Spike Synchronization Research Articles

Inhibitory coherence in a heterogeneous population of subthreshold and suprathreshold type-I neurons

We study inhibitory coherence (i.e. collective coherence by synaptic inhibition) in a population of globally coupled type-I neurons, which can fire at arbitrarily low frequency. No inhibitory coherence is observed in a homogeneous population composed of only subthreshold neurons, which exhibit noise-induced firings. In addition to subthreshold neurons, there exist spontaneously firing suprathreshold neurons in a noisy environment of a real brain. To take into consideration the effect of suprathreshold neurons on inhibitory coherence, we consider a heterogeneous population of subthreshold and suprathreshold neurons and investigate the inhibitory coherence by increasing the fraction of suprathreshold neurons Psupra. As Psupra passes a threshold P*supra, suprathreshold neurons begin to synchronize and play the role of coherent inhibitors for the emergence of inhibitory coherence. Thus, regularly oscillating population-averaged global potential appears for Psupra > P*supra. For this coherent case, suprathreshold neurons exhibit sparse spike synchronization (i.e. individual potentials of suprathreshold neurons consist of coherent sparse spikings and coherent subthreshold small-amplitude hoppings). By virtue of their coherent inhibition, sparsely synchronized suprathreshold neurons suppress the noisy activity of subthreshold neurons. Thus, subthreshold neurons exhibit hopping synchronization (i.e. only coherent subthreshold hopping oscillations without spikings appear in the individual potentials of subthreshold neurons). We also characterize the inhibitory coherence in terms of the ‘statistical-mechanical’ spike-based and correlation-based measures, which quantify the average contributions of the microscopic individual spikes and individual potentials to the macroscopic global potential. Finally, the effect of sparse randomness of synaptic connectivity on the inhibitory coherence is briefly discussed.

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.

Collective behavior of interacting locally synchronized oscillations in neuronal networks

Local circuits in the cortex and hippocampus are endowed with resonant, oscillatory firing properties which underlie oscillations in various frequency ranges (e.g. gamma range) frequently observed in the local field potentials, and in electroencephalography. Synchronized oscillations are thought to play important roles in information binding in the brain. This paper addresses the collective behavior of interacting locally synchronized oscillations in realistic neural networks. A network of five neurons is proposed in order to produce locally synchronized oscillations. The neuron models are Hindmarsh–Rose type with electrical and/or chemical couplings. We construct large-scale models using networks of such units which capture the essential features of the dynamics of cells and their connectivity patterns. The profile of the spike synchronization is then investigated considering different model parameters such as strength and ratio of excitatory/inhibitory connections. We also show that transmission time-delay might enhance the spike synchrony. The influence of spike-timing-dependence-plasticity is also studies on the spike synchronization.

Network properties of a computational model of the dorsal raphe nucleus

Serotonin (5-HT) plays an important role in regulating mood, cognition and behaviour. The midbrain dorsal raphe nucleus (DRN) is one of the primary sources of 5-HT. Recent studies show that DRN neuronal activities can encode rewarding (e.g., appetitive) and unrewarding (e.g., aversive) behaviours. Experiments have also shown that DRN neurons can exhibit heterogeneous spiking behaviours. In this work, we build and study a basic spiking neuronal network model of the DRN constrained by neuronal properties observed in experiments. We use an efficient adaptive quadratic integrate-and-fire neuronal model to capture slow afterhyperpolarization current, occasional bursting behaviours in 5-HT neurons, and fast spiking activities in the non-5-HT inhibitory neurons. Provided that our noisy and heterogeneous spiking neuronal network model adopts a feedforward inhibitory network architecture, it is able to replicate the main features of DRN neuronal activities recorded in monkeys performing a reward-based memory-guided saccade task. The model exhibits theta band oscillation, especially among the non-5-HT inhibitory neurons during the rewarding outcome of a simulated trial, thus forming a model prediction. By varying the inhibitory synaptic strengths and the afferent inputs, we find that the network model can oscillate over a range of relatively low frequencies, allow co-existence of multiple stable frequencies, and spike synchrony can spread from within a local neural subgroup to global. Our model suggests plausible network architecture, provides interesting model predictions that can be experimentally tested, and offers a sufficiently realistic multi-scale model for 5-HT neuromodulation simulations.

