Dynamic Neural Mechanisms for Recognizing Spike Trains
Dynamic neural networks are designed to discuss how the dynamic mechanisms in the neurons and synapses work in recognizing interspike intervals (ISIs). The threshold integration of post-synaptic membrane potentials, the refractory period of neurons, together with the spike-time-dependent plasticity (STDP) learning rule are discussed. Based on these dynamic mechanisms, the input inter-spike interval sequences are decomposed into isolated spikes. The synaptic delay times modulated by STDP learning rule is the key mechanism in the ISIs recognition, based on which the ISIs are learned and saved in the delay times. After learning, the neural networks could recognize whether different input sequences include the same consecutive ISIs.