Dynamics of map-based neuronal network with modified spike-timing-dependent plasticity
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The effect of adaptive coupling is studied in a neural network of randomly-coupled Rulkov maps. As an adaptive mechanism, we propose a modified spike-timing-dependent plasticity (STDP) rule with implemented homeostatic property. The comparison of the results of classical and modified STDP shows that the implication of homeostatic property results in significant changes in the network dynamics. Moreover, the neural network with modified STPD demonstrates much more pronounced dynamical changes when internal noise and stimulus amplitudes are varied. The use of the modified rule also leads to decreasing coherence and characteristic correlation time in the system.
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