cell (low-pass filter) with 1D linear dynamics, and (iii) nonlinear graded synaptic connections (excitatory or inhibitory) with instantaneous dynamics. We demonstrate that (i) the network oscillations crucially depend on the presence of MPR in the resonator, (ii) they are amplified by the network connectivity, (iii) they develop relaxation oscillations for high enough levels of mutual inhibition/excitation, and (iv) the network frequency monotonically depends on the resonators resonant frequency. We explain these phenomena using a reduced adapted version of the classical phase-plane analysis that helps uncovering the type of effective network nonlinearities that contribute to the generation of network oscillations. We extend our results to networks having cells with 2D dynamics. Our results have direct implications for network models of firing rate type and other biological oscillatory networks (e.g, biochemical, genetic).

RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience

Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.

Multiplicative noise is beneficial for the transmission of sensory signals in simple neuron models

We study simple integrate-and-fire type models with multiplicative noise and consider the transmission of a weak and slow signal, i.e. a signal that evokes a small modulation of the instantaneous firing rate on time scales that are much larger than the membrane time scale and the mean interspike interval. The specific question of interest is whether and how the state-dependence of the noise can be optimized with respect to information transmission. First, in a simple model in which the noise intensity varies linearly with the state variable, we show analytically that multiplicative fluctuations may benefit the signal transfer and we elucidate the mechanism for this improvement. In a conductance-based integrate-and-fire model with synaptically filtered shot-noise input, we show by means of extended numerical simulations that also in a biophysically more relevant situation, multiplicative noise can enhance the signal-to-noise ratio. Our results shed light on a so far unexplored aspect of stochastic signal transmission in neural systems.

Intrinsic physiological properties underlie auditory response diversity in the avian cochlear nucleus

Sensory systems exploit parallel processing of stimulus features to enable rapid, simultaneous extraction of information. Mechanisms that facilitate this differential extraction of stimulus features can be intrinsic or synaptic in origin. A subdivision of the avian cochlear nucleus, nucleus angularis (NA), extracts sound intensity information from the auditory nerve and contains neurons that exhibit diverse responses to sound and current injection. NA neurons project to multiple regions ascending the auditory brain stem including the superior olivary nucleus, lateral lemniscus, and avian inferior colliculus, with functional implications for inhibitory gain control and sound localization. Here we investigated whether the diversity of auditory response patterns in NA can be accounted for by variation in intrinsic physiological features. Modeled sound-evoked auditory nerve input was applied to NA neurons with dynamic clamp during in vitro whole cell recording at room temperature. Temporal responses to auditory nerve input depended on variation in intrinsic properties, and the low-threshold K⁺ current was implicated as a major contributor to temporal response diversity and neuronal input-output functions. An auditory nerve model of acoustic amplitude modulation produced synchrony coding of modulation frequency that depended on the intrinsic physiology of the individual neuron. In Primary-Like neurons, varying low-threshold K⁺ conductance with dynamic clamp altered temporal modulation tuning bidirectionally. Taken together, these data suggest that intrinsic physiological properties play a key role in shaping auditory response diversity to both simple and more naturalistic auditory stimuli in the avian cochlear nucleus. NEW & NOTEWORTHY This article addresses the question of how the nervous system extracts different information in sounds. Neurons in the cochlear nucleus show diverse responses to acoustic stimuli that may allow for parallel processing of acoustic features. The present studies suggest that diversity in intrinsic physiological features of individual neurons, including levels of a low voltage-activated K⁺ current, play a major role in regulating the diversity of auditory responses.

Resilience to Pain: A Peripheral Component Identified Using Induced Pluripotent Stem Cells and Dynamic Clamp

Pain is a complex process that involves both detection in the peripheral nervous system and perception in the CNS. Individual-to-individual differences in pain are well documented, but not well understood. Here we capitalized on inherited erythromelalgia (IEM), a well characterized human genetic model of chronic pain, and studied a unique family containing related IEM subjects with the same disease-causing Na_V1.7 mutation, which is known to make dorsal root ganglion (DRG) neurons hyperexcitable, but different pain profiles (affected son with severe pain, affected mother with moderate pain, and an unaffected father). We show, first, that, at least in some cases, relative sensitivity to pain can be modeled in subject-specific induced pluripotent stem cell (iPSC)-derived sensory neurons in vitro; second, that, in some cases, mechanisms operating in peripheral sensory neurons contribute to interindividual differences in pain; and third, using whole exome sequencing (WES) and dynamic clamp, we show that it is possible to pinpoint a specific variant of another gene, KCNQ in this particular kindred, that modulates the excitability of iPSC-derived sensory neurons in this family. While different gene variants may modulate DRG neuron excitability and thereby contribute to interindividual differences in pain in other families, this study shows that subject-specific iPSCs can be used to model interindividual differences in pain. We further provide proof-of-principle that iPSCs, WES, and dynamic clamp can be used to investigate peripheral mechanisms and pinpoint specific gene variants that modulate pain signaling and contribute to interindividual differences in pain.SIGNIFICANCE STATEMENT Individual-to-individual differences in pain are well documented, but not well understood. In this study, we show, first, that, at least in some cases, relative sensitivity to pain can be modeled in subject-specific induced pluripotent stem cell-derived sensory neurons in vitro; second, that, in some cases, mechanisms operating in peripheral sensory neurons contribute to interindividual differences in pain; and third, using whole exome sequencing and dynamic clamp, we show that it is possible to pinpoint a specific gene variant that modulates pain signaling and contributes to interindividual differences in pain.

