Neuronal assemblies

Phasic and tonic patterns of locus coeruleus output differentially modulate sensory network function in the awake rat

Neurons of the nucleus locus coeruleus (LC) discharge with phasic bursts of activity superimposed on highly regular tonic discharge rates. Phasic bursts are elicited by bottom-up input mechanisms involving novel/salient sensory stimuli and top-down decision making processes; whereas tonic rates largely fluctuate according to arousal levels and behavioral states. Although it is generally believed that these two modes of activity differentially modulate information processing in LC targets, the unique role of phasic versus tonic LC output on signal processing in cells, circuits, and neural networks of waking animals is not well understood. In the current study, simultaneous recordings of individual neurons within ventral posterior medial thalamus and barrel field cortex of conscious rats provided evidence that each mode of LC output produces a unique modulatory impact on single neuron responsiveness to sensory-driven synaptic input and representations of sensory information across ensembles of simultaneously recorded cells. Each mode of LC activation specifically modulated the relationship between sensory-stimulus intensity and the subsequent responses of individual neurons and neural ensembles. Overall these results indicate that phasic versus tonic modes of LC discharge exert fundamentally different modulatory effects on target neuronal circuits within the rodent trigeminal somatosensory system. As such, each mode of LC output may differentially influence signal processing as a means of optimizing behaviorally relevant neural computations within this sensory network. Likely the ability of the LC system to differentially regulate neural responses and local circuit operations according to behavioral demands extends to other brain regions including those involved in higher cognitive functions.

Dynamics of stimulus-evoked spike timing correlations in the cat lateral geniculate nucleus

Precisely synchronized neuronal activity has been commonly observed in the mammalian visual pathway. Spike timing correlations in the lateral geniculate nucleus (LGN) often take the form of phase synchronized oscillations in the high gamma frequency range. To study the relations between oscillatory activity, synchrony, and their time-dependent properties, we recorded activity from multiple single units in the cat LGN under stimulation by stationary spots of light. Autocorrelation analysis showed that approximately one third of the cells exhibited oscillatory firing with a mean frequency ∼80 Hz. Cross-correlation analysis showed that 30% of unit pairs showed significant synchronization, and 61% of these pairs consisted of synchronous oscillations. Cross-correlation analysis assumes that synchronous firing is stationary and maintained throughout the period of stimulation. We tested this assumption by applying unitary events analysis (UEA). We found that UEA was more sensitive to weak and transient synchrony than cross-correlation analysis and detected a higher incidence (49% of cell pairs) of significant synchrony (unitary events). In many unit pairs, the unitary events were optimally characterized at a bin width of 1 ms, indicating that neural synchrony has a high degree of temporal precision. We also found that approximately one half of the unit pairs showed nonstationary changes in synchrony that could not be predicted by the modulation of firing rates. Population statistics showed that the onset of synchrony between LGN cells occurred significantly later than that observed between retinal afferents and LGN cells. The synchrony detected among unit pairs recorded on separate tetrodes tended to be more transient and have a later onset than that observed between adjacent units. These findings show that stimulus-evoked synchronous activity within the LGN is often rhythmic, highly nonstationary, and modulated by endogenous processes that are not tightly correlated with firing rate.

Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding

The brain is a highly modular structure. To exploit modularity, it is necessary that spiking activity can propagate from one module to another while preserving the information it carries. Therefore, reliable propagation is one of the key properties of a candidate neural code. Surprisingly, the conditions under which spiking activity can be propagated have received comparatively little attention in the experimental literature. By contrast, several computational studies in the last decade have addressed this issue. Using feedforward networks (FFNs) as a generic network model, they have identified two dynamical activity modes that support the propagation of either asynchronous (rate code) or synchronous (temporal code) spiking. Here, we review the dichotomy of asynchronous and synchronous propagation in FFNs, propose their integration into a single extended conceptual framework and suggest experimental strategies to test our hypothesis.

CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains

Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulant suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence.

Cortical Cell Assemblies: A Possible Mechanism for Motor Programs

The concept of a motor program has been used to interpret a diverse range of empirical findings related to preparation and initiation of voluntary movement. In the absence of an underlying mechanism, its exploratory power has been limited to that of an analogy with running a stored computer program. We argue that the theory of cortical cell assemblies suggests a possible neural mechanism for motor programming. According to this view, a motor program may be conceptualized as a cell assembly, which is stored in the form of strengthened synaptic connections between cortical pyramidal neurons. These connections determine which combinations of corticospinal neurons are activated when the cell assembly is ignited. The dynamics of cell assembly ignition are considered in relation to the problem of serial order. These considerations lead to a plausible neural mechanism for the programming of movements and movement sequences that is compatible with the effects of precue information and sequence length on reaction times. Anatomical and physiological guidelines for future quantitative models of cortical cell assemblies are suggested. By taking into account the parallel re-entrant loops between the cerebral cortex and basal ganglia, the theory of cortical cell assemblies suggests a mechanism for motor plans that involve longer sequences. The suggested model is compared with other existing neural network models for motor programming.

Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference

The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typically require vast sample sizes. While multiple single-cell recordings become increasingly available, experimental approaches to investigate the role of higher-order correlations suffer from the limitations of available analysis techniques. We have recently presented a novel method for cumulant-based inference of higher-order correlations (CuBIC) that detects correlations of higher order even from relatively short data stretches of length T = 10-100 s. CuBIC employs the compound Poisson process (CPP) as a statistical model for the population spike counts, and assumes spike trains to be stationary in the analyzed data stretch. In the present study, we describe a non-stationary version of the CPP by decoupling the correlation structure from the spiking intensity of the population. This allows us to adapt CuBIC to time-varying firing rates. Numerical simulations reveal that the adaptation corrects for false positive inference of correlations in data with pure rate co-variation, while allowing for temporal variations of the firing rates has a surprisingly small effect on CuBICs sensitivity for correlations.

Chasing the cell assembly

Although we know enormous amounts of detailed information about the neurons that make up the cortex, placing this information back into the context of the behaving animal is a serious challenge. The functional cell assembly hypothesis first described by Hebb (The Organization of Behavior; a Neuropsychological Theory. New York: Wiley; 1949) aimed to provide a mechanistic explanation of how groups of neurons, acting together, form a percept. The vast number of neurons potentially involved make testing this hypothesis exceedingly difficult as neither the number nor locations of assembly members are known. Although increasing the number of neurons from which simultaneous recordings are made is of benefit, providing evidence for or against a hypothesis like Hebb's requires more than this. In this review, we aim to outline some recent technical advances, which may light the way in the chase for the functional cell assembly.

Estimating the contribution of assembly activity to cortical dynamics from spike and population measures

The hypothesis that cortical networks employ the coordinated activity of groups of neurons, termed assemblies, to process information is debated. Results from multiple single-unit recordings are not conclusive because of the dramatic undersampling of the system. However, the local field potential (LFP) is a mesoscopic signal reflecting synchronized network activity. This raises the question whether the LFP can be employed to overcome the problem of undersampling. In a recent study in the motor cortex of the awake behaving monkey based on the locking of coincidences to the LFP we determined a lower bound for the fraction of spike coincidences originating from assembly activation. This quantity together with the locking of single spikes leads to a lower bound for the fraction of spikes originating from any assembly activity. Here we derive a statistical method to estimate the fraction of spike synchrony caused by assemblies—not its lower bound—from the spike data alone. A joint spike and LFP surrogate data model demonstrates consistency of results and the sensitivity of the method. Combining spike and LFP signals, we obtain an estimate of the fraction of spikes resulting from assemblies in the experimental data.

Not the module does memory make – but the network

This commentary questions the target articles inferences from a limited set of empirical data to support this model and conceptual scheme. Especially questionable is the attribution of internal representation properties to an assembly of cells in a discrete cortical module firing at a discrete attractor frequency. Alternative inferences are drawn from cortical cooling and cell-firing data that point to the internal representation as a broad and specific cortical network defined by cortico-cortical connectivity. Active memory, it is proposed, consists in the sustained activation of the component neuron populations of the network.

Distributed cell assemblies and detailed cell models

Hebbian cell-assembly theory and attractor networks are good starting points for modeling cortical processing. Detailed cell models can be useful in understanding the dynamics of attractor networks. Cell assemblies are likely to be distributed, with the cortical column as the local processing unit. Synaptic memory may be dominant in all but the first couple of seconds.

Another ANN model for the Miyashita experiments

The Miyashita experiments are very interesting and the results should be examined from a viewpoint of attractor dynamics. Amit's target article shows a path toward realistic modeling by artificial neural networks (ANN), but it is not necessarily the only one. I introduce another model that can explain a substantial part of the empirical observations and makes an interesting prediction. This model consists of such units that have nonmonotonic input-output characteristics with local inhibition neurons.

The Hebbian paradigm reintegrated: Local reverberations as internal representations

Recurrent excitation is experimentally well documented in cortical populations. It provides for intracortical excitatory biases that linearize negative feedback interactions and induce macroscopic state transitions during perception. The concept of the local neighborhood should be expanded to spatial patterns as the basis for perception, in which large areas of cortex are bound into cooperative behavior with near-silent columns as important as active columns revealed by unit recording.

