This study is an attempt to answer the question – what kind of spiking neural networks could efficiently transform rate-coded input signal into temporally coded form – specific activity of neuronal groups with strictly fixed temporal delays between spikes emitted by different neurons in every group. Since theoretical approach to the solution of this problem appears to be very hard or impossible the network configurations performing this task efficiently were found by means of genetic algorithm. Exploration of their structure showed that while excitatory neurons form the groups with stereotypical firing patterns, the inhibitory neurons of the network make these patterns specific for different rate-coded stimuli and, thus, play the key role in conversion of rate-coded input signal to temporal code.

Cite this paper as: Kiselev M.V. (2016) Conversion from Rate Code to Temporal Code – Crucial Role of Inhibition. In: Cheng L., Liu Q., Ronzhin A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science, vol 9719. Springer, Cham

Published as a part of the International Symposium on Neural Networks

© Springer International Publishing Switzerland 2016

Available from: https://link.springer.com/chapter/10.1007%2F978-3-319-40663-3_76