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Moshe Abeles

Publications and source records attributed to Moshe Abeles.

10 recordsLinked to original sources
PubMed2005

Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior.

We show that times of spikes can be very precise. In the cerebral cortex, where each nerve cell is affected by thousands of others, it is the common belief that the exact time of a spike is random up to an averaged firing rate over tens of milliseconds. In a brain slice, precise time relations of several neurons have been observed. It remained unclear whether this phenomenon can also be observed in brains of behaving animals. Here we show, in behaving monkeys, that time intervals between spikes, measured in correspondence to a specific behavior, may be controlled to within the milliseconds range.

Action Potentials↗
PubMed2005

Partial cross-correlation analysis resolves ambiguity in the encoding of multiple movement features.

A classical question in neuroscience is which features of a stimulus or of an action are represented in brain activity. When several features are interdependent either at a given point in time or at distinct points in time, neural activity related to one feature appears to be correlated with other features. Thus techniques that simultaneously consider multiple features cannot account for delayed interdependencies between features. The result is an ambiguity with respect to the encoded features. Here, we resolve this ambiguity by applying a novel statistical method based on partial cross-correlations. The method yields estimates of linear correlations between neural activity and a given feature that are not affected by linear correlations with other features at multiple time delays. The method also provides a graphical output measured on a scale that allows for comparisons between different features, neurons, and experiments. We use real movement data and neural activity simulated according to a wide range of tuning models to illustrate the method. When applied to real neural activity, the procedure yields results that indicate which of the considered features the neural activity is related to and at what time delays.

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PubMed2005

Applying resampling methods to neurophysiological data.

Standard statistical techniques do not always provide answers to complex physiological questions because often there are no parametric or non-parametric distributions on which significance can be estimated. Resampling methods provide a battery of tests that can be used in such circumstances. In the past few years these methods have been explored theoretically and are now employed frequently. In this paper we describe a unified framework for the use of such methods in the context of neurophysiological data analysis. We construct specific tests for placing confidence limits on estimates of mutual information and on parameters of circular data, and we present procedures for testing hypotheses on circular and on partitioned data. These tests are explained in detail and illustrated with real data from experiments with behaving monkeys.

Algorithms↗
PubMed2005

Memory capacity of balanced networks.

We study the problem of memory capacity in balanced networks of spiking neurons. Associative memories are represented by either synfire chains (SFC) or Hebbian cell assemblies (HCA). Both can be embedded in these balanced networks by a proper choice of the architecture of the network. The size w(E) of a pool in an SFC or of an HCA is limited from below and from above by dynamical considerations. Proper scaling of w(E) by radicalK, where K is the total excitatory synaptic connectivity, allows us to obtain a uniform description of our system for any given K. Using combinatorial arguments, we derive an upper limit on memory capacity. The capacity allowed by the dynamics of the system, alpha(c), is measured by simulations. For HCA, we obtain alpha(c) of order 0.1, and for SFC, we find values of order 0.065. The capacity can be improved by introducing shadow patterns, inhibitory cell assemblies that are fed by the excitatory assemblies in both memory models. This leads to a doubly balanced network, where, in addition to the usual global balancing of excitation and inhibition, there exists specific balance between the effects of both types of assemblies on the background activity of the network. For each of the memory models and for each network architecture, we obtain an allowed region (phase space) for w(E)/ radicalK in which the model is viable.

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PubMed2003

Neuronal activity in motor cortical areas reflects the sequential context of movement.

Natural actions can be described as chains of simple elements, whereas individual motion elements are readily concatenated to generate countless movement sequences. Sequence-specific neurons have been described extensively, suggesting that the motor system may implement temporally complex motions by using such neurons to recruit lower-level movement neurons modularly. Here, we set out to investigate whether activity of movement-related neurons is independent of the sequential context of the motion. Two monkeys were trained to perform linear arm movements either individually or as components of double-segment motions. However, comparison of neuronal activity between these conditions is delicate because subtle kinematic variations generally occur within different contexts. We therefore used extensive procedures to identify the contribution of variations in motor execution to differences in neuronal activity. Yet, even after application of these procedures we find that neuronal activity in the motor cortex (PMd and M1) associated with a given motion segment differs between the two contexts. These differences appear during preparation and become even more prominent during motion execution. Interestingly, despite context-related differences on the single-neuron level, the population as a whole still allows a reliable readout of movement direction regardless of the sequential context. Thus the direction of a movement and the sequential context in which it is embedded may be simultaneously and reliably encoded by neurons in the motor cortex.