Voltage-gated sodium currents in cerebellar Purkinje neurons: functional and molecular diversity

Purkinje neurons, the sole output of the cerebellar cortex, deliver GABA-mediated inhibition to the deep cerebellar nuclei. To subserve this critical function, Purkinje neurons fire repetitively, and at high frequencies, features that have been linked to the unique properties of the voltage-gated sodium (Nav) channels expressed. In addition to the rapidly activating and inactivating, or transient, component of the Nav current (INaT) present in many types of central and peripheral neurons, Purkinje neurons, also expresses persistent (INaP) and resurgent (INaR) Nav currents. Considerable progress has been made in detailing the biophysical properties and identifying the molecular determinants of these discrete Nav current components, as well as defining their roles in the regulation of Purkinje neuron excitability. Here, we review this important work and highlight the remaining questions about the molecular mechanisms controlling the expression and the functioning of Nav currents in Purkinje neurons. We also discuss the impact of the dynamic regulation of Nav currents on the functioning of individual Purkinje neurons and cerebellar circuits.

Neural Coding With Bursts-Current State and Future Perspectives

Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of spikes, will more reliably cross a synapse, increasing the likelihood of eliciting a postsynaptic spike, depending on the specific short-term plasticity at that synapse. Both the number and the temporal pattern of spikes in a burst provide a coding space that lies within the temporal integration realm of single neurons. Bursts have been observed in many species, including the non-mammalian, and in brain regions that range from subcortical to cortical. Despite their widespread presence and potential relevance, the uncertainties of how to classify bursts seems to have limited the research into the coding possibilities for bursts. The present series of research articles provides new insights into the relevance and interpretation of bursts across different neural circuits, and new methods for their analysis. Here, we provide a succinct introduction to the history of burst coding and an overview of recent work on this topic.

All-Optical Electrophysiology for High-Throughput Functional Characterization of a Human iPSC-Derived Motor Neuron Model of ALS

Human induced pluripotent stem cell (iPSC)-derived neurons are an attractive substrate for modeling disease, yet the heterogeneity of these cultures presents a challenge for functional characterization by manual patch-clamp electrophysiology. Here, we describe an optimized all-optical electrophysiology, “Optopatch,” pipeline for high-throughput functional characterization of human iPSC-derived neuronal cultures. We demonstrate the method in a human iPSC-derived motor neuron (iPSC-MN) model of amyotrophic lateral sclerosis (ALS). In a comparison of iPSC-MNs with an ALS-causing mutation (SOD1 A4V) with their genome-corrected controls, the mutants showed elevated spike rates under weak or no stimulus and greater likelihood of entering depolarization block under strong optogenetic stimulus. We compared these results with numerical simulations of simple conductance-based neuronal models and with literature results in this and other iPSC-based models of ALS. Our data and simulations suggest that deficits in slowly activating potassium channels may underlie the changes in electrophysiology in the SOD1 A4V mutation.

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All-optical electrophysiology enables high-throughput assays in hiPSC-derived neurons

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Neurons derived from ALS patients fire differently from genome-corrected controls

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A deficit in the Kv7 potassium current can explain the difference in firing

Applications of Dynamic Clamp to Cardiac Arrhythmia Research: Role in Drug Target Discovery and Safety Pharmacology Testing

Dynamic clamp, a hybrid-computational-experimental technique that has been used to elucidate ionic mechanisms underlying cardiac electrophysiology, is emerging as a promising tool in the discovery of potential anti-arrhythmic targets and in pharmacological safety testing. Through the injection of computationally simulated conductances into isolated cardiomyocytes in a real-time continuous loop, dynamic clamp has greatly expanded the capabilities of patch clamp outside traditional static voltage and current protocols. Recent applications include fine manipulation of injected artificial conductances to identify promising drug targets in the prevention of arrhythmia and the direct testing of model-based hypotheses. Furthermore, dynamic clamp has been used to enhance existing experimental models by addressing their intrinsic limitations, which increased predictive power in identifying pro-arrhythmic pharmacological compounds. Here, we review the recent advances of the dynamic clamp technique in cardiac electrophysiology with a focus on its future role in the development of safety testing and discovery of anti-arrhythmic drugs.

