A biophysical perspective on the resilience of neuronal excitability across timescales

Neuronal membrane excitability must be resilient to perturbations that can take place over timescales from milliseconds to months (or even years in long-lived animals). A great deal of attention has been paid to classes of homeostatic mechanisms that contribute to long-term maintenance of neuronal excitability through processes that alter a key structural parameter: the number of ion channel proteins present at the neuronal membrane. However, less attention has been paid to the self-regulating 'automatic' mechanisms that contribute to neuronal resilience by virtue of the kinetic properties of ion channels themselves. Here, we propose that these two sets of mechanisms are complementary instantiations of feedback control, together enabling resilience on a wide range of temporal scales. We further point to several methodological and conceptual challenges entailed in studying these processes - both of which involve enmeshed feedback control loops - and consider the consequences of these mechanisms of resilience.

Sodium channel slow inactivation normalizes firing in axons with uneven conductance distributions

The Na+ channels that are important for action potentials show rapid inactivation, a state in which they do not conduct, although the membrane potential remains depolarized.1,2 Rapid inactivation is a determinant of millisecond-scale phenomena, such as spike shape and refractory period. Na+ channels also inactivate orders of magnitude more slowly, and this slow inactivation has impacts on excitability over much longer timescales than those of a single spike or a single inter-spike interval.3,4,5,6,7,8,9,10 Here, we focus on the contribution of slow inactivation to the resilience of axonal excitability11,12 when ion channels are unevenly distributed along the axon. We study models in which the voltage-gated Na+ and K+ channels are unevenly distributed along axons with different variances, capturing the heterogeneity that biological axons display.13,14 In the absence of slow inactivation, many conductance distributions result in spontaneous tonic activity. Faithful axonal propagation is achieved with the introduction of Na+ channel slow inactivation. This "normalization" effect depends on relations between the kinetics of slow inactivation and the firing frequency. Consequently, neurons with characteristically different firing frequencies will need to implement different sets of channel properties to achieve resilience. The results of this study demonstrate the importance of the intrinsic biophysical properties of ion channels in normalizing axonal function.

Identifying regulation with adversarial surrogates

Homeostasis, the ability to maintain a relatively constant internal environment in the face of perturbations, is a hallmark of biological systems. It is believed that this constancy is achieved through multiple internal regulation and control processes. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. In this work, we develop a robust data-driven method to identify these objectives, namely to understand: "what does the system care about?". We propose an algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), that receives an array of temporal measurements of the system and outputs a candidate for the control objective, expressed as a combination of observed variables. IRAS is an iterative algorithm consisting of two competing players. The first player, realized by an artificial deep neural network, aims to minimize a measure of invariance we refer to as the coefficient of regulation. The second player aims to render the task of the first player more difficult by forcing it to extract information about the temporal structure of the data, which is absent from similar "surrogate" data. We test the algorithm on four synthetic and one natural data set, demonstrating excellent empirical results. Interestingly, our approach can also be used to extract conserved quantities, e.g., energy and momentum, in purely physical systems, as we demonstrate empirically.

Visual detection of time-varying signals: Opposing biases and their timescales

Human visual perception is a complex, dynamic and fluctuating process. In addition to the incoming visual stimulus, it is affected by many other factors including temporal context, both external and internal to the observer. In this study we investigate the dynamic properties of psychophysical responses to a continuous stream of visual near-threshold detection tasks. We manipulate the incoming signals to have temporal structures with various characteristic timescales. Responses of human observers to these signals are analyzed using tools that highlight their dynamical features as well. Our experiments show two opposing biases that shape perceptual decision making simultaneously: positive recency, biasing towards repeated response; and adaptation, entailing an increased probability of changed response. While both these effects have been reported in previous work, our results shed new light on the timescales involved in these effects, and on their interplay with varying inputs. We find that positive recency is a short-term bias, inversely correlated with response time, suggesting it can be compensated by afterthought. Adaptation, in contrast, reflects trends over longer times possibly including multiple previous trials. Our entire dataset, which includes different input signal temporal structures, is consistent with a simple model with the two biases characterized by a fixed parameter set. These results suggest that perceptual biases are inherent features which are not flexible to tune to input signals.

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks

Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.

Inhibition increases response variability and reduces stimulus discrimination in random networks of cortical neurons

Much of what is known about the contribution of inhibition to stimulus discrimination is due to extensively studied sensory systems, which are highly structured neural circuits. The effect of inhibition on stimulus representation in less structured networks is not as clear. Here we exercise a biosynthetic approach in order to study the impacts of inhibition on stimulus representation in non-specialized network anatomy. Combining pharmacological manipulation, multisite electrical stimulation and recording from ex-vivo randomly rewired networks of cortical neurons, we quantified the effects of inhibition on response variability and stimulus discrimination at the population and single unit levels. We find that blocking inhibition quenches variability of responses evoked by repeated stimuli and enhances discrimination between stimuli that invade the network from different spatial loci. Enhanced stimulus discrimination is reserved for representation schemes that are based on temporal relation between spikes emitted in groups of neurons. Our data indicate that - under intact inhibition - the response to a given stimulus is a noisy version of the response evoked in the absence of inhibition. Spatial analysis suggests that the dispersion effect of inhibition is due to disruption of an otherwise coherent, wave-like propagation of activity.

Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model

Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

Universality, complexity and the praxis of biology: Two case studies

The phenomenon of biology provides a prime example for a naturally occurring complex system. The approach to this complexity reflects the tension between a reductionist, reverse-engineering stance, and more abstract, systemic ones. Both of us are reductionists, but our observations challenge reductionism, at least the naive version of it. Here we describe the challenge, focusing on two universal characteristics of biological complexity: two-way microscopic-macroscopic degeneracy, and lack of time scale separation within and between levels of organization. These two features and their consequences for the praxis of experimental biology, reflect inherent difficulties in separating the dynamics of any given level of organization from the coupled dynamics of all other levels, including the environment within which the system is embedded. Where these difficulties are not deeply acknowledged, the impacts of fallacies that are inherent to naive reductionism are significant. In an era where technology enables experimental high-resolution access to numerous observables, the challenge faced by the mature reductionist-identification of relevant microscopic variables-becomes more demanding than ever. The demonstrations provided here are taken from two very different biological realizations: populations of microorganisms and populations of neurons, thus making the lesson potentially general.

Slow dynamics in features of synchronized neural network responses

In this report trial-to-trial variations in the synchronized responses of neural networks are explored over time scales of minutes, in ex-vivo large scale cortical networks. We show that sub-second measures of the individual synchronous response, namely-its latency and decay duration, are related to minutes-scale network response dynamics. Network responsiveness is reflected as residency in, or shifting amongst, areas of the latency-decay plane. The different sensitivities of latency and decay durations to synaptic blockers imply that these two measures reflect aspects of inhibitory and excitatory activities. Taken together, the data suggest that trial-to-trial variations in the synchronized responses of neural networks might be related to effective excitation-inhibition ratio being a dynamic variable over time scales of minutes.

Synaptic dynamics contribute to long-term single neuron response fluctuations

Firing rate variability at the single neuron level is characterized by long-memory processes and complex statistics over a wide range of time scales (from milliseconds up to several hours). Here, we focus on the contribution of non-stationary efficacy of the ensemble of synapses–activated in response to a given stimulus–on single neuron response variability. We present and validate a method tailored for controlled and specific long-term activation of a single cortical neuron in vitro via synaptic or antidromic stimulation, enabling a clear separation between two determinants of neuronal response variability: membrane excitability dynamics vs. synaptic dynamics. Applying this method we show that, within the range of physiological activation frequencies, the synaptic ensemble of a given neuron is a key contributor to the neuronal response variability, long-memory processes and complex statistics observed over extended time scales. Synaptic transmission dynamics impact on response variability in stimulation rates that are substantially lower compared to stimulation rates that drive excitability resources to fluctuate. Implications to network embedded neurons are discussed.

Controlling neural network responsiveness: tradeoffs and constraints

In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input-output relations and activity dependent dispersion of neuronal pair-wise correlations across the network.

Self-organized criticality in single-neuron excitability

We present experimental and theoretical arguments, at the single-neuron level, suggesting that neuronal response fluctuations reflect a process that positions the neuron near a transition point that separates excitable and unexcitable phases. This view is supported by the dynamical properties of the system as observed in experiments on isolated cultured cortical neurons, as well as by a theoretical mapping between the constructs of self-organized criticality and membrane excitability biophysics.

Synchronization by elastic neuronal latencies

Psychological and physiological considerations entail that formation and functionality of neuronal cell assemblies depend upon synchronized repeated activation such as zero-lag synchronization. Several mechanisms for the emergence of this phenomenon have been suggested, including the global network quantity, the greatest common divisor of neuronal circuit delay loops. However, they require strict biological prerequisites such as precisely matched delays and connectivity, and synchronization is represented as a stationary mode of activity instead of a transient phenomenon. Here we show that the unavoidable increase in neuronal response latency to ongoing stimulation serves as a nonuniform gradual stretching of neuronal circuit delay loops. This apparent nuisance is revealed to be an essential mechanism in various types of neuronal time controllers, where synchronization emerges as a transient phenomenon and without predefined precisely matched synaptic delays. These findings are described in an experimental procedure where conditioned stimulations were enforced on a circuit of neurons embedded within a large-scale network of cortical cells in vitro, and are corroborated and extended by simulations of circuits composed of Hodgkin-Huxley neurons with time-dependent latencies. These findings announce a cortical time scale for time controllers based on tens of microseconds stretching of neuronal circuit delay loops per spike. They call for a reexamination of the role of the temporal periodic mode in brain functionality using advanced in vitro and in vivo experiments.

Enhancement of neural representation capacity by modular architecture in networks of cortical neurons

Biological networks are ubiquitously modular, a feature that is believed to be essential for the enhancement of their functional capacities. Here, we have used a simple modular in vitro design to examine the possibility that modularity enhances network functionality in the context of input representation. We cultured networks of cortical neurons obtained from newborn rats in vitro on substrate-integrated multi-electrode arrays, forcing the network to develop two well-defined modules of neural populations that are coupled by a narrow canal. We measured the neural activity, and examined the capacity of each module to individually classify (i.e. represent) spatially distinct electrical stimuli and propagate input-specific activity features to their downstream coupled counterpart. We show that, although each of the coupled modules maintains its autonomous functionality, a significant enhancement of representational capacity is achieved when the system is observed as a whole. We interpret our results in terms of a relative decorrelation effect imposed by weak coupling between modules.

