Neocortical gap junction-coupled interneuron systems may induce chaotic behavior itinerant among quasi-attractors exhibiting transient synchrony

FUJII, H

doi:10.1016/s0925-2312(04)00032-3

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…This dynamic transition between different representations of information has been demonstrated experimentally ( Rainer et al 1999 ; Takeda and Funahashi 2002 ; Mushiake et al 2006 ), leading to the question of how neural circuits achieve the state transition. Theoretical studies have suggested that a state transition could be induced by the operation of external inputs having synchronous properties ( Salinas and Sejnowski 2000 ; Aoyagi and Aoki 2004 ; Fujii and Tsuda 2004 ; Aoki and Aoyagi 2007 ), including transient synchrony in the network ( Gutkin et al 2001 ).…”

Section: Introductionmentioning

confidence: 99%

Discharge Synchrony during the Transition of Behavioral Goal Representations Encoded by Discharge Rates of Prefrontal Neurons

et al. 2008

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To investigate the temporal relationship between synchrony in the discharge of neuron pairs and modulation of the discharge rate, we recorded the neuronal activity of the lateral prefrontal cortex of monkeys performing a behavioral task that required them to plan an immediate goal of action to attain a final goal. Information about the final goal was retrieved via visual instruction signals, whereas information about the immediate goal was generated internally. The synchrony of neuron pair discharges was analyzed separately from changes in the firing rate of individual neurons during a preparatory period. We focused on neuron pairs that exhibited a representation of the final goal followed by a representation of the immediate goal at a later stage. We found that changes in synchrony and discharge rates appeared to be complementary at different phases of the behavioral task. Synchrony was maximized during a specific phase in the preparatory period corresponding to a transitional stage when the neuronal activity representing the final goal was replaced with that representing the immediate goal. We hypothesize that the transient increase in discharge synchrony is an indication of a process that facilitates dynamic changes in the prefrontal neural circuits in order to undergo profound state changes.

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Section: Introductionmentioning

confidence: 99%

Discharge Synchrony during the Transition of Behavioral Goal Representations Encoded by Discharge Rates of Prefrontal Neurons

et al. 2008

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“…However, meaningfulness cannot be reduced to the detection of a behavioural correlate [62] – [64] . The repeating activity in a network may also be considered meaningful if it allows the activation of neural elements that can be associated to other attractors, thus allowing the build-up of higher order dynamics by means of itinerancy between attractor basins and opening the way to chaotic neural dynamics [51] , [65] – [70] .…”

Section: Methodsmentioning

confidence: 99%

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Cabessa

Villa

2014

PLoS ONE

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We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.

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Interneurons: their cognitive roles - A perspective from dynamical systems view

Fujii

Tsuda²

Proceedings. The 4nd International Conference on Development and Learning, 2005.

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Interneurons may function as coordinator of cortical dynamics, as "super networks" driven by top down signals. Dynamical systems-theoretic view on cortical dynamics and its hypothetical cognitive roles are discussed.In particular, we discuss three related issues concerning the cognitive role of inhibitory interneurons in the neocortex. Theoretical possibility of emergence of transient synchrony in chaotic itinerant dynamics in gap junctioncoupled interneuron networks. 2.Dynamical systems-theoretic, and non-classical memory view based on the concept of Milnor attractor. 3.A new hypothesis on functional role of activity-dependent depolarizing action of GABA A in inhibitory interneurons.

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Neocortical gap junction-coupled interneuron systems may induce chaotic behavior itinerant among quasi-attractors exhibiting transient synchrony

Cited by 4 publications

References 8 publications

Discharge Synchrony during the Transition of Behavioral Goal Representations Encoded by Discharge Rates of Prefrontal Neurons

Discharge Synchrony during the Transition of Behavioral Goal Representations Encoded by Discharge Rates of Prefrontal Neurons

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Interneurons: their cognitive roles - A perspective from dynamical systems view

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