Decoding the Neural Activity of Recurrent Neural Network Playing a Reflexive Game

Abstract

We demonstrate the possibility of identification of certain stimuli time series, which is received by simple recurrent neural network while playing a reflexive game “Even-Odd” (Matching Pennies), using its neural activity patterns. For successful identification by the method of neural network-based decoding, a non-linear decoder with at least 6 neurons on the hidden layer is required. This result indicates the presence of attractors of neural activity, which allow the trained recurrent neural network to determine the type of the received stimuli sequence and form the right response.