Where the adventure is

Interpreting the Miyashita et al. experiments in terms of a cellassembly representation does not adequately explain the performance of Miyashita's monkeys on novel stimuli. We will argue that the latter observations point to acompositionalrepresentation and suggest a dynamics involving rapid and reversible binding of distinct activity patterns.

Reverberation reconsidered: On the path to cognitive theory

Amit's work addresses a critical issue in cognitive science: the structure of neural representations. The use of Hebbian cell assemblies is a positive step, and we now need to consider its role in a larger cognitive theory. When considering the dynamics of a system built out of attractors, a more limited version of reverberation becomes necessary.

Reverberations of Hebbian thinking

Cortical reverberations may induce synaptic changes that underlie developmental plasticity as well as long-term memory. They may be especially important for the consolidation of synaptic changes. Reverberations in cortical networks should have particular significance during development, when large numbers of new representations are formed. This includes the formation of representations across different sensory modalities.

How do local reverberations achieve global integration?

Amit's Hebbian model risks being overexplanatory, since it does not depend on specific physiological modelling of cortical ANNs, but concentrates on those phenomena which are modelled by a large class of ANNs. While offering a strong demonstration of the presence of Hebb's “cell assemblies,” it does not offer an equal account of Hebb's “phase sequence” concept.

Mathematics of Hebbian attractors

The concept of an attractor in a mathematical dynamical system is reviewed. Emphasis is placed on the distinction between a cell assembly, the corresponding attractor, and the attractor dynamics. The biological significance of these entities is discussed, especially the question of whether the representation of the stimulus requires the full attractor dynamics, or merely the cell assembly as a set of reverberating neurons. Comparison is made to Freeman's study of dynamic patterns in olfaction.

What's in a cell assembly?

The cell assembly as a simple attractor cannot explain many cognitive phenomena. It must be a highly structured network that can sustain highly structured excitation patterns. Moreover, a cell assembly must be more widely distributed in space than on a square millimeter.

The Hebbian paradigm reintegrated: Local reverberations as internal representations

The neurophysiological evidence from the Miyashita group's experiments on monkeys as well as cognitive experience common to us all suggests that local neuronal spike rate distributions might persist in the absence of their eliciting stimulus. In Hebb's cell-assembly theory, learning dynamics stabilize such self-maintaining reverberations. Quasi-quantitive modeling of the experimental data on internal representations in association-cortex modules identifies the reverberations (delay spike activity) as the internal code (representation). This leads to cognitive and neurophysiological predictions, many following directly from the language used to describe the activity in the experimental delay period, others from the details of how the model captures the properties of the internal representations.

Additional tests of Amit's attractor neural networks

Further tests of Amit's model are indicated. One strategy is to use the apparent coding sparseness of the model to make predictions about coding sparseness in Miyashita's network. A second approach is to use memory overload to induce false positive responses in modules and biological systems. In closing, the importance of temporal coding and timing requirements in developing biologically plausible attractor networks is mentioned.

Efficient identification of assembly neurons within massively parallel spike trains

The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide a way to reduce data sets to the "relevant" neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time.

Long-term modifications in motor cortical dynamics induced by intensive practice

The planning of goal-directed movements requires sensory, temporal, and contextual information to be combined. Sensorimotor functions are embedded in large neuronal networks, but it is unclear how networks organize their activity in space and time to optimize behavior. Temporal coordination of activity in many neurons within a network, e.g., spike synchrony, might be complementary to a firing rate code, allowing efficient computation with overall less population activity. Here we asked the question whether intensive practice induces long-term modifications in the temporal structure of synchrony and firing rate at the population level. Three monkeys were trained in a delayed pointing task in which the selection of movement direction depended on correct time estimation. The synchronous firing among pairs of simultaneously recorded neurons in motor cortex was analyzed using the "unitary event" technique. The evolution of synchrony in both time, within the trial, and temporal precision was then quantified at the level of an entire population of neurons by using two different quantification techniques and compared with the population firing rate. We find that the task timing was represented in the temporal structure of significant spike synchronization at the population level. During practice, the temporal structure of synchrony was shaped, with synchrony becoming stronger and more localized in time during late experimental sessions, in parallel with an improvement in behavioral performance. Concurrently, the average population firing rate mainly decreased. Performance optimization through practice might therefore be achieved by boosting the computational contribution of spike synchrony, allowing an overall reduction in population activity.

