T134. Quantifying dynamical interactions between neural assemblies

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here we recorded the activity from ~40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven network model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine, unveils ~200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. From this, we mathematically derived an interregional functional connectivity matrix, which is conserved across individual animals and correlates well with structural connectivity. This novel, assembly-based generative model of brain-wide neural dynamics enables physiology-bound perturbation experiments in silico.

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Decoding the allocation of visual attention from prefrontal neural assemblies in behaving primates

Journal of Vision ◽

10.1167/14.10.519 ◽

2014 ◽

Vol 14 (10) ◽

pp. 519-519

Author(s):

S. Tremblay ◽

F. Pieper ◽

A. Sachs ◽

J. Martinez-Trujillo

Keyword(s):

Visual Attention ◽

Neural Assemblies

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Cooperativity in Distributed Respiratory and Cardiovascular-Related Brainstem Neural Assemblies: Insights from Many-Neuron Recordings

Cardiorespiratory and Motor Coordination ◽

10.1007/978-3-642-75507-1_15 ◽

1991 ◽

pp. 131-137

Author(s):

B. G. Lindsey ◽

Y. M. Hernandez ◽

R. Shannon

Keyword(s):

Neural Assemblies

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The dynamics of language

Behavioral and Brain Sciences ◽

10.1017/s0140525x99251825 ◽

1999 ◽

Vol 22 (2) ◽

pp. 284-285

Author(s):

Peter W. Culicover ◽

Andrzej Nowak

Keyword(s):

Phase Space ◽

Dynamical Systems ◽

Simple Model ◽

Syntactic Structure ◽

Neural Assemblies ◽

Systems Perspective ◽

Model Based ◽

Associative Structures

To deal with syntactic structure, one needs to go beyond a simple model based on associative structures, and to adopt a dynamical systems perspective, where each phrase and sentence of a language is represented as a trajectory in a syntactic phase space. Neural assemblies could possibly be used to produce dynamics that in principle could handle syntax along these lines.

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Properties of Correlated Neural Activity Clusters in Cat Auditory Cortex Resemble Those of Neural Assemblies

Journal of Neurophysiology ◽

10.1152/jn.00059.2006 ◽

2006 ◽

Vol 96 (2) ◽

pp. 746-764 ◽

Cited By ~ 45

Author(s):

Jos J. Eggermont

Keyword(s):

Auditory Cortex ◽

Spike Activity ◽

Correlation Matrix ◽

Cross Correlation ◽

Receptive Fields ◽

Primary Auditory Cortex ◽

Simultaneous Recording ◽

Electrode Arrays ◽

Tone Stimulus ◽

Neural Assemblies

Spiking activity was recorded from cat auditory cortex using multi-electrode arrays. Cross-correlograms were calculated for spikes recorded on separate microelectrodes. The pair-wise cross-correlation matrix was constructed for the peak values of the correlograms. Hierarchical clustering was performed on the cross-correlation matrix for six stimulus conditions. These were silence, three multi-tone stimulus ensembles with different spectral densities, low-pass amplitude-modulated noise, and Poisson-distributed click trains that each lasted 15 min. The resulting neuron clusters reflect patches in cortex of up to several mm2 in size that expand and contract in response to different stimuli. Cluster positions and size were very similar for spontaneous activity and multi-tone stimulus-evoked activity but differed between those conditions and the noise and click stimuli. Cluster size was significantly larger in posterior auditory field (PAF) compared with primary auditory cortex (AI), whereas the fraction of common spikes (within a 10-ms window) across all electrode activity participating in a cluster was significantly higher in AI compared with PAF. Clusters crossed area boundaries in <5% of the cases were simultaneous recording were made in AI and PAF. Clusters are therefore similar to but not synonymous with the traditional view of neural assemblies. Common-spike spectrotemporal receptive fields (STRFs) were obtained for common-spike activity and all-spike activity within a cluster. Common-spike STRFs had higher signal-to-noise ratio than all-spike STRFs and showed generally spectral and temporal sharpening. The coincident and noncoincident output of the clusters could potentially act in parallel and may serve different modes of stimulus coding.

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On the unproductiveness of language and linguistics

Behavioral and Brain Sciences ◽

10.1017/s0140525x0635902x ◽

2006 ◽

Vol 29 (1) ◽

pp. 82-84

Author(s):

David M. W. Powers

Keyword(s):

Computational Complexity ◽

Computational Model ◽

Neural Assemblies ◽

And Linguistics

van der Velde & de Kamps (dvV&dK) present a response to Jackendoff's four challenges in terms of a computational model. This commentary supports the position that neural assemblies mediated by recurrence and delay indeed have sufficient theoretical power to deal with all four challenges. However, we question the specifics of the model proposed, in terms of both neurophysiological plausibility and computational complexity.

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