Auditory neuropathies

For lack of therapy targeting sensorineural hearing loss, hearing-impaired patients must be fitted with sound-amplifying hearing aids or cochlear implants, successfully in a majority of cases. Yet failures are often found among auditory neuropathies. Auditory neuropathies are a class of conditions characterized by disrupted spike synchrony in auditory pathways despite reasonably preserved hearing sensitivity: amplification by hearing aids is inadequate and electrical stimulation of the auditory nerve may not improve discharge synchrony. Among the already partially understood pathogenetic frameworks, this article reviews physiological reasons why some rehabilitation procedures can restore neural synchrony, whereas others either fail or might even increase the damage, and what tests could help predict the outcome of intervention.

Amplitude Dependency of Synchrony Codes

Event Abstract Back to Event Amplitude Dependency of Synchrony Codes Jan Grewe1*, Nahal Sharafi2, Henriette Walz1, Benjamin Lindner2, 3 and Jan Benda1, 4 1 Ludwig-Maximilians-Universität München, Department Biologie II, Germany 2 Max Planck Institut für Physik komplexer Systeme, Germany 3 Humboldt Universität zu Berlin, Theory of Complex Systems and Neurophysics, Germany 4 Eberhard Karls Universität Tübingen, Institut für Neurobiologie, Germany Synchronous activity among neurons in a population is usually associated with the occurrence of specific features in the common stimulus. We investigate the synchrony code in the context of the encoding of electrosensory information in the weakly electric fish Apteronotus leptorhynchus. Middleton et al., 2009 showed that the extraction of synchronous spikes from a population of input neurons shifts the range of encoded signals to higher frequencies and thus can be understood as a way to separarte coding channels. In the electrosensory world this separation roughly matches the frequency ranges used for the two purposes of active electrosensation, i.e. navigation and prey detection, in the lower range, and communication in the high frequency range. Here, we compare experimental results from in vivo recordings of p-unit electroreceptors with predictions from linear response theory in the limit of weak stimuli. Analytical expressions are derived for the coherence of both the synchronous spikes, as a coincidence detector would read them out, and the integrated spike trains. The theoretical results show how the synchrony code depends on stimulus amplitude and the power-spectrum of baseline firing. Theory predicts that any cell that has a regular baseline firing rate and contains a certain amount of intrinsic noise can employ a synchrony code and thus separate coding channels. Furthermore the separation of the frequency bands of integrated and synchronous responses dependes on the stimulus intensity. Weak stimuli lead to a clear separation that becomes weaker with increasing stimulus intensity. Our experimental results from the electrosensory systems of the weakly electric fish support these predictions. In irregular spiking P-units of the active electrosensory system the observed separation of the coding channels indeed shows the predicted stimulus dependency. On the other hand, ampullary receptors of the passive electrosensory system exhibit a much more regular baseline firing rate than those of the active system. Here the coding ranges of the synchronous and integrated responses are not shifted relative to each other. Thus, the receptors of the passive system do not have a sufficient level of intrinsic noise, although a distinction of synchronous and non-synchronous spikes is still possible. Again, these results are in line with the predictions of the theory. References Middleton, JW., Longtin, A., Benda, J., and Maler, L. (2009). Postsynaptic Receptive Field Size and Spike Threshold Determine Encoding of High-Frequency Information Via Sensitivity to Synchronous Presynaptic Activity. J. Neurophysiol., 101:(3) 1160-1170 Keywords: electrosensory system, population coding, synchrony code Conference: Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012. Presentation Type: Poster Topic: Sensory processing and perception Citation: Grewe J, Sharafi N, Walz H, Lindner B and Benda J (2012). Amplitude Dependency of Synchrony Codes. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. doi: 10.3389/conf.fncom.2012.55.00110 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 May 2012; Published Online: 12 Sep 2012. * Correspondence: Dr. Jan Grewe, Ludwig-Maximilians-Universität München, Department Biologie II, Planegg-Martinsried, 82152, Germany, jan.grewe@uni-tuebingen.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jan Grewe Nahal Sharafi Henriette Walz Benjamin Lindner Jan Benda Google Jan Grewe Nahal Sharafi Henriette Walz Benjamin Lindner Jan Benda Google Scholar Jan Grewe Nahal Sharafi Henriette Walz Benjamin Lindner Jan Benda PubMed Jan Grewe Nahal Sharafi Henriette Walz Benjamin Lindner Jan Benda Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony?

