Self-organization of a neural network with heterogeneous neurons enhances coherence and stochastic resonance

摘要: Most network models for neural behavior assume a predefined topology and consist of almost identical elements exhibiting little heterogeneity. In this paper, we propose self-organized consisting heterogeneous neurons with different behaviors or degrees excitability. The synaptic connections evolve according to the spike-timing dependent plasticity mechanism finally sparse active-neuron-dominant structure is observed. That is, strong are mainly distributed synapses from active inactive ones. We argue that self-emergent essentially reflects competition encodes This shown significantly enhance coherence resonance stochastic entire network, indicating its high efficiency in information processing.