Interactions between network synchrony and the dynamics of neuronal threshold

Synchronous activity impacts on a range of functional brain capacities in health and disease. To address the interrelations between cellular level activity and network-wide synchronous events, we implemented in vitro a recently introduced technique, the response clamp, which enables online monitoring of single neuron threshold dynamics while ongoing network synchronous activity continues uninterrupted. We show that the occurrence of a synchronous network event causes a significant biphasic change in the single neuron threshold. These threshold dynamics are correlated across the neurons constituting the network and are entailed by the input to the neurons rather than by their own spiking (i.e., output) activity. The magnitude of network activity during a synchronous event is correlated with the threshold state of individual neurons at the event's onset. Recovery from the impact of a given synchronous event on the neuronal threshold lasts several seconds and seems to be a key determinant of the time to the next spontaneously occurring synchronous event. Moreover, the neuronal threshold is shown to be correlated with the excitability dynamics of the entire network. We conclude that the relations between the two levels (network activity and the single neuron threshold) should be thought of in terms that emphasize their interactive nature.

Modulation of excessive neuronal activity by fibroblasts: potential use in treatment of Parkinson's disease

PURPOSE

A number of neurological disorders are marked by increased or aberrant frequency of neuronal discharge in specific parts of the brain. Administration of drugs such as antiepileptic compounds results in the depression of neuronal activity in the whole brain, with the potential for serious side-effects. In the search for additional therapies to reduce the unphysiological electrical activity of over-active brain foci, we have examined the effect of fibroblasts transplanted to areas responsible for motor dysfunction in hemi-parkinsonian rats, since bursting synchronous discharges in internal segment of globus pallidus (GPi) are thought to be partially responsible for the movement disorders of PD. Fibroblasts express gap junctions and ion channels, and so, when transplanted to brain tissue, can potentially modulate excessive electrical activity.

METHODS

Neonatal cortical neurons were cultured on multi-electrode arrays, and their electrical activity was evaluated before and after fibroblast seeding. Unilateral 6-hydroxydopamine (6-OHDA) lesion was carried out in Fischer rats. Lesioned or control rats were transplanted with either syngeneic dermal fibroblasts, microfine glass beads, ibotenic acid, or physiological saline, in the entopeduncular nucleus (EP). Apomorphine-induced rotational behavior and L-dopa-induced dyskinetic movements were evaluated before transplantation (baseline) and 2, 4, 8, 12, and 24 weeks following transplantation. Following behavioral experiments, rats were perfused with 4% formaldehyde in PBS for immunohistochemical study of the brain.

RESULTS

We demonstrate in vitro that the introduction of fibroblasts into a network of neurons does not interfere with overall functional measures of activity, while moderately altering the characteristics of synchronous neuronal discharge. In rats with unilateral 6-hydroxydopamine lesions of the nigro-striatal dopaminergic pathway, apomorphine-induced rotations were reduced by more than 60% following ipsilateral transplantation of fibroblasts to the EP. L-Dopa-induced dyskinesia was also significantly reduced. Transplantation of inert microspheres, or chemical lesion of the same area with ibotenic acid, did not produce beneficial effects on parkinsonian symptomatology.

CONCLUSION

Fibroblast transplantation could be an alternative treatment strategy for the parkinsonian patient.

Relational Dynamics in Perception: Impacts on Trial-to-trial Variation

We show that trial-to-trial variability in sensory detection of a weak visual stimulus is dramatically diminished when rather than presenting a fixed stimulus contrast, fluctuations in a subject's judgment are matched by fluctuations in stimulus contrast. This attenuation of fluctuations does not involve a change in the subject's psychometric function. The result is consistent with the interpretation of trial-to-trial variability in this sensory detection task being a high-level meta-cognitive control process that explores for something that our brains are so used to: subject-object relational dynamics.

Neuronal response clamp

Responses of individual neurons to ongoing input are highly variable, reflecting complex threshold dynamics. Experimental access to this threshold dynamics is required in order to fully characterize neuronal input–output relationships. The challenge is practically intractable using present day experimental paradigms due to the cumulative, non-linear interactions involved. Here we introduce the Neuronal Response Clamp, a closed-loop technique enabling control over the instantaneous response probability of the neuron. The potential of the technique is demonstrated by showing direct access to threshold dynamics of cortical neuron in vitro using extracellular recording and stimulation, over timescales ranging from seconds to many hours. Moreover, the method allowed us to expose the sensitivity of threshold dynamics to spontaneous input from the network in which the neuron is embedded. The Response-Clamp technique follows the rationale of the voltage-clamp and dynamic-clamp approaches, extending it to the neuron's spiking behavior. The general framework offered here is applicable in the study of other neural systems, beyond the single neuron level.

A generic framework for real-time multi-channel neuronal signal analysis, telemetry control, and sub-millisecond latency feedback generation

Distinct modules of the neural circuitry interact with each other and (through the motor-sensory loop) with the environment, forming a complex dynamic system. Neuro-prosthetic devices seeking to modulate or restore CNS function need to interact with the information flow at the level of neural modules electrically, bi-directionally and in real-time. A set of freely available generic tools is presented that allow computationally demanding multi-channel short-latency bi-directional interactions to be realized in in vivo and in vitro preparations using standard PC data acquisition and processing hardware and software (Mathworks Matlab and Simulink). A commercially available 60-channel extracellular multi-electrode recording and stimulation set-up connected to an ex vivo developing cortical neuronal culture is used as a model system to validate the method. We demonstrate how complex high-bandwidth (>10 MBit/s) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution.