Spike synchronization is thought to have a constructive role for feature integration, attention, associative learning, and the formation of bidirectionally connected Hebbian cell assemblies. By contrast, theoretical studies on spike-timing-dependent plasticity (STDP) report an inherently decoupling influence of spike synchronization on synaptic connections of coactivated neurons. For example, bidirectional synaptic connections as found in cortical areas could be reproduced only by assuming realistic models of STDP and rate coding. We resolve this conflict by theoretical analysis and simulation of various simple and realistic STDP models that provide a more complete characterization of conditions when STDP leads to either coupling or decoupling of neurons firing in synchrony. In particular, we show that STDP consistently couples synchronized neurons if key model parameters are matched to physiological data: First, synaptic potentiation must be significantly stronger than synaptic depression for small (positive or negative) time lags between presynaptic and postsynaptic spikes. Second, spike synchronization must be sufficiently imprecise, for example, within a time window of 5–10 ms instead of 1 ms. Third, axonal propagation delays should not be much larger than dendritic delays. Under these assumptions synchronized neurons will be strongly coupled leading to a dominance of bidirectional synaptic connections even for simple STDP models and low mean firing rates at the level of spontaneous activity.

Brain at work: time, sparseness and superposition principles

Many studies explored mechanisms through which the brain encodes sensory inputs allowing a coherent behavior. The brain could identify stimuli via a hierarchical stream of activity leading to a cardinal neuron responsive to one particular object. The opportunity to record from numerous neurons offered investigators the capability of examining simultaneously the functioning of many cells. These approaches suggested encoding processes that are parallel rather than serial. Binding the many features of a stimulus may be accomplished through an induced synchronization of cell's action potentials. These interpretations are supported by experimental data and offer many advantages but also several shortcomings. We argue for a coding mechanism based on a sparse synchronization paradigm. We show that synchronization of spikes is a fast and efficient mode to encode the representation of objects based on feature bindings. We introduce the view that sparse synchronization coding presents an interesting venue in probing brain encoding mechanisms as it allows the functional establishment of multi-layered and time-conditioned neuronal networks or multislice networks. We propose a model based on integrate-and-fire spiking neurons.

