Information coding on the basis of synchronization of neuronal activity

Borisyuk, Roman; Borisyuk, Galina

doi:10.1016/0303-2647(96)01624-3

Cited by 35 publications

(19 citation statements)

References 5 publications

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“…It is interesting to compare the above results with those on spontaneous attention switching reported in Borisyuk and Borisyuk (1997). The common character for their work and ours is the employment of phase oscillators in attention models.…”

Section: Selective Attentionmentioning

confidence: 63%

“…It can be seen that our results simulated the top-down mechanism, while their work concentrated on the attention shifting caused by the bottom-up mechanism. In addition, when attention shifting occurs in Borisyuk and Borisyuk (1997), the average frequency of the central oscillator is always drawn to the average frequency of the targetted group of cortical oscillators. The situation is otherwise in our results, i.e., the frequencies of the targetted COs are bound to the synchronization frequency of the HOs.…”

Section: Selective Attentionmentioning

confidence: 99%

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Selective visual attention in a neurocomputational model of phase oscillators

Guo

1999

Biological Cybernetics

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In order to understand the dynamic property of covert selective visual attention, which is different from the proposed mechanism of the spotlight metaphor, a two-layered network of phase oscillators was developed. The first layer is related to the hippocampus and controls attention focus formation. The second layer is related to the visual cortex, and each cortical oscillator in it simulates an assembly of cells coding for a particular stimulus in the sense of feature binding. Selective visual attention is interpreted as the result of the emergent synchronization of hippocampus oscillators and a part of cortical oscillators. Numerical experiments are presented to illustrate attention focus formation and attention shifting from one set of stimuli to another. From a neurocomputational point of view, our results demonstrate that attention is an emergent property of the dynamical cell assemblies responding to the whole visual field.

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Section: Selective Attentionmentioning

confidence: 63%

Section: Selective Attentionmentioning

confidence: 99%

Selective visual attention in a neurocomputational model of phase oscillators

Guo

1999

Biological Cybernetics

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“…Biologically plausible dynamical models of neural systems have been developed for example in [7], [8], [9], [10], [11]. Chaotic models have been established also in the field of computational neural networks [12], [13], [14], [15], [16], [17]. These works emphasized chaos control, which meant the suppression of chaos in the models [18], [19].…”

Section: A Connectionist Models Of Spatio-temporal Neural Dynamicsmentioning

confidence: 99%

Chaotic Neurodynamics for Autonomous Agents

Harter

Kozma

2005

IEEE Trans. Neural Netw.

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Abstract-Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limitcycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multi-layer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.

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“…In recent years promising results obtained in nonlinear dynamics, and neurophysiology predetermined formation of oscillator paradigm that combines knowledge from neurobiology, molecular physics, electronics, chaos and synchronization theories [5,8,14,16]. One of most perspective techniques that apply internal synchronization of coupled chaotic oscillators to clustering is described in [1].…”

Section: Oscillator Paradigmmentioning

confidence: 99%

Self-organized Clustering and Classification: A Unified Approach via Distributed Chaotic Computing

Benderskaya

Zhukova

2011

Advances in Intelligent and Soft Computing

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Abstract. The paper describes a unified approach to solve clustering and classification problems by means of oscillatory neural networks with chaotic dynamics. It is discovered that self-synchronized clusters once formed can be applied to classify objects. The advantages of distributed clusters formation in comparison to centers of clusters estimation are demonstrated. New approach to clustering on-the-fly is proposed.

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Information coding on the basis of synchronization of neuronal activity

Cited by 35 publications

References 5 publications

Selective visual attention in a neurocomputational model of phase oscillators

Selective visual attention in a neurocomputational model of phase oscillators

Chaotic Neurodynamics for Autonomous Agents

Self-organized Clustering and Classification: A Unified Approach via Distributed Chaotic Computing

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