Neural timescales or lack thereof

This article aims at making readers, experimentalists and theorists, more aware of the abstractions made by an observer when measuring and reporting behavioral and neural timescales. These abstractions stow away the fact that, above lower boundaries that reflect fairly well understood physical constraints, observed and reported timescales are often not intrinsic to the biological system; rather, in most cases they reflect conditions that are imposed by the observer through the measuring procedure. This is true at practically every level of organization, from behavior down to molecules. I analyze the impacts of the resulting temporal manifold in behavioral and brain sciences on experimental designs and mathematical modeling.

On the precarious path of reverse neuro-engineering

In this perspective we provide an example for the limits of reverse engineering in neuroscience. We demonstrate that application of reverse engineering to the study of the design principle of a functional neuro-system with a known mechanism, may result in a perfectly valid but wrong induction of the system's design principle. If in the very simple setup we bring here (static environment, primitive task and practically unlimited access to every piece of relevant information), it is difficult to induce a design principle, what are our chances of exposing biological design principles when more realistic conditions are examined? Implications to the way we do Biology are discussed.

Adaptive transition rates in excitable membranes

Adaptation of activity in excitable membranes occurs over a wide range of timescales. Standard computational approaches handle this wide temporal range in terms of multiple states and related reaction rates emanating from the complexity of ionic channels. The study described here takes a different (perhaps complementary) approach, by interpreting ion channel kinetics in terms of population dynamics. I show that adaptation in excitable membranes is reducible to a simple Logistic-like equation in which the essential non-linearity is replaced by a feedback loop between the history of activation and an adaptive transition rate that is sensitive to a single dimension of the space of inactive states. This physiologically measurable dimension contributes to the stability of the system and serves as a powerful modulator of input–output relations that depends on the patterns of prior activity; an intrinsic scale free mechanism for cellular adaptation that emerges from the microscopic biophysical properties of ion channels of excitable membranes.

Order-based representation in random networks of cortical neurons

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.

Selective adaptation in networks of heterogeneous populations: model, simulation, and experiment

Biological systems often change their responsiveness when subject to persistent stimulation, a phenomenon termed adaptation. In neural systems, this process is often selective, allowing the system to adapt to one stimulus while preserving its sensitivity to another. In some studies, it has been shown that adaptation to a frequent stimulus increases the system's sensitivity to rare stimuli. These phenomena were explained in previous work as a result of complex interactions between the various subpopulations of the network. A formal description and analysis of neuronal systems, however, is hindered by the network's heterogeneity and by the multitude of processes taking place at different time-scales. Viewing neural networks as populations of interacting elements, we develop a framework that facilitates a formal analysis of complex, structured, heterogeneous networks. The formulation developed is based on an analysis of the availability of activity dependent resources, and their effects on network responsiveness. This approach offers a simple mechanistic explanation for selective adaptation, and leads to several predictions that were corroborated in both computer simulations and in cultures of cortical neurons developing in vitro. The framework is sufficiently general to apply to different biological systems, and was demonstrated in two different cases.

Our mind continuously adapts to background sensory events while preserving and even enhancing its sensitivity to deviant objects. In the visual modality, for instance, a target violating a surrounding pattern is easily detected, a phenomenon termed “pop-out.” Indeed, the automatic attention we pay to the irregular or the surprising, which developed as a valuable aid in our survival, is often used to advantage nowadays in popular culture and advertisement. Such phenomena have been investigated in many systems, from psychophysics in behaving animals to experiments in neural networks developing in vitro. In this work, we develop a mechanistic model that demonstrates how a relatively simple system may express such selective behavior. We apply our model first to the case of transiently responding networks, and compare the results with computer simulations and experimental data collected from neural networks developing in vitro. We also demonstrate the application of the model to other systems. Our approach provides insight as to how complex, behavior-related phenomena may arise from simple dynamic interactions between the system's elementary components.

Cell therapy for modification of the myocardial electrophysiological substrate

BACKGROUND

Traditional antiarrhythmic pharmacological therapies are limited by their global cardiac action, low efficacy, and significant proarrhythmic effects. We present a novel approach for the modification of the myocardial electrophysiological substrate using cell grafts genetically engineered to express specific ionic channels.

METHODS AND RESULTS

To test the aforementioned concept, we performed ex vivo, in vivo, and computer simulation studies to determine the ability of fibroblasts transfected to express the voltage-sensitive potassium channel Kv1.3 to modify the local myocardial excitable properties. Coculturing of the transfected fibroblasts with neonatal rat ventricular myocyte cultures resulted in a significant reduction (68%) in the spontaneous beating frequency of the cultures compared with baseline values and cocultures seeded with naive fibroblasts. In vivo grafting of the transfected fibroblasts in the rat ventricular myocardium significantly prolonged the local effective refractory period from an initial value of 84+/-8 ms (cycle length, 200 ms) to 154+/-13 ms (P<0.01). Margatoxin partially reversed this effect (effective refractory period, 117+/-8 ms; P<0.01). In contrast, effective refractory period did not change in nontransplanted sites (86+/-7 ms) and was only mildly increased in the animals injected with wild-type fibroblasts (73+/-5 to 88+/-4 ms; P<0.05). Similar effective refractory period prolongation also was found during slower pacing drives (cycle length, 350 to 500 ms) after transplantation of the potassium channels expressing fibroblasts (Kv1.3 and Kir2.1) in pigs. Computer modeling studies confirmed the in vivo results.