Computational investigations of small-worlds networks in neuronal populations

Event Abstract Back to Event Computational investigations of small-worlds networks in neuronal populations Antonio G. Zippo1*, Giuliana Gelsomino1, Sara Nencini1, Gian Carlo Caramenti2, Maurizio Valente1 and Gabriele E. Biella1 1 National Research Council, Institute of Molecular Bioimaging and Physiology, Italy 2 National Research Council, Institute of Biomedical Technologies, Italy The study of the brain as neuronal network allows gaining insights to the analysis of information processing [1]. Network interactions can play a crucial role in this scenario. In fact many studies identify in the modality of neuronal interaction the key of the problem, but limitations in neuronal recordings makes the clear mechanisms elusive [2-4]. In this perspective, we investigated the functional organization of neuronal networks hypothesizing that they work as small-world networks [5]. We developed two different computational approaches: in the first, we asked whether neuronal populations actually express small-world properties during a learning task. To this purpose we developed the Inductive Conceptual Network (ICN), a hierarchical bio-inspired spiking network, able to learn invariant patterns by Variable-order Markov Models implemented in its nodes [6]. We found that the ICN model expressed small-word networks during learning. As control, we exerted the ICN with random binary inputs, where no patterns can be learnt, and we obtained no small-world network functional organization among nodes. Conjecturing that the expression of small-world networks is not only related to learning, in the second part, we built the de facto network assuming that every information process in brain occurs exhibiting functionally a small-world network. In the de facto network the functional dependencies of the small-world networks, were reflected by synchronous spikes. From the analysis of spiking activity, versus the null hypothesis where small-world networks were replaced by random networks, we detected mainly three functional characteristics, observed in biological networks: timing and rate codes, conventional coding strategies [7], and the neuronal avalanches, cascades of bursting activities distributed as a power-law distribution [8]. Interestingly, rate and timing codes are thus allowed to coexist in the same network model yet in nodes at different hierarchical positions. Our results suggest that small-word functional configurations represent a milestone of brain information processing at the level of neurons. In conclusion, accordingly with other theoretical and experimental works, short path length and sparse connectivity may promote simultaneous performance of information segregation (information retention) and integration (generalization) within neuronal systems. Acknowledgements We thank Riccardo Storchi and Jianyi Lin for his helpful suggestions.

Voltage Dependence of Calcium Spike Characteristics in Rat Cardiac Myocytes

Integral calcium release fluxes in mammalian cardiac myocytes increase in amplitude not only due to the increased recruitment of individual release sites but also due to their increased synchronization (1). Here we characterize the voltage dependence of synchronization of individual release sites at high temporal resolution. Calcium spikes and calcium currents elicited in isolated rat ventricular myocytes (2) by 80 ms voltage pulses from −50 to −40 - +50 mV were recorded using Leica TCS AOBS confocal microscope (2 kHz line scan mode) and Axopatch 200B patch-clamp amplifier, respectively. Calcium spikes were identified and analyzed by a new Matlab-based software that highly increased the productivity and reliability of approximation of calcium spikes by the kinetic model of local calcium release (2). Calcium spikes were described by their amplitude, onset latency, time-to-peak, FDHM, time constants of activation and termination, and fractional probability of activation. The synchrony of calcium spikes increased with depolarization and could be fully explained by the voltage dependence of their onset latency that reached a lower limit of about 4 ms at depolarizations between 0 and 40 mV. The rate of calcium spike activation after its onset was independent of the applied voltage or of calcium current amplitude, while the probability of calcium spike activation was related to the voltage dependence of the integral of calcium current. We conclude that both the synchrony of local calcium release activation and recruitment of individual release sites are fully controlled by the activation probability of DHPR calcium channels and by the DHPR-RYR coupling fidelity.Supported by APVV-0721-10, VEGA 2/0203/11, VEGA 2/0917/11.1. Song LS, Sham JS, Stern MD, Lakatta EG, Cheng H. J Physiol. 512:677-91, 1998.2. Zahradnikova A Jr, Polakova E, Zahradnik I, Zahradnikova A. J Physiol. 578:677-91, 2007.

Calcium Signaling in Myocytes of Injured Myocardium

Calcium signaling during cardiac excitation-contraction coupling is compromised after myocardial injury (MI). Myocytes of injured myocardium display reduced synchronization of calcium spikes at low calcium current density indicating worsened communication between sarcolemmal DHPR and sarcoplasmic reticulum RyR calcium channels [1]. Here we study the activation of Ca-spikes by using strong synchronized calcium triggers. Cardiac myocytes were isolated from left ventricles of young adult male Wistar rats on day 15 after administration of a single high dose of isoproterenol, to model the myocardial injury (MI), or a vehicle as the control, and studied by means of the whole cell patch clamp and confocal microscopy techniques. Ca-spikes were activated by a short prepulse to +60 mV from a holding potential of −50 mV and subsequent step repolarization to negative voltages that induced perfectly synchronized influx of Ca2+ ions via DHPR channels. Ca-spikes were recorded using 100 μM fluo-3 and 1 mM EGTA at 2 kHz line scan frequency. In this arrangement, increase of the calcium influx increased the probability of Ca-spike activation in the same manner in both the control and MI groups. The probability of local Ca2+ release was not different between the two groups. In the MI group, the amplitude of Ca-spikes was increased and the latency of spikes was abbreviated relative to controls. Both effects could be explained by increased sarcoplasmic reticulum Ca2+ load revealed by caffeine application. We conclude that the DHPR-RYR calcium coupling in MI myocytes functions well when the triggering calcium influx is strong, most likely due to sensitizing effect of increased sarcoplasmic reticulum Ca2+ load on RyR activation by calcium.Supported by grants APVV-0721-10 and VEGA 2/0197/11.