Early bilateral sensory deprivation blocks the development of coincident discharge in rat barrel cortex

Several theories have proposed a functional role for synchronous neuronal firing in generating the neural code of a sensory perception. Synchronous neural activity develops during a critical postnatal period of cortical maturation, and severely reducing neural activity in a sensory pathway during this period could interfere with the development of coincident discharge among cortical neurons. Loss of such synchrony could provide a fundamental mechanism for the degradation of acuity shown in behavioral studies. We tested the hypothesis that synchronous discharge of barrel cortex neurons would fail to develop after sensory deprivation produced by bilateral whisker trimming from birth to postnatal day 60. By studying the correlated discharge of cortical neuron pairs, we found evidence for strong correlated firing in control animals, and this synchrony was almost absent among pairs of cortical barrel neurons in deprived animals. The degree of synchrony impairment was different in subregions of rat barrel cortex. The model that best fits the data is that cortical neurons receiving direct inputs from the primary sensory (lemniscal) pathway show the greatest decrement in synchrony following sensory deprivation, while neurons with diverse inputs from other areas of thalamus and cortex are relatively less affected in this dimension of cortical function.

Data-driven significance estimation for precise spike correlation

The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools and the impact of potentially erroneous preprocessing stages are emphasized.

Bayesian inference of functional connectivity and network structure from spikes

Current multielectrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron influences the other neurons. A Bayesian approach that combines information from the recorded spikes (likelihood) with prior beliefs about functional connectivity (prior) can improve inference of these parameters and reduce overfitting. Recent studies have used likelihood functions based on the statistics of point-processes and a prior that captures the sparseness of neural connections. Here we include a prior that captures the empirical finding that interactions tend to vary smoothly in time. We show that this method can successfully infer connectivity patterns in simulated data and apply the algorithm to spike data recorded from primary motor (M1) and premotor (PMd) cortices of a monkey. Finally, we present a new approach to studying structure in inferred connections based on a Bayesian clustering algorithm. Groups of neurons in M1 and PMd show common patterns of input and output that may correspond to functional assemblies.

Can spike coordination be differentiated from rate covariation?

There has been a long and lively debate on whether rate covariance and temporal coordination of spikes, regarded as potential origins for correlations in cortical spike signals, fulfill different roles in the cortical code. In this context, studies that report spike coordination have often been criticized for ignoring fast nonstationarities, which would result in wrongly assigned spike coordination. The underlying hypothesis of this critique is that spike coordination is essentially identical to rate covariation, only on a shorter timescale. This study investigates the validity of this critique. We provide a decomposition for the cross-correlation function of doubly stochastic point processes, where each of the components corresponds precisely to the concepts of dependence under investigation. This allows us to correct the correlation function for rate effects, which implies that spike coordination and rate covariation are statistically separable concepts of dependence. Furthermore, we present direct and intuitive model implementations of the discussed concepts and illustrate that their difference is not a matter of timescale. Analysis of data generated by our models and analytical description of the relevant estimators reveals, however, that spike coordination dramatically influences the accuracy of rate covariance estimation. As a consequence, extreme parameter combinations can lead to situations where the concept of dependence cannot be identified empirically. However, for a wide range of parameters, the concept of dependence underlying a given data set can be identified regardless of its timescale.

Widespread spatial integration in primary somatosensory cortex

Tactile discrimination depends on integration of information from the discrete receptive fields (RFs) of peripheral sensory afferents. Because this information is processed over a hierarchy of subcortical nuclei and cortical areas, the integration likely occurs at multiple levels. The current study presents results indicating that neurons across most of the extent of the hand representation in monkey primary somatosensory cortex (area 3b) interact, even when these neurons have separate RFs. We obtained simultaneous recordings by using a 100-electrode array implanted in the hand representation of primary somatosensory cortex of two anesthetized owl monkeys. During a series of 0.5-s skin indentations with single or dual probes, the distance between electrodes from which neurons with synchronized spike times were recorded exceeded 2 mm. The results provide evidence that stimuli on different parts of the hand influence the degree of synchronous firing among a large population of neurons. Because spike synchrony potentiates the activation of commonly targeted neurons, synchronous neural activity in primary somatosensory cortex can contribute to discrimination of complex tactile stimuli.

NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events

We present a non-parametric and computationally efficient method named NeuroXidence that detects coordinated firing of two or more neurons and tests whether the observed level of coordinated firing is significantly different from that expected by chance. The method considers the full auto-structure of the data, including the changes in the rate responses and the history dependencies in the spiking activity. Also, the method accounts for trial-by-trial variability in the dataset, such as the variability of the rate responses and their latencies. NeuroXidence can be applied to short data windows lasting only tens of milliseconds, which enables the tracking of transient neuronal states correlated to information processing. We demonstrate, on both simulated data and single-unit activity recorded in cat visual cortex, that NeuroXidence discriminates reliably between significant and spurious events that occur by chance.