CONCLUSIONS

Genetically engineered cell grafts, transfected to express potassium channels, can couple with host cardiomyocytes and alter the local myocardial electrophysiological properties by reducing cardiac automaticity and prolonging refractoriness.

Dynamics and effective topology underlying synchronization in networks of cortical neurons

Cognitive processes depend on synchronization and propagation of electrical activity within and between neuronal assemblies. In vivo measurements show that the size of individual assemblies depends on their function and varies considerably, but the timescale of assembly activation is in the range of 0.1-0.2 s and is primarily independent of assembly size. Here we use an in vitro experimental model of cortical assemblies to characterize the process underlying the timescale of synchronization, its relationship to the effective topology of connectivity within an assembly, and its impact on propagation of activity within and between assemblies. We show that the basic mode of assembly activation, "network spike," is a threshold-governed, synchronized population event of 0.1-0.2 s duration and follows the logistics of neuronal recruitment in an effectively scale-free connected network. Accordingly, the sequence of neuronal activation within a network spike is nonrandom and hierarchical; a small subset of neurons is consistently recruited tens of milliseconds before others. Theory predicts that scale-free topology allows for synchronization time that does not increase markedly with network size; our experiments with networks of different densities support this prediction. The activity of early-to-fire neurons reliably forecasts an upcoming network spike and provides means for expedited propagation between assemblies. We demonstrate this capacity by observing the dynamics of two artificially coupled assemblies in vitro, using neuronal activity of one as a trigger for electrical stimulation of the other.

Learning in ex-vivo developing networks of cortical neurons

This contribution describes the use of multi-site interaction with large cortical networks in the study of learning. The general physiological properties of the network are described, and the concept of learning is mapped to the experimental network preparation. Learning is then analyzed in terms of exploration (defined as changes in the configuration of associations within the biological network) and recognition (the stabilization of "worthy" associations).

Dopamine-Induced Dispersion of Correlations Between Action Potentials in Networks of Cortical Neurons

The involvement of dopamine in the process of learning, at the cellular and behavioral levels, has been studied extensively. Evidently, dopamine is released from midbrain nuclei neurons on exposure to salient unpredicted stimuli and binds to neurons of cortical and subcortical structures, where its neuromodulatory effects are exerted. The neuromodulatory effects of dopamine at the synaptic and cellular levels are very rich, but it is difficult to extrapolate from these elementary levels what their effect might be at the behaviorally relevant level of neuronal ensembles. Using multi-site recordings from networks of cortical neurons developing ex vivo, we studied the effects of dopamine on connectivity within neuronal ensembles. We found that dopamine disperses correlations between individual neuronal activities while preserving the global distribution of correlations at the network level. Using selective D1 and D2 modulators, we show that both receptor types are contributing to dopamine-induced dispersion. Our results indicate that, at the neuronal ensemble level, dopamine acts to enhance changes in network connectivity rather than stabilize such connections.

Cardiac memory and cortical memory: do learning patterns in neural networks impact on cardiac arrhythmias?

Memory is a property of diverse biological systems, including brain and heart. Studies in cortical neuronal networks have identified an increased sensitivity to infrequent (rare) stimulation patterns that can result in their achieving dominance over network firing. This adaptive behavior is applied to the heart in an attempt to explain the ability of pulmonary venous and other ectopic foci to achieve dominance over cardiac rhythm. Developmental changes in determinants of cardiac rhythm are explored as possible determinants of the range of rhythms expressed by the heart. By understanding the mechanisms for these behavior patterns, we may obtain new means for manipulating memory to return dysrhythmic hearts to normal sinus rhythm.

Selective adaptation in networks of cortical neurons

A key property of neural systems is their ability to adapt selectively to stimuli with different features. Using multisite electrical recordings from networks of cortical neurons developing ex vivo, we show that neurons adapt selectively to different stimuli invading the network. We focus on selective adaptation to frequent and rare stimuli; networks were stimulated at two sites with two different stimulus frequencies. When both stimuli were presented within the same period, neurons in the network attenuated their responsiveness to the more frequent input, whereas their responsiveness to the rarely delivered stimuli showed a marked average increase. The amplification of the response to rare stimuli required the presence of the other, more frequent stimulation source. By contrast, the decreased response to the frequent stimuli occurred regardless of the presence of the rare stimuli. Analysis of the response of single units suggests that both of these effects are caused by changes in synaptic transmission. By using synaptic blockers, we find that the increased responsiveness to the rarely stimulated site depends specifically on fast GABAergic transmission. Thus, excitatory synaptic depression, the inhibitory sub-network, and their balance play an active role in generating selective gain control. The observation that selective adaptation arises naturally in a network of cortical neurons developing ex vivo indicates that this is an inherent feature of spontaneously organizing cortical networks.