A Dynamic Clamp on Every Rig

The dynamic clamp should be a standard part of every cellular electrophysiologist’s toolbox. That it is not, even 25 years after its introduction, comes down to three issues: money, the disruption that adding dynamic clamp to an existing electrophysiology rig entails, and the technical prowess required of experimenters. These have been valid and limiting issues in the past, but no longer. Technological advances associated with the so-called maker movement render them moot. We demonstrate this by implementing a fast (∼100 kHz) dynamic clamp system using an inexpensive microcontroller (Teensy 3.6). The overall cost of the system is less than USD$100, and assembling it requires no prior electronics experience. Modifying it—for example, to add Hodgkin–Huxley-style conductances—requires no prior programming experience. The system works together with existing electrophysiology data acquisition systems (for Macintosh, Windows, and Linux); it does not attempt to supplant them. Moreover, the process of assembling, modifying, and using the system constitutes a useful pedagogical exercise for students and researchers with no background but an interest in electronics and programming. We demonstrate the system’s utility by implementing conductances as fast as a transient sodium conductance and as complex as the Ornstein–Uhlenbeck conductances of the “point conductance” model of synaptic background activity.

Using insects to drive mobile robots - hybrid robots bridge the gap between biological and artificial systems

The use of mobile robots is an effective method of validating sensory-motor models of animals in a real environment. The well-identified insect sensory-motor systems have been the major targets for modeling. Furthermore, mobile robots implemented with such insect models attract engineers who aim to avail advantages from organisms. However, directly comparing the robots with real insects is still difficult, even if we successfully model the biological systems, because of the physical differences between them. We developed a hybrid robot to bridge the gap. This hybrid robot is an insect-controlled robot, in which a tethered male silkmoth (Bombyx mori) drives the robot in order to localize an odor source. This robot has the following three advantages: 1) from a biomimetic perspective, the robot enables us to evaluate the potential performance of future insect-mimetic robots; 2) from a biological perspective, the robot enables us to manipulate the closed-loop of an onboard insect for further understanding of its sensory-motor system; and 3) the robot enables comparison with insect models as a reference biological system. In this paper, we review the recent works regarding insect-controlled robots and discuss the significance for both engineering and biology.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

OBJECTIVE

Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model.

APPROACH

This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity.

MAIN RESULTS

Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology.

SIGNIFICANCE

Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI)

The ability to experimentally perturb biological systems has traditionally been limited to static pre-programmed or operator-controlled protocols. In contrast, real-time control allows dynamic probing of biological systems with perturbations that are computed on-the-fly during experimentation. Real-time control applications for biological research are available; however, these systems are costly and often restrict the flexibility and customization of experimental protocols. The Real-Time eXperiment Interface (RTXI) is an open source software platform for achieving hard real-time data acquisition and closed-loop control in biological experiments while retaining the flexibility needed for experimental settings. RTXI has enabled users to implement complex custom closed-loop protocols in single cell, cell network, animal, and human electrophysiology studies. RTXI is also used as a free and open source, customizable electrophysiology platform in open-loop studies requiring online data acquisition, processing, and visualization. RTXI is easy to install, can be used with an extensive range of external experimentation and data acquisition hardware, and includes standard modules for implementing common electrophysiology protocols.

Activity Clamp Provides Insights into Paradoxical Effects of the Anti-Seizure Drug Carbamazepine

A major challenge in experimental epilepsy research is to reconcile the effects of anti-epileptic drugs (AEDs) on individual neurons with their network-level actions. Highlighting this difficulty, it is unclear why carbamazepine (CBZ), a frontline AED with a known molecular mechanism, has been reported to increase epileptiform activity in several clinical and experimental studies. We confirmed in an in vitro mouse model (in both sexes) that the frequency of interictal bursts increased after CBZ perfusion. To address the underlying mechanisms, we developed a method, activity clamp, to distinguish the response of individual neurons from network-level actions of CBZ. We first recorded barrages of synaptic conductances from neurons during epileptiform activity and then replayed them in pharmacologically isolated neurons under control conditions and in the presence of CBZ. CBZ consistently decreased the reliability of the second action potential in each burst of activity. Conventional current-clamp recordings using excitatory ramp or square-step current injections failed to reveal this effect. Network modeling showed that a CBZ-induced decrease of neuron recruitment during epileptic bursts can lead to an increase in burst frequency at the network level by reducing the refractoriness of excitatory transmission. By combining activity clamp with computer simulations, the present study provides a potential explanation for the paradoxical effects of CBZ on epileptiform activity.

