Decoding Methods for Predicting Postsynaptic Responses to Spike Trains

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9421388 Laurence Abbott The nervous system uses electrical spike trains to transport information that is converted into actions at the synapses between motor neurons and muscles. A wide variety of different patterns of the neuronal spike trains arrive at the neuromuscular junction and are converted by the muscle into an equally wide range of muscle contractions. These investigators would like to understand this transduction process and determine what effect changes in the output of the motor neuron have on the muscle response. To do this they will construct a mathematical formalism that allows them to predict the response of the muscle to any spike train. The prediction will be made by linear integration kernels and mathematical neural networks using learning rules that improve the performance of the system over time while a variety of spike trains are delivered to the muscle. The resulting network will then be able to predict the effect of novel spike trains. In addition, the form of the model will provide important information about the biophysical processes underlying the synaptic transduction. The techniques that will be developed can then be applied to synapses between neurons within the nervous system as well as those between neurons and muscles. This work will have an important impact on our understanding of how the brain processes stimuli from the environment and converts them into a resultant behavior.