The cerebellum input stage has been known to perform combinatorial operations   on input signals. In this paper, we developed a model to study information transmission and signal recoding in the cerebellar granular layer and to test observations like center-surround organization and time-window hypothesis  . We also developed simple neuron models for abstracting timing phenomena in large networks. Detailed biophysical models were used to study synaptic plasticity and its effect in generation and modulation of spikes in the granular layer network. Our results indicated that spatio-temporal information transfer through the granular network is controlled by synaptic inhibition . Spike amplitude and number of spikes were modulated by L TP and LTD. Both in vitro and in vivo simulations indicated that inhibitory input via Golgi cells acts as a modulator and regulates the post synaptic excitability. © 2010 IEEE.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@363e061 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@12200204 Through org.apache.xalan.xsltc.dom.DOMAdapter@73645de7; Conference Code:83128
C. Medini, S. Subramaniyam, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Modeling cerebellar granular layer excitability and combinatorial computation with spikes”, in Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010, Changsha, 2010, pp. 1495-1503.