Mitral cell spike synchrony modulated by dendrodendritic synapse location

On their long lateral dendrites, mitral cells of the olfactory bulb form dendrodendritic synapses with large populations of granule cell interneurons. The mitral-granule cell microcircuit operating through these reciprocal synapses has been implicated in inducing synchrony between mitral cells. However, the specific mechanisms of mitral cell synchrony operating through this microcircuit are largely unknown and are complicated by the finding that distal inhibition on the lateral dendrites does not modulate mitral cell spikes. In order to gain insight into how this circuit synchronizes mitral cells within its spatial constraints, we built on a reduced circuit model of biophysically realistic multi-compartment mitral and granule cells to explore systematically the roles of dendrodendritic synapse location and mitral cell separation on synchrony. The simulations showed that mitral cells can synchronize when separated at arbitrary distances through a shared set of granule cells, but synchrony is optimally attained when shared granule cells form two balanced subsets, each subset clustered near to a soma of the mitral cell pairs. Another constraint for synchrony is that the input magnitude must be balanced. When adjusting the input magnitude driving a particular mitral cell relative to another, the mitral-granule cell circuit served to normalize spike rates of the mitral cells while inducing a phase shift or delay in the more weakly driven cell. This shift in phase is absent when the granule cells are removed from the circuit. Our results indicate that the specific distribution of dendrodendritic synaptic clusters is critical for optimal synchronization of mitral cell spikes in response to their odor input.

Entorhinal theta-frequency input to the dentate gyrus trisynaptically evokes hippocampal CA1 LTP

There exists substantial evidence that some forms of explicit learning in mammals require long-term potentiation (LTP) at hippocampal CA3-CA1 synapses. While CA1 LTP has been well characterized at the monosynaptic level, it still remains unclear how the afferent systems to the hippocampus can initiate formation of this neuroplastic phenomenon. Using voltage-sensitive dye imaging (VSDI) in a mouse brain slice preparation, we show that evoked entorhinal cortical (EC) theta-frequency input to the dentate gyrus highly effectively generates waves of neuronal activity which propagate through the entire trisynaptic circuit of the hippocampus (“HTC-Waves”). This flow of activity, which we also demonstrate in vivo, critically depends on frequency facilitation of mossy fiber to CA3 synaptic transmission. The HTC-Waves are rapidly boosted by the cognitive enhancer caffeine (5 μM) and the stress hormone corticosterone (100 nM). They precisely follow the rhythm of the EC input, involve high-frequency firing (>100 Hz) of CA3 pyramidal neurons, and induce NMDA receptor-dependent CA1 LTP within a few seconds. Our study provides the first experimental evidence that synchronous theta-rhythmical spiking of EC stellate cells, as occurring during EC theta oscillations, has the capacity to drive induction of CA1 LTP via the hippocampal trisynaptic pathway. Moreover, we present data pointing to a basic filter mechanism of the hippocampus regarding EC inputs and describe a methodology to reveal alterations in the “input–output relationship” of the hippocampal trisynaptic circuit.