Bootstrap significance test of synchronous spike events—A case study of oscillatory spike trains

The purpose of this monograph is two folds. Firstly, we introduce challenging spike data to the statistical analysis. The data of two neurons recorded from the cat visual pathway show various non-stationary characteristics not fitted by the Poisson spike train. Spike firings of both neurons are strongly periodic and tightly synchronized. Our second purpose is a case study of applications of various statistical methods for the significance test of the time-varying spike synchrony. We provide various general remarks to the statistical analysis of the synchronous spike activities. At first, we apply the unitary event analysis. The significance limit for the coincident spike events by the Poisson distribution is compared with the limit given by the non-parametric test based on the bootstrap samplings. The bootstrap test performs superior to the Poisson test in two respects: (1) avoids false positives due to the sudden change of spike density; and (2) takes into account the non-stationary change of the spiking pattern at different sampling windows. When the spike trains are highly periodic, the histogram of the number of accidental coincident spike events over the bootstrap samples has a systematically larger variance than the Poisson distribution. We find that a large variance originates from the correlation between the successive coincident spike events in the structured spike trains. The significance of the time-varying synchrony is tested by another statistical method by Ventura et al., which is based on the adaptive smoothing method and the bootstrap significance test. .

Locus ceruleus regulates sensory encoding by neurons and networks in waking animals

Substantial evidence indicates that the locus ceruleus (LC)-norepinephrine (NE) projection system regulates behavioral state and state-dependent processing of sensory information. Tonic LC discharge (0.1-5.0 Hz) is correlated with levels of arousal and demonstrates an optimal firing rate during good performance in a sustained attention task. In addition, studies have shown that locally applied NE or LC stimulation can modulate the responsiveness of neurons, including those in the thalamus, to nonmonoaminergic synaptic inputs. Many recent investigations further indicate that within sensory relay circuits of the thalamus both general and specific features of sensory information are represented within the collective firing patterns of like-modality neurons. However, no studies have examined the impact of NE or LC output on the discharge properties of ensembles of functionally related cells in intact, conscious animals. Here, we provide evidence linking LC neuronal discharge and NE efflux with LC-mediated modulation of single-neuron and neuronal ensemble representations of sensory stimuli in the ventral posteriomedial thalamus of waking rats. As such, the current study provides evidence that output from the LC across a physiologic range modulates single thalamic neuron responsiveness to synaptic input and representation of sensory information across ensembles of thalamic neurons in a manner that is consistent with the well documented actions of LC output on cognition.

Computational inference of neural information flow networks

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

One of the challenges in the area of brain research is to decipher networks describing the flow of information among communicating neurons in the form of electrophysiological signals. These networks are thought to be responsible for perceiving and learning about the environment, as well as producing behavior. Monitoring these networks is limited by the number of electrodes that can be placed in the brain of an awake animal, while inferring and reasoning about these networks is limited by the availability of appropriate computational tools. Here, Smith and Yu and colleagues begin to address these issues by implanting microelectrode arrays in the auditory pathway of freely moving songbirds and by analyzing the data using new computational tools they have designed for deciphering networks. The authors find that a dynamic Bayesian network algorithm they developed to decipher gene regulatory networks from gene expression data effectively infers putative information flow networks in the brain from microelectrode array data. The networks they infer conform to known anatomy and other biological properties of the auditory system and offer new insight into how the auditory system processes natural and synthetic sound. The authors believe that their results represent the first validated study of the inference of information flow networks in the brain.

fMRI MODELS OF DENDRITIC AND ASTROCYTIC NETWORKS

In order to elucidate the relationships between hierarchical structures within the neocortical neuropil and the information carried by an ensemble of neurons encompassing a single voxel, it is essential to predict through volume conductor modeling LFPs representing average extracellular potentials, which are expressed in terms of interstitial potentials of individual cells in networks of gap-junctionally connected astrocytes and synaptically connected neurons. These relationships have been provided and can then be used to investigate how the underlying neuronal population activity can be inferred from the measurement of the BOLD signal through electrovascular coupling mechanisms across the blood-brain barrier. The importance of both synaptic and extrasynaptic transmission as the basis of electrophysiological indices triggering vascular responses between dendritic and astrocytic networks, and sequential configurations of firing patterns in composite neural networks is emphasized. The purpose of this review is to show how fMRI data may be used to draw conclusions about the information transmitted by individual neurons in populations generating the BOLD signal.