Action Potentials↗
PubMed2003

On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance.

The capability of feedforward networks composed of multiple layers of integrate-and-fire neurons to transmit rate code was examined. Synaptic connections were made only from one layer to the next, and excitation was balanced by inhibition. When time is discrete and the synaptic potentials rise instantaneously, we show that, for random uncorrelated input to layer one, the mean rate of activity in deep layers is essentially independent of input firing rate. This implies that the input rate cannot be transmitted reliably in such feedforward networks because neurons in a given layer tend to synchronize partially with each other because of shared inputs. As a result of this synchronization, the average firing rate in deep layers will either decay to zero or reach a stable fixed point, depending on model parameters. When time is treated continuously and the synaptic potentials rise instantaneously, these effects develop slowly, and rate transmission over a limited number of layers is possible. However, the correlations among neurons at the same layer hamper reliable assessment of firing rate by averaging over 100 msec (or less). When the synaptic potentials develop gradually, as is the realistic case, transmission of rate code fails. In a network in which inhibition only balances the mean excitation but is not timed precisely with it, neurons in each layer fire together, and this volley successively propagates from layer to layer. We conclude that the transmission of rate code in feedforward networks is highly unlikely.

Animals↗
PubMed2003

Dynamical organization of directional tuning in the primate premotor and primary motor cortex.

Although previous studies have shown that activity of neurons in the motor cortex is related to various movement parameters, including the direction of movement, the spatial pattern by which these parameters are represented is still unresolved. The current work was designed to study the pattern of representation of the preferred direction (PD) of hand movement over the cortical surface. By studying pairwise PD differences, and by applying a novel implementation of the circular variance during preparation and movement periods in the context of a center-out task, we demonstrate a nonrandom distribution of PDs over the premotor and motor cortical surface of two monkeys. Our analysis shows that, whereas PDs of units recorded by nonadjacent electrodes are not more similar than expected by chance, PDs of units recorded by adjacent electrodes are. PDs of units recorded by a single electrode display the greatest similarity. Comparison of PD distributions during preparation and movement reveals that PDs of nearby units tend to be more similar during the preparation period. However, even for pairs of units recorded by a single electrode, the mean PD difference is typically large (45 degrees and 75 degrees during preparation and movement, respectively), so that a strictly modular representation of hand movement direction over the cortical surface is not supported by our data.

Animals↗
PubMedDate not supplied

A model for representing the dynamics of a system of synfire chains.

Competitive synchronization among synfire chains may model the dynamics of binding and compositionality. Typically, such models require simulations of hundreds of thousands of neurons. Here we show that the behavior of such large systems can be numerically analyzed by representing the neuronal activity in a synfire chain as a wave. The position and velocity of waves are the only parameters needed to represent the neural activity within a synfire chain. With this wave model we describe how waves are generated, decay, interact within a single chain and among chains. The behavior of the wave model is compared to the behavior of detailed simulations of synfire chains with no qualitative difference. We show that interacting waves tend to become locked to each other (wave synchronization). Finally we prove that: (1) Within a system of many synfire chains with symmetric interchain connections, as long as waves do not fade away or become fully synchronized, the total synchrony among waves can only increase (or stay constant), but never decrease. (2) A wave that increases its speed during the synchronization process becomes more stable.

Models, Neurological↗
PubMedDate not supplied

Modeling compositionality by dynamic binding of synfire chains.

This paper examines the feasibility of manifesting compositionality by a system of synfire chains. Compositionality is the ability to construct mental representations, hierarchically, in terms of parts and their relations. We show that synfire chains may synchronize their waves when a few orderly cross links are available. We propose that synchronization among synfire chains can be used for binding component into a whole. Such synchronization is shown both for detailed simulations, and by numerical analysis of the propagation of a wave along a synfire chain. We show that global inhibition may prevent spurious synchronization among synfire chains. We further show that selecting which synfire chains may synchronize to which others may be improved by including inhibitory neurons in the synfire pools. Finally we show that in a hierarchical system of synfire chains, a part-binding problem may be resolved, and that such a system readily demonstrates the property of priming. We compare the properties of our system with the general requirements for neural networks that demonstrate compositionality.

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