Neural information coding on small-world spiking neuronal networks modulated by spike-timing-dependent plasticity under external noise stimulation

Guo, Lei; Zhang, Wei; Zhang, Jialei

doi:10.1007/s10586-017-1188-6

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Etémé et al found that electromagnetic stimulation induces not only regular firing activity of the neuron with spiking and bursting regimes but also synchronous neuronal modes in neural network under magnetic stimulation [35]. In our previous work, the responses of the time coding and the rate coding of the smallworld SNN both showed respective specificity under white Gaussian noise and impulse noise [36]. The study of the noise suppression ability of the SNN based on synaptic plasticity is still in the stage of exploration.…”

Section: Introductionmentioning

confidence: 89%

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Guo

Kan

et al. 2020

PLoS ONE

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With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

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Section: Introductionmentioning

confidence: 89%

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Guo

Kan

et al. 2020

PLoS ONE

Self Cite

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“…Spiking neural networks (SNNs) have been increasingly popular in recent years due to their relationship to dynamics in human brain and enhanced biological relevance [3]. The SNNs with cognitive and motor functions, such as the hippocampal network, have been investigated with abundant in vitro and model-based experiments [4,5]. One of the most essential components of the human brain is the hippocampus, which processes short-term memory and spatial navigation information [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence

et al. 2020

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In this paper, an expanded digital hippocampal spurt neural network (HSNN) is innovatively proposed to simulate the mammalian cognitive system and to perform the neuroregulatory dynamics that play a critical role in the cognitive processes of the brain, such as memory and learning. The real-time computation of a large-scale peak neural network can be realized by the scalable on-chip network and parallel topology. By exploring the latest research in the field of neurons and comparing with the results of this paper, it can be found that the implementation of the hippocampal neuron model using the coordinate rotation numerical calculation algorithm can significantly reduce the cost of hardware resources. In addition, the rational use of on-chip network technology can further improve the performance of the system, and even significantly improve the network scalability on a single field programmable gate array chip. The neuromodulation dynamics are considered in the proposed system, which can replicate more relevant biological dynamics. Based on the analysis of biological theory and the theory of hardware integration, it is shown that the innovative system proposed in this paper can reproduce the biological characteristics of the hippocampal network and may be applied to brain-inspired intelligent subjects. The study in this paper will have an unexpected effect on the future research of digital neuromorphic design of spike neural network and the dynamics of the hippocampal network.

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Encoding specificity of scale-free spiking neural network under different external stimulations

Guo

Hou

et al. 2020

Neurocomputing

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Neural information coding on small-world spiking neuronal networks modulated by spike-timing-dependent plasticity under external noise stimulation

Cited by 3 publications

References 25 publications

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence

Encoding specificity of scale-free spiking neural network under different external stimulations

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