Stimulus-dependent correlated firing in directionally selective retinal ganglion cells

Synchronous spiking has been postulated to be a meta-signal in visual cortex and other CNS loci that tags neuronal spike responses to a single entity. In retina, however, synchronized spikes have been postulated to arise via mechanisms that would largely preclude their carrying such a code. One such mechanism is gap junction coupling, in which synchronous spikes would be a by-product of lateral signal sharing. Synchronous spikes have also been postulated to arise from common-source inputs to retinal ganglion cells having overlapping receptive fields, and thus code for stimulus location in the overlap area. On-Off directionally selective ganglion cells of the rabbit retina exhibit a highly precise tiling pattern in which gap junction coupling occurs between some neighboring, same-preferred-direction cells. Depending on how correlated spikes arise, and for what purpose, one could postulate that synchronized spikes in this system (1) always arise in some subset of same-direction cells because of gap junctions, but never in non-same-preferred-directional cells; (2) never arise in same-directional cells because their receptive fields do not overlap, but arise only in different-directional cells whose receptive fields overlap, as a code for location in the overlap region; or (3) arise in a stimulus-dependent manner for both same- and different-preferred-direction cells for a function similar to that postulated for neurons in visual cortex. Simultaneous, extracellular recordings were obtained from neighboring On-Off directionally selective (DS) ganglion cells having the same and different preferred directions in an isolated rabbit retinal preparation. Stimulation by large flashing spots elicited responses from DS ganglion-cell pairs that typically showed little synchronous firing. Movement of extended bars, however, often produced synchronous spikes in cells having similar or orthogonal preferred directions. Surprisingly, correlated firing could occur for the opposite contrast polarity edges of moving stimuli when the leading edge of a sweeping bar excited the receptive field of one cell as its trailing edge stimulated another. Pharmacological manipulations showed that the spike synchronization is enhanced by excitatory cholinergic amacrine-cell inputs, and reduced by inhibitory GABAergic inputs, in a motion-specific manner. One possible interpretation is that this synchronous firing could be a signal to higher centers that the outputs of the two DS ganglion cells should be "bound" together as responding to a contour of a common object.

Measurement of time-dependent changes in the irregularity of neural spiking

Irregularity of firing in spike trains has been associated with coding processes and information transfer or alternatively treated as noise. Previous studies of irregularity have mainly used the coefficient of variation (CV) of the interspike interval distribution. Proper estimation of CV requires a constant underlying firing rate, a condition that most experimental situations do not fulfill either within or across trials. Here we introduce a novel irregularity metric based on the ratio of adjacent intervals in the spike train. The new metric is not affected by firing rate and is very localized in time so that it can be used to examine the time course of irregularity relative to an alignment marker. We characterized properties of the new metric with simulated spike trains of known characteristics and then applied it to data recorded from 108 single neurons in the motor cortex of two monkeys during performance of a precision grip task. Fifty-six cells were antidromically identified as pyramidal tract neurons (PTNs). Sixty-one cells (30 PTNs) exhibited significant temporal modulation of their irregularity during task performance with the contralateral hand. The irregularity modulations generally differed in sign and latency from the modulations of firing rate. High irregularity tended to occur during the task phases requiring the most detailed control of movement, whereas neural firing became more regular during the steady hold phase. Such irregularity modulation could have important consequences for the response of downstream neurons and may provide insight into the nature of the cortical code.

NMDA/AMPA ratio impacts state transitions and entrainment to oscillations in a computational model of the nucleus accumbens medium spiny projection neuron

We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. After tuning the model by adjusting maximal current conductances in each compartment, the model cell closely matched whole-cell recordings from an adult rat NAcb slice preparation. Synaptic inputs in the range of 1000-1300 Hz are required to maintain an "up" state in the model. Cell firing in the model required concurrent depolarization of several dendritic branches, which responded independently to afferent input. Depolarization from action potentials traveled to the tips of the dendritic branches and increased Ca2+ influx through voltage-gated Ca2+ channels. As NMDA/AMPA current ratios were increased, the membrane showed an increase in hysteresis of "up" and "down" state dwell times, but intrinsic bistability was not observed. The number of oscillatory inputs required to entrain the model cell was determined to be approximately 20% of the "up" state inputs. Altering the NMDA/AMPA ratio had a profound effect on processing of afferent input, including the ability to entrain to oscillations in afferent input in the theta range (4-12 Hz). These results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction.

Finite State Automata Resulting from Temporal Information Maximization and a Temporal Learning Rule

We extend Linkser's Infomax principle for feedforward neural networks to a measure for stochastic interdependence that captures spatial and temporal signal properties in recurrent systems. This measure, stochastic interaction, quantifies the Kullback-Leibler divergence of a Markov chain from a product of split chains for the single unit processes. For unconstrained Markov chains, the maximization of stochastic interaction, also called Temporal Infomax, has been previously shown to result in almost deterministic dynamics. This letter considers Temporal Infomax on constrained Markov chains, where some of the units are clamped to prescribed stochastic processes providing input to the system. Temporal Infomax in that case leads to finite state automata, either completely deterministic or weakly nondeterministic. Transitions between internal states of these systems are almost perfectly predictable given the complete current state and the input, but the activity of each single unit alone is virtually random. The results are demonstrated by means of computer simulations and confirmed analytically. It is furthermore shown numerically that Temporal Infomax leads to a high information flow from the input to internal units and that a simple temporal learning rule can approximately achieve the optimization of temporal interaction. We relate these results to experimental data concerning the correlation dynamics and functional connectivities observed in multiple electrode recordings.