Modeling the process of rate selection in neuronal activity

We present the elements of a mathematical computational model that reflects the experimental finding that the time-scale of a neuron is not fixed; but rather varies with the history of its stimulus. Unlike most physiological models, there are no pre-determined rates associated with transitions between states of the system nor are there pre-determined constants associated with adaptation rates; instead, the model is a kind of "modulating automata" where the rates emerge from the history of the system itself. We focus in this paper on the temporal dynamics of a neuron and show how a simple internal structure will give rise to complex temporal behavior. The internal structure modeled here is an abstraction of a reasonably well-understood physiological structure. We also suggest that this behavior can be used to transform a "rate" code into a "temporal one".

Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy

1. Introduction 63

1.1 Outline 63

1.2 Universals versus realizations in the study of learning and memory 64

2. Large random cortical networks developing ex vivo 65

2.1 Preparation 65

2.2 Measuring electrical activity 67

3. Spontaneous development 69

3.1 Activity 69

3.2 Connectivity 70

4. Consequences of spontaneous activity: pharmacological manipulations 72

4.1 Structural consequences 72

4.2 Functional consequences 73

5. Effects of stimulation 74

5.1 Response to focal stimulation 74

5.2 Stimulation-induced changes in connectivity 74

6. Embedding functionality in real neural networks 77

6.1 Facing the physiological definition of ‘reward’: two classes of theories 78

6.2 Closing the loop 79

7. Concluding remarks 84

8. Acknowledgments 85

9. References 85

The phenomena of learning and memory are inherent to neural systems that differ from each other markedly. The differences, at the molecular, cellular and anatomical levels, reflect the wealth of possible instantiations of two neural learning and memory universals: (i) an extensive functional connectivity that enables a large repertoire of possible responses to stimuli; and (ii) sensitivity of the functional connectivity to activity, allowing for selection of adaptive responses. These universals can now be fully realized in ex-vivo developing neuronal networks due to advances in multi-electrode recording techniques and desktop computing. Applied to the study of ex-vivo networks of neurons, these approaches provide a unique view into learning and memory in networks, over a wide range of spatio-temporal scales. In this review, we summarize experimental data obtained from large random developing ex-vivo cortical networks. We describe how these networks are prepared, their structure, stages of functional development, and the forms of spontaneous activity they exhibit (Sections 2–4). In Section 5 we describe studies that seek to characterize the rules of activity-dependent changes in neural ensembles and their relation to monosynaptic rules. In Section 6, we demonstrate that it is possible to embed functionality into ex-vivo networks, that is, to teach them to perform desired firing patterns in both time and space. This requires ‘closing a loop’ between the network and the environment. Section 7 emphasizes the potential of ex-vivo developing cortical networks in the study of neural learning and memory universals. This may be achieved by combining closed loop experiments and ensemble-defined rules of activity-dependent change.

Electrophysiological modulation of cardiomyocytic tissue by transfected fibroblasts expressing potassium channels: a novel strategy to manipulate excitability

BACKGROUND

Traditional pharmacological therapies aiming to modify the abnormal electrophysiological substrate underlying cardiac arrhythmias may be limited by their relatively low efficacy, global cardiac activity, and significant proarrhythmic effects. We suggest a new approach, in which transfected cellular grafts expressing various ionic channels may be used to manipulate the local electrophysiological properties of cardiac tissue. To examine the feasibility of this concept, we tested the hypothesis that transfected fibroblasts expressing the voltage-sensitive potassium channel Kv1.3 can modify the electrophysiological properties of cardiomyocytic cultures.

METHODS AND RESULTS

A high-resolution multielectrode mapping technique was used to assess the electrophysiological and structural properties of primary cultures of neonatal rat ventricular myocytes. The transfected fibroblasts, added to the cardiomyocytic cultures, caused a significant effect on the conduction properties of the hybrid cultures. These changes were manifested by significant reduction in extracellular signal amplitude and by the appearance of multiple local conduction blocks. The location of all conduction blocks correlated with the spatial distribution of the transfected fibroblasts assessed by vital staining. All electrophysiological changes were reversed after the application of Charybdotoxin, a specific Kv1.3 blocker. In contrast, conduction remained uniform in the control hybrid cultures when nontransfected fibroblasts were used.

CONCLUSIONS

Transfected fibroblasts are able to electrically couple with cardiac myocytes, causing a significant local and reversible modification of the tissue's electrophysiological properties. More broadly, this study suggests that transfected cellular grafts expressing various ionic channels may be used to modify cardiac excitability, providing a possible future novel cell therapy strategy.

Social phobia subtyping with the MMPI-2

The present article examines the utility of the MMPI-2 for the subtyping of social phobia (SP). Cluster analysis was conducted on the MMPI-2 profiles of 109 patients with SP. Clusters were compared on demographic and clinical variables prior to treatment, as well as following completion of cognitive-behavioral group therapy (CBGT). Three distinct clusters emerged. The first is characterized by an absence of significant scale elevations and appears to be consistent with the reported "circumscribed" subtype of SP. It is associated with a significantly later age at onset of SP, a higher proportion of married individuals, and lower scores on pretreatment clinical variables. Significant elevations on Scales 2 (Depression) and 7 (Psychasthenia) and moderately high scores on pretreatment clinical variables characterize the second cluster. The third cluster is characterized by significantly high elevations on Scales 8, 7, and 2, and the highest scores on pretreatment clinical variables. Patients from all three groups improved significantly following a course of CBGT.