SIGNIFICANCE STATEMENT The effects of anti-epileptic drugs on individual neurons are difficult to separate from their network-level actions. Although carbamazepine (CBZ) has a known anti-epileptic mechanism, paradoxically, it has also been reported to increase epileptiform activity in clinical and experimental studies. To investigate this paradox during realistic neuronal epileptiform activity, we developed a method, activity clamp, to distinguish the effects of CBZ on individual neurons from network-level actions. We demonstrate that CBZ consistently decreases the reliability of the second action potential in each burst of epileptiform activity. Network modeling shows that this effect on individual neuronal responses could explain the paradoxical effect of CBZ at the network level.

Nonlinear effects of hyperpolarizing shifts in activation of mutant Nav1.7 channels on resting membrane potential

The Na_v1.7 sodium channel is preferentially expressed within dorsal root ganglion (DRG) and sympathetic ganglion neurons. Gain-of-function mutations that cause the painful disorder inherited erythromelalgia (IEM) shift channel activation in a hyperpolarizing direction. When expressed within DRG neurons, these mutations produce a depolarization of resting membrane potential (RMP). The biophysical basis for the depolarized RMP has to date not been established. To explore the effect on RMP of the shift in activation associated with a prototypical IEM mutation (L858H), we used dynamic-clamp models that represent graded shifts that fractionate the effect of the mutation on activation voltage dependence. Dynamic-clamp recording from DRG neurons using a before-and-after protocol for each cell made it possible, even in the presence of cell-to-cell variation in starting RMP, to assess the effects of these graded mutant models. Our results demonstrate a nonlinear, progressively larger effect on RMP as the shift in activation voltage dependence becomes more hyperpolarized. The observed differences in RMP were predicted by the "late" current of each mutant model. Since the depolarization of RMP imposed by IEM mutant channels is known, in itself, to produce hyperexcitability of DRG neurons, the development of pharmacological agents that normalize or partially normalize activation voltage dependence of IEM mutant channels merits further study.NEW & NOTEWORTHY Inherited erythromelalgia (IEM), the first human pain disorder linked to a sodium channel, is widely regarded as a genetic model of neuropathic pain. IEM is produced by Na_v1.7 mutations that hyperpolarize activation. These mutations produce a depolarization of resting membrane potential (RMP) in dorsal root ganglion neurons. Using dynamic clamp to explore the effect on RMP of the shift in activation, we demonstrate a nonlinear effect on RMP as the shift in activation voltage dependence becomes more hyperpolarized.

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

Combined Changes in Chloride Regulation and Neuronal Excitability Enable Primary Afferent Depolarization to Elicit Spiking without Compromising its Inhibitory Effects

The central terminals of primary afferent fibers experience depolarization upon activation of GABAA receptors (GABAAR) because their intracellular chloride concentration is maintained above electrochemical equilibrium. Primary afferent depolarization (PAD) normally mediates inhibition via sodium channel inactivation and shunting but can evoke spikes under certain conditions. Antidromic (centrifugal) conduction of these spikes may contribute to neurogenic inflammation while orthodromic (centripetal) conduction could contribute to pain in the case of nociceptive fibers. PAD-induced spiking is assumed to override presynaptic inhibition. Using computer simulations and dynamic clamp experiments, we sought to identify which biophysical changes are required to enable PAD-induced spiking and whether those changes necessarily compromise PAD-mediated inhibition. According to computational modeling, a depolarizing shift in GABA reversal potential (EGABA) and increased intrinsic excitability (manifest as altered spike initiation properties) were necessary for PAD-induced spiking, whereas increased GABAAR conductance density (ḡGABA) had mixed effects. We tested our predictions experimentally by using dynamic clamp to insert virtual GABAAR conductances with different EGABA and kinetics into acutely dissociated dorsal root ganglion (DRG) neuron somata. Comparable experiments in central axon terminals are prohibitively difficult but the biophysical requirements for PAD-induced spiking are arguably similar in soma and axon. Neurons from naïve (i.e. uninjured) rats were compared before and after pharmacological manipulation of intrinsic excitability, and against neurons from nerve-injured rats. Experimental data confirmed that, in most neurons, both predicted changes were necessary to yield PAD-induced spiking. Importantly, such changes did not prevent PAD from inhibiting other spiking or from blocking spike propagation. In fact, since the high value of ḡGABA required for PAD-induced spiking still mediates strong inhibition, we conclude that PAD-induced spiking does not represent failure of presynaptic inhibition. Instead, diminished PAD caused by reduction of ḡGABA poses a greater risk to presynaptic inhibition and the sensory processing that relies upon it.