specific neural coding (SNC) forms the basis of information processing in bio-brain, which generates distinct patterns of neuralcoding in response to corresponding exterior forms of stimulus. The performance of SNC i...
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specific neural coding (SNC) forms the basis of information processing in bio-brain, which generates distinct patterns of neuralcoding in response to corresponding exterior forms of stimulus. The performance of SNC is extremely dependent on brain-inspired models. However, the bio-rationality of a brain-inspired model remains inadequate. The purpose of this paper is to investigate a more bio-rational brain-inspired model and the SNC of this brain-inspired model. In this study, we construct a complex spiking neural network (CSNN) in which its topology has the small-word property and the scale-free property. Then, we investigated the SNC of CSNN under various strengths of various stimuli and discussed its mechanism. Our results indicate that (1) CSNN has similar neural time coding under same kind of stimulus;(2) CSNN has significant SNC based on time coding under various exterior stimuli;(3) our discussion implies that the inherent factor of SNC is synaptic plasticity.
specific neural coding is the key to achieving advanced cognitive function in a bio-brain, which can form an identifying coding pattern for external stimulation. The performance of specific neural coding depends extre...
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specific neural coding is the key to achieving advanced cognitive function in a bio-brain, which can form an identifying coding pattern for external stimulation. The performance of specific neural coding depends extremely on brain-like models. However, the bio-interpretability of the topology of a brain-like model is still insufficient. The purpose of this paper is to investigate a more biological interpretative brain-like model verified by the performance of specific neural coding. In this study, we used the topology constrained by human brain functional magnetic resonance imaging (fMRI) to construct a new spiking neural network (SNN) as a brain-like model called fMRI-SNN. In the fMRI-SNN, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with time-delay. Then, we investigated the specific neural coding of fMRI-SNN, and discussed its mechanism. Our results indicated that: (i) fMRI-SNN has obvious specific neural coding based on time coding for different external stimulations. Furthermore, our discussion on relevance analysis implies that the intrinsic element of specific neural coding is synaptic plasticity. (ii) The specific neural coding of fMRI-SNN outperforms that of scale-free SNN and small-world SNN. Furthermore, our discussion on dynamic topological characteristics implies that the network topology is an element that impacts the performance level of the specific neural coding. (iii) Taking a speech recognition task as a case study, the performance of fMRI-SNN outperforms that of scale-free SNN and small-world SNN in terms of speech recognition accuracy.
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