Partial synchronization of neural activity and information processing

Authors:
R. Borisyuk;D. Chik;Y. Kazanovich
Affiliations:
Centre for Theoretical and Computational Neuroscience, University of Plymouth, Plymouth, UK;Centre for Theoretical and Computational Neuroscience, University of Plymouth, Plymouth, UK;Institute of Mathematical Problems in Biology, Russian Academy of Sciences, Pushchino, Russia
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 8
Cited 0

The brain binds entities and events by multiregional activation from convergence zones

Neural Computation
Dynamics of neural networks with a central element

Neural Networks
Object selection based on oscillatory correlation

Neural Networks
A neurodynamical model for selective visual attention using oscillators

Neural Networks
Oscillatory model of attention-guided object selection and novelty detection

Neural Networks
Transient Synchrony of Distant Brain Areas and Perceptual Switching in Ambiguous Figures

Biological Cybernetics
Visual Grouping and the Focusing of Attention Induce Gamma-band Oscillations at Different Frequencies in Human Magnetoencephalogram Signals

Journal of Cognitive Neuroscience
Locally excitatory globally inhibitory oscillator networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study dynamics of neural activity in brain-inspired neural networks which comprise both low and high layers of information processing. Information propagates from the low layer which includes "peripheral neurons" (PNs), and the dynamics is controlled by the feedback from the higher layer of "central neurons" (CNs). We use the Hodgkin-Huxley type model to describe spike generation properties of neural elements. Synaptic connections are of excitatory and inhibitory type and some of them have fixed connection strengths and some are adjustable according to Hebbian type learning rule. The regime of partial synchronization between spiking activity of the CNs and PNs has been found. It is shown that PNs with higher firing rates are selected preferentially by the central neurons. In the case of local connections between PNs, we have found that local excitatory connections facilitate synchronization; while local inhibitory connections help distinguishing two groups of PNs with similar intrinsic frequencies. We hypothesize that the regime of partial synchronization can be used to simulate neural mechanisms of perception and attention. In particular, sequential selection of stimuli simultaneously present in the visual scene is demonstrated by the model which deals with a real image in the frequency domain.