Neuronal synchronicity in the pigeon "prefrontal cortex" during learning

In general electrophysiological studies focus on the investigation of changes in discharge rate of neuronal responses which are related to sensory or behavioral events. However, equally important for explanation of higher cognitive functions, learning, memory storage and complex behavior is the interaction between neurons that are connected in cell assemblies. Synchronized inputs onto a neuron are much more effective at eliciting the following activity than uncorrelated inputs. The goal of the present study was to determine either the changes in discharge rate of neurons in the pigeon nidopallium caudolaterale as well as the synchronicity of these neurons during a discriminatory learning task. We found rate modulation effects as well as modulation of synchronization during the learning process.

Advancing the boundaries of high-connectivity network simulation with distributed computing

The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The software scales excellently on a wide range of tested hardware, so it can be used in an interactive and iterative fashion for the development of ideas, and results can be produced quickly even for very large networks. In contrast to earlier approaches, a wide class of neuron models and synaptic dynamics can be represented.

Highly nonrandom features of synaptic connectivity in local cortical circuits

How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further.

A dataset of hundreds of recordings in which four neurons were simultaneously monitored reveals clustered connectivity patterns among cortical neurons

Neural circuitry of judgment and decision mechanisms

Tracing the neural circuitry of decision formation is a critical step in the understanding of higher cognitive function. To make a decision, the primate brain coordinates dynamic interactions between several cortical and subcortical areas that process sensory, cognitive, and reward information. In selecting the optimal behavioral response, decision mechanisms integrate the accumulating evidence with reward expectation and knowledge from prior experience, and deliberate about the choice that matches the expected outcome. Linkages between sensory input and behavioral output responsible for response selection are shown in the neural activity of structures from the prefrontal-basal ganglia-thalamo-cortical loop. The deliberation process can be best described in terms of sensitivity, selection bias, and activation threshold. Here, we show a systems neuroscience approach of the visual saccade decision circuit and the interaction between its components during decision formation.

Rapid fluctuations in rat barrel cortex plasticity

Neuronal populations in the sensory cortex exhibit fluctuations in excitability, and the present experiments tested the hypothesis that these variations coincide with peaks and troughs in cortical modifiability. The activity of multiunit neuronal clusters under light urethane anesthesia was recorded through 100-microelectrode arrays implanted in the infragranular layers of rat barrel cortex. Spontaneous activity was characterized by "bursts" of spikes, synchronized across the barrel cortex. This allowed activity at one selected electrode to be taken as a reliable monitor of widespread cortical bursts. We used spikes at the selected electrode to trigger stimulation of two pairs of whiskers during a 50 min conditioning procedure: (1) for the "burst-conditioned" whisker pair, each stimulus was delivered 1 msec after the triggering spike, activating cortex coincident with the burst; and (2) for the "interburst-conditioned" whisker pair, each stimulus was delivered 300 msec after the triggering spike, activating cortex during the trough between bursts. The cross-correlation between cortical neurons in the pairs of columns matching the stimulated whisker pairs was estimated after the termination of the conditioning procedure. Conditioning produced a twofold increase in the degree of co-firing between infragranular neurons in columns receiving burst-conditioned costimulation but no significant change in connectivity between infragranular neurons in columns receiving interburst-conditioned costimulation, although the two pairs of columns received an equal number of sensory inputs. These findings suggest that the strength of co-activity between columns in the barrel cortex can be modified by sensory input patterns during discrete, intermittent intervals time-locked to bursts.

Search for computational modules in the C. elegans brain

BACKGROUND

Does the C. elegans nervous system contain multi-neuron computational modules that perform stereotypical functions? We attempt to answer this question by searching for recurring multi-neuron inter-connectivity patterns in the C. elegans nervous system's wiring diagram.

RESULTS

Our statistical analysis reveals that some inter-connectivity patterns containing two, three and four (but not five) neurons are significantly over-represented relative to the expectations based on the statistics of smaller inter-connectivity patterns.

CONCLUSIONS

Over-represented patterns (or motifs) are candidates for computational modules that may perform stereotypical functions in the C. elegans nervous system. These modules may appear in other species and need to be investigated further.

