Pulse Coupled Neural Network Modeling of Firings in Hippocampus CA3

The hippocampus has been the focus of many researches over the last decades. The aim of this study is to simulate firings in hippocampus CA3 area with pulse coupled neural network (PCNN). The model consists of 120 neurons, of which the ratio of excitatory to inhibitory neuron is 5 to 1. The weight parameter is set according to Gaussian distribution. Results show that for the three different inputs: sinusoidal input, rectangular pulse, and the sum of the above inputs, average population firings rate is less than 10%; the sparse connectivity among neurons can be adjusted by weight matrix. We may come to the conclusions that: (1) Under three different types of inputs, the mean activity level of PCNN is less than 10%, which satisfies the sparse coding of hippocampus CA3; (2) The connectivity of the neurons is adjusted by the synaptic weight matrix. It satisfies the sparse connectivity of hippocampus neuron; (3) PCNN outputs different time series according to the input, which may be further used in future coding studies.