State-Dependent Modulation of Gap Junction Signaling by the Persistent Sodium Current

Thalamic neurons fluctuate between two states: a hyperpolarized state associated with burst firing and sleep spindles, and a depolarized state associated with tonic firing and rapid, reliable information transmission between the sensory periphery and cortex. The thalamic reticular nucleus (TRN) plays a central role in thalamocortical processing by providing feed-forward and feedback inhibition to thalamic relay cells; TRN cells participate in the generation of sleep spindles, and have been suggested to focus the neural “searchlight” of attention. The mechanisms underlying synchrony in the TRN during different behavioral states are largely unknown. TRN cells are densely interconnected by electrical synapses. Here we show that activation of the persistent sodium current (INaP) by depolarization causes up to fourfold changes in electrical synaptic efficacy between TRN neurons. We further show that amplification of electrical synaptic responses strongly enhances tonic spike synchrony but, surprisingly, does not affect burst coordination. We use a Hodgkin–Huxley model to gain insight into the differences between the effects of burstlets, spikelets, and amplification on burst and spike times.

Brute force parameter fitting for a simulation of an in vitro neuronal network

Event Abstract Back to Event Brute force parameter fitting for a simulation of an in vitro neuronal network Kerstin Lenk1*, Kay Barthold1 and Barbara Priwitzer1 1 Lausitz University of Applied Sciences, Faculty of Engineering and Computer Science, Germany We developed a pulsing model called INEX [1] which simulates neuronal activity as observed in neuronal networks cultivated on multielectrode array (MEA) neurochips. In an in vitro experiment approximately 500,000 cells of the frontal cortex of embryonic mice are cultivated on such a MEA neurochip (experiments are carried out by NeuroProof GmbH, Rostock, Germany). Circa 10,000 neurons and 90,000 glia cells of the total amount survive. The INEX model is based on an inhomogeneous Poisson process [2] to simulate neurons which are active without external input or stimulus as observed in neurochip experiments. It is accomplished using an Ising model [3] with Glauber dynamics [4] and has four parameter types: basic activity c, the excitatory weights w_e of the synapses which have positive values, the inhibitory weights w_i of the synapses which have negative values and a factor f indicating the varying probability for the occurrence of spikes depending on the previous activity of the neuron. Using an automated fitting method, these four parameter types were chosen in the following way: First, the ranges for the four parameter types were set. Then we simulated twenty times a neuronal network with 800 excitatory and 200 inhibitory neurons with parameter type values in different intervals but within the range. For each spike train twelve activity describing features were calculated. The same features were calculated from the spike trains of twenty MEA neurochip experiments with cell cultures originated from frontal cortex of embryonic mice after 28 days in vitro. Per experiment 15 to 50 spike trains were available. The simulated and the experimental spike strains had a length of 30 minutes. The mean value and the standard deviation were calculated for all features. The parameter fitting was considered as successful if the calculated features of the simulated spike trains lay within the standard deviation of the MEA neurochip features. The parameter types are scalable. For the choice of the parameter range we developed a heuristic based on spike rates and burst rates observed in experiments and the dependency between the parameters. The described brute force parameter fitting was applied within the intervals according to the derived parameter dependency. Results were shown in several isoline 3D plots depending on the value of f with the mean basic activity of all 1,000 neurons on the x-axis, the mean inhibitory weights on the y-axis and the mean excitatory weights on the z-axis. The simulated spike trains show typical synchronous spike and burst patterns as known from MEA neurochip experiments with frontal cortex neurons. Calculated features adapted from spikes and bursts indicate that the presented model simulates neuronal activity similar to activity as observed on MEA neurochips. Acknowledgements We thank Matthias Reuter (Clausthal University of Technology, Germany), Lars Schwabe (University of Rostock, Germany) and last not least Olaf Schröder (NeuroProof GmbH, Rostock, Germany).