Control of neuronal discharge timing by afferent fiber number and the temporal pattern of afferent impulses

We employ computer simulations to explore the effect of different temporal patterns of afferent impulses on the evoked discharge of a model cerebellar Purkinje cell. We show that the frequency and temporal correlation of impulses across afferent fibers determines which of four regimes of discharge activity is evoked. In the uncorrelated, here Poissonian, case, (i) cell discharge is determined by the total stimulation rate and temporal patterns of discharge are the same for different combinations of afferent fiber number and mean impulse rate per fiber giving the same total stimulation. Alternatively, if temporal correlations are present in the stimulus, (ii) for stimulation frequencies of 4 to at least 64 Hz there is a narrow range of afferent fiber number for which every stimulus pulse (composed of a single impulse on each afferent fiber) evokes a single action potential. In this case cell discharge is frequency locked to the stimulus with a concomitant reduction in discharge variability. (iii) For lower fiber numbers and thus discharge frequencies lower than the locking frequency, the variability of cell discharge is typically independent of afferent impulse timing, whereas, (iv) at higher fiber numbers and thus higher discharge frequencies, the reverse is true. We conclude that in case (iii) the cell acts as an integrator and discharge is determined by the stimulation rate, whereas in case (iv) the cell acts as a coincidence detector and the timing of discharge is determined by the temporal pattern of afferent stimulation. We discuss our results in terms of their significance for neuronal activity at the network level and suggest that the reported effects of varying stimulus timing and afferent convergence can be expected to obtain also with other principal cell types within the central nervous system.

Amplitude and Direction of Saccadic Eye Movements Depend on the Synchronicity of Collicular Population Activity

Synchronization of neuronal discharges has been observed in numerous brain structures, but opinions diverge regarding its significance in neuronal processing. Here we investigate whether the motion vectors of saccadic eye movements evoked by electrical multisite stimulation of the cat superior colliculus (SC) are influenced by varying the degree of synchrony between the stimulus trains. With synchronous activation of SC sites, the vectors of the resulting saccades correspond approximately to the averages of the vectors of saccades evoked from each site alone. In contrast, when the pulses of trains applied to the different sites are temporally offset by as little as 5–10 ms, the vectors of the resulting saccades come close to the sum of the individual vectors. Thus saccade vectors depend not only on the site and amplitude of collicular activation but also on the precise temporal relations among the respective spike trains. These data indicate that networks within or downstream from the SC discriminate with high temporal resolution between synchronous and asynchronous population responses. This supports the hypothesis that information is encoded not only in the rate of neuronal responses but also in the precise temporal relations between discharges.

Optimal stimulus coding by neural populations using rate codes

We create a framework based on Fisher information for determining the most effective population coding scheme for representing a continuous-valued stimulus attribute over its entire range. Using this scheme, we derive optimal single- and multi-neuron rate codes for homogeneous populations using several statistical models frequently used to describe neural data. We show that each neuron's discharge rate should increase quadratically with the stimulus and that statistically independent neural outputs provides optimal coding. Only cooperative populations can achieve this condition in an informationally effective way.

Dopaminergic drug effects on physiological connectivity in a human cortico-striato-thalamic system

Cortico-striato-thalamic (CST) systems are anatomical substrates for many motor and executive functions and are implicated in diverse neuropsychiatric disorders. Electrophysiological studies in rats, monkeys and patients with Parkinson's disease have shown that power and coherence of low frequency oscillations in CST systems can be profoundly modulated by dopaminergic drugs. We combined functional MRI with correlational and path analyses to investigate functional and effective connectivity, respectively, of a prefronto-striato-thalamic system activated by object location learning in healthy elderly human subjects (n = 23; mean age = 72 years). Participants were scanned in a repeated measures, randomized, placebo-controlled design to measure modulation of physiological connectivity between CST regions following treatment with drugs which served both to decrease (sulpiride) and increase (methylphenidate) dopaminergic transmission, as well as non-dopaminergic treatments (diazepam and scopolamine) to examine non-specific effects. Functional connectivity of caudate nucleus was modulated specifically by dopaminergic drugs, with opposing effects of sulpiride and methylphenidate. The more salient effect of sulpiride was to increase functional connectivity between caudate and both thalamus and ventral midbrain. A path diagram based on prior knowledge of unidirectional anatomical projections between CST components was fitted satisfactorily to the observed inter-regional covariance matrix. The effect of sulpiride was defined more specifically in the context of this model as increased strength of effective connection from ventral midbrain to caudate nucleus. In short, we have demonstrated enhanced functional and effective connectivity of human caudate nucleus following sulpiride treatment, which is compatible both with the anatomy of ascending dopaminergic projections and with electrophysiological studies indicating abnormal coherent oscillations of CST neurons in parkinsonian states.