Dual Information Representation with Stable Firing Rates and Chaotic Spatiotemporal Spike Patterns in a Neural Network Model

Authors:
Osamu Araki;Kazuyuki Aihara
Affiliations:
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa 923-1292, Japan.;Department of Complexity Science and Engineering, University of Tokyo, Bunkyoku, Tokyo 113-8656, Japan, and CREST, Japan Science and Technology Corporation, Kawaguchi, Saitama 332-0012, Japan.
Venue:
Neural Computation
Year:
2001

Citing 10
Cited 4

Simulation of chaotic EEG patterns with a dynamic model of the olfactory system

Biological Cybernetics
Dynamics of functional coupling in the cerebral cortex: an attempt at a model-based interpretation

Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
What matters in neuronal locking?

Neural Computation
Dynamical cell assembly hypothesis—theoretical possibility of spatio-temporal coding in the cortex

Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Chaos and asymptotical stability in discrete-time neural networks

Physica D
Role of temporal integration and fluctuation detection in the highly irregular firing of a leaky integrator neuron model with partial reset

Neural Computation
Associative dynamics in a chaotic neural network

Neural Networks
Chaos in neural networks composed of coincidence detector neurons

Neural Networks
Chaotic balanced state in a model of cortical circuits

Neural Computation
Fast global oscillations in networks of integrate-and-fire neurons with low firing rates

Neural Computation

Duality of rate coding and temporal coding in multilayered feedforward networks

Neural Computation
Ergodicity of spike trains: when does trial averaging make sense?

Neural Computation
Self-organizing dual coding based on spike-time-dependent plasticity

Neural Computation
An asynchronous spiking chaotic neuron integrated circuit

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Although various means of information representation in the cortex have been considered, the fundamental mechanism for such representation is not well understood. The relation between neural network dynamics and properties of information representation needs to be examined. We examined spatial pattern properties of mean firing rates and spatiotemporal spikes in an interconnected spiking neural network model. We found that whereas the spatiotemporal spike patterns are chaotic and unstable, the spatial patterns of mean firing rates (SPMFR) are steady and stable, reflecting the internal structure of synaptic weights. Interestingly, the chaotic instability contributes to fast stabilization of the SPMFR. Findings suggest that there are two types of network dynamics behind neuronal spiking: internally-driven dynamics and externally driven dynamics. When the internally driven dynamics dominate, spikes are relatively more chaotic and independent of external inputs; the SPMFR are steady and stable. When the externally driven dynamics dominate, the spiking patterns are relatively more dependent on the spatiotemporal structure of external inputs. These emergent properties of information representation imply that the brain may adopt a dual coding system. Recent experimental data suggest that internally driven and externally driven dynamics coexist and work together in the cortex.