Purkinje neuron synchrony elicits time-locked spiking in the cerebellar nuclei

An unusual feature of the cerebellar cortex is that its output neurons, Purkinje cells, are GABAergic. Their high intrinsic firing rates1 (50 Hz) and extensive convergence2,3 predict that that target neurons in the cerebellar nuclei would be largely inhibited unless Purkinje cells pause their spiking, yet Purkinje and nuclear neuron firing rates do not always vary inversely4. A potential clue to how these synapses transmit information is that populations of Purkinje neurons synchronize their spikes during cerebellar behaviors5–11. If nuclear neurons respond to Purkinje synchrony, they may encode signals from subsets of inhibitory inputs7,12–14. Here we show in weanling and adult mice that nuclear neurons transmit the timing of synchronous Purkinje afferent spikes, owing to modest Purkinje-to-nuclear convergence ratios (~40:1), fast IPSC kinetics (τdecay=2.5 ms), and high intrinsic firing rates (~90 Hz). In vitro, dynamically clamped asynchronous IPSPs mimicking Purkinje afferents suppress nuclear cell spiking, whereas synchronous IPSPs entrain nuclear cell spiking. With partial synchrony, nuclear neurons time-lock their spikes to the synchronous subpopulation of inputs, even when only 2 of 40 afferents synchronize. In vivo, nuclear neurons reliably phase-lock to regular trains of molecular layer stimulation. Thus, cerebellar nuclear neurons can preferentially relay the spike timing of synchronized Purkinje cells to downstream premotor areas.

Computation Emerges from Adaptive Synchronization of Networking Neurons

The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.

Synergy from Silence in a Combinatorial Neural Code

The manner in which groups of neurons represent events in the external world is a central question in neuroscience. Estimation of the information encoded by small groups of neurons has shown that in many neural systems, cells carry mildly redundant information. These measures average over all the activity patterns of a neural population. Here, we analyze the population code of the salamander and guinea pig retinas by quantifying the information conveyed by specific multicell activity patterns. Synchronous spikes, even though they are relatively rare and highly informative, convey less information than the sum of either spike alone, making them redundant coding symbols. Instead, patterns of spiking in one cell and silence in others, which are relatively common and often overlooked as special coding symbols, were found to be mostly synergistic. Our results reflect that the mild average redundancy between ganglion cells that was previously reported is actually the result of redundant and synergistic multicell patterns, whose contributions partially cancel each other when taking the average over all patterns. We further show that similar coding properties emerge in a generic model of neural responses, suggesting that this form of combinatorial coding, in which specific compound patterns carry synergistic or redundant information, may exist in other neural circuits.

Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma

Spike-timing-dependent plasticity (STDP) modifies the weight (or strength) of synaptic connections between neurons and is considered to be crucial for generating network structure. It has been observed in physiology that, in addition to spike timing, the weight update also depends on the current value of the weight. The functional implications of this feature are still largely unclear. Additive STDP gives rise to strong competition among synapses, but due to the absence of weight dependence, it requires hard boundaries to secure the stability of weight dynamics. Multiplicative STDP with linear weight dependence for depression ensures stability, but it lacks sufficiently strong competition required to obtain a clear synaptic specialization. A solution to this stability-versus-function dilemma can be found with an intermediate parametrization between additive and multiplicative STDP. Here we propose a novel solution to the dilemma, named log-STDP, whose key feature is a sublinear weight dependence for depression. Due to its specific weight dependence, this new model can produce significantly broad weight distributions with no hard upper bound, similar to those recently observed in experiments. Log-STDP induces graded competition between synapses, such that synapses receiving stronger input correlations are pushed further in the tail of (very) large weights. Strong weights are functionally important to enhance the neuronal response to synchronous spike volleys. Depending on the input configuration, multiple groups of correlated synaptic inputs exhibit either winner-share-all or winner-take-all behavior. When the configuration of input correlations changes, individual synapses quickly and robustly readapt to represent the new configuration. We also demonstrate the advantages of log-STDP for generating a stable structure of strong weights in a recurrently connected network. These properties of log-STDP are compared with those of previous models. Through long-tail weight distributions, log-STDP achieves both stable dynamics for and robust competition of synapses, which are crucial for spike-based information processing.

Bistability induces episodic spike communication by inhibitory neurons in neuronal networks

Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.