Learning in networks of cortical neurons

The results presented here demonstrate selective learning in a network of real cortical neurons. We focally stimulate the network at a low frequency (0.3-1 Hz) until a desired predefined response is observed 50 +/- 10 msec after a stimulus, at which point the stimulus is stopped for 5 min. Repeated cycles of this procedure ultimately lead to the desired response being directly elicited by the stimulus. By plotting the number of stimuli required to achieve the target response in each cycle, we are able to generate learning curves. Presumably, the repetitive stimulation is driving changes in the circuit, and we are selecting for changes consistent with the predefined desired response. To the best of our knowledge, this is the first time learning of arbitrarily chosen tasks, in networks composed of real cortical neurons, is demonstrated outside of the body.

Frequency tuning of input-output relation in a rat cortical neuron in-vitro

The input-output relation of a single neuron stands at the basis of every biologically oriented description of the brain. This report shows that the input-output relation of cultured cortical neurons is non-linearly tuned by the input frequency. Increasing the rate of stimulation results in the appearance of ordered temporal firing patterns, which are qualitatively different for different input frequencies. The experimental results of this study lead to the conclusion that frequency tuning of neuronal input-output relation arises from activity-dependent rates at the molecular level underlying the mechanism of excitability itself.

A global defect in scaling relationship between electrical activity and availability of muscle sodium channels in hyperkalemic periodic paralysis

Hyperkalemic periodic paralysis (HyperPP) is a hereditary disorder characterized by alternate episodic attacks of muscle weakness and muscle myotonia. The most common mutation associated with HyperPP is a T704M substitution in the skeletal-muscle sodium channel. This mutation increases sodium persistent currents, alters voltage dependence of activation and impairs slow inactivation. The present study shows experimental evidence in support of a potentially important global defect caused by the T704M mutation. While the effective rate of recovery from slow inactivation, in both normal and mutated channels, is related to the duration of past activity by a power law function, the scaling power of the mutated channel is significantly greater. This difference between the channels offers a clue for an explanation to the wide range of time scales, history dependence, and the mixed myotonic/paralysis effect, which mark the clinical picture of HyperPP.

Interaction between duration of activity and time course of recovery from slow inactivation in mammalian brain Na+ channels

NaII and NaIIA channels are the most abundant voltage-gated channels in neonatal and adult cortex, respectively. The relationships between activity and availability for activation of these channels were examined using the Xenopus expression system. The main point of this work is that the time constant (tau) of recovery from the unavailable (inactivated) pool is related to the duration (t) of previous activation by a power law: tau(t) = p . tD, with a scaling power D congruent to 0.8 and 0.5 for NaII and NaIIA, respectively, and p as a constant kinetic setpoint. These relationships extend from tens of milliseconds to several minutes and are intrinsic to the channel protein. Coexpression of beta1 auxiliary subunit, together with the alpha subunit of the NaIIA channel, modulates the constant kinetic setpoint but not the scaling power of the latter. The power law scaling between activity and availability is not a universal property of ion channels; unlike that of voltage-gated sodium channels, the rate of recovery from slow inactivation of the ShakerB channel is virtually insensitive to the duration of previous stimuli. It is suggested that the power law scaling described here can act as a molecular memory mechanism that preserves traces of previous activity, over a wide range of time scales, in the form of modulated reaction rates. This mechanism should be considered when theorizing about the dynamics of threshold and firing patterns of neurons.

Effects of density and gating of delayed-rectifier potassium channels on resting membrane potential and its fluctuations

The aim of this study is to evaluate directly, using a reduced experimental system, the nature of interactions between voltage-gated potassium channels and the resting membrane potential. Xenopus oocytes were injected with various concentrations of cRNA coding for a delayed-rectifier potassium channel Shaker-IR. The effects of the density and kinetics of the expressed channels on resting membrane potential is explored in isolated ("inside-out") patches. The channel density is given in terms of maximal conductance (Gmax), measured from the maximal slope of the I-V curve under voltage clamp conditions. The capacitance of the experimental setup is approximately 1 pF. At high channel densities (Gmax > 10 pA/mV) the mean membrane potential is stabilized at approximately -60 mV. This resting membrane potential is more than 35 mV positive to the reversal potential for potassium ions under the same experimental conditions. Analyses of voltage clamp experiments indicate that at high channel densities the mean membrane potential is determined by the rates of channel activation and deactivation, but is not affected by the rates involved in the process of slow (C-type) inactivation. In contrast, at lower channel densities membrane potential is very unstable, and its mean value and amplitude of fluctuations are strongly affected by the process of slow (C-type) inactivation.

Intracellular and extracellular amino acids that influence C-type inactivation and its modulation in a voltage-dependent potassium channel

The rate of C-type inactivation of the cloned voltage-gated potassium channel, Kv1.3, measured in membrane patches from Xenopus oocytes, increases when the patch is detached from the cell; the structural basis for this on-cell/off-cell change was examined. First, four serine and threonine residues, that are putative sites for phosphorylation by protein kinases A and C, were mutated to alanines. Mutating any one of these residues, or two or three of them simultaneously, does not eliminate the change in C-type inactivation. However, the basal rate of C-type inactivation in the cell-attached patch is markedly slower in the triple phosphorylation site mutant. Second, a homologous potassium channel, Kv 1.6, does not exhibit the on-cell/off-cell change. When an extracellular histidine at position 401 of Kv1.3 is replaced with tyrosine, the residue at the equivalent position (430) in Kv1.6, the resulting Kv1.3 H401Y mutant channel does not undergo the on-cell/off-cell change. The results indicate that several potentially phosphorylatable intracellular amino acids influence the basal rate of C-type inactivation, but are not essential for the on-cell/off-cell change in inactivation kinetics. In contrast, an extracellular amino acid is critical for this on-cell/off-cell change.

Rich dynamics in a simplified excitable system

The study aims at exploring effects of microscopic channel fluctuations on macroscopic dynamics of excitable systems. Molecular biology techniques are used in order to construct a minimal excitable system that is built of cloned channels embedded in a small (approximately 1 micron 2) isolated patch of membrane. This simple synthetic "point" system exhibits dynamics in time scales that are several orders of magnitude longer then a single spike.

State-dependent inactivation of the Kv3 potassium channel

Inactivation of Kv3 (Kv1.3) delayed rectifier potassium channels was studied in the Xenopus oocyte expression system. These channels inactivate slowly during a long depolarizing pulse. In addition, inactivation accumulates in response to a series of short depolarizing pulses (cumulative inactivation), although no significant inactivation occurs within each short pulse. The extent of cumulative inactivation does not depend on the voltage during the depolarizing pulse, but it does vary in a biphasic manner as a function of the interpulse duration. Furthermore, the rate of cumulative inactivation is influenced by changing the rate of deactivation. These data are consistent with a model in which Kv3 channel inactivation is a state-dependent and voltage-independent process. Macroscopic and single channel experiments indicate that inactivation can occur from a closed (silent) state before channel opening. That is, channels need not open to inactivate. The transition that leads to the inactivated state from the silent state is, in fact, severalfold faster then the observed inactivation of current during long depolarizing pulses. Long pulse-induced inactivation appears to be slow, because its rate is limited by the probability that channels are in the open state, rather than in the silent state from which they can inactivate. External potassium and external calcium ions alter the rates of cumulative and long pulse-induced inactivation, suggesting that antagonistic potassium and calcium binding steps are involved in the normal gating of the channel.

Modeling state-dependent inactivation of membrane currents

Inactivation of many ion channels occurs through largely voltage-independent transitions to an inactivated state from the open state or from other states in the pathway leading to opening of the channel. Because this form of inactivation is state-dependent rather than voltage-dependent, it cannot be described by the standard Hodgkin-Huxley formalism used in virtually all modeling studies of neuronal behavior. Using two examples, cumulative inactivation of the Kv3 potassium channel and inactivation of the fast sodium channel, we extend the standard formalism for modeling macroscopic membrane currents to account for state-dependent inactivation. Our results provide an accurate description of cumulative inactivation of the Kv3 channel, new insight into inactivation of the sodium channel, and a general framework for modeling macroscopic currents when state-dependent processes are involved. In a model neuron, the macroscopic Kv3 current produces a novel short-term memory effect and firing delays similar to those seen in hippocampal neurons.

Immunological rejection of heart transplant: how lytic granules from cytotoxic T lymphocytes damage guinea pig ventricular myocytes

We investigated the mechanism by which lytic granules extracted from cytotoxic T lymphocytes (CTL) damage guinea pig ventricular myocytes in order to determine whether their actions can be related to the overall immunological rejection of the transplanted heart. Granule-induced myocyte morphological changes and final destruction were preceded by shortening of action potential duration (APD) and reductions of the resting potential and the action potential amplitude. APD shortening was probably caused by a granule-induced increase in outward current (most likely non-specific). Ryanodine, which blocks Ca2+ release from the sarcoplasmic reticulum, did not interfere with the morphological and electrophysiological effects of lytic granules. Fura-2 imaging indicated that [Ca2+]i initially increased about 2-fold from 90.0 +/- 11.5 nM, while cell length decreased less than 5% from a mean value of 99.0 +/- 9.0 microns. A further increase in [Ca2+]i (greater than 10 fold) was associated with progressive contracture and destruction, suggesting that the structural damage inflicted by lytic granules is caused by [Ca2+]i overload. The results indicate that the cytocidal action of CTL-derived lytic granules may be involved in immunologically induced damage, even to the extent of rejection of the transplanted heart.

Single-Channel Techniques Applied to Kidney Proximal Tubule Apical Membranes

New electrophysiological methods, related to single-channel recording techniques that enable investigation of the molecular components participating in transport mechanisms, have been developed in the last decade. Applications of these methods to renal physiology indicate possible implications for present concepts of proximal reabsorption.

Brush-border membrane cation conducting channels from rat kidney proximal tubules

This is a description and kinetic characterization of cation channels from rat kidney brush-border membrane vesicles and from apical membranes of proximal tubule cells in culture. Channel activity was demonstrated and characterized in both artificial phospholipid bilayers and in tissue culture. Intermediate conductance, approximately 50 pS, cation-selective channels were observed by both methods. Channels were characterized by a Na permeability (PNa)/K permeability (PK) of 1-5:1. Open-channel current-voltage curves were linear in symmetric 300 mM NaCl. In tissue culture the gating kinetics are described by two open-time constants and two closed-time constants. Channel activity was neither voltage nor Ca2+ dependent and the probability of being in the open state ranged from 0.6 to 0.95. In tissue culture experiments the channel demonstrated nonstationary gating activity. A second, 15-pS cation channel, seen in planar bilayers, demonstrated a higher selectivity for Na+ with a (PNa/PK ratio of greater than 10).