On the Applicability of Spiking Neural Network Models to Solve the Task of Recognizing Gender Hidden in Texts

Two approaches to utilize spiking neural networks, applicable for implementing in neuromorphic hardware with ultra-low power consumption, in the task of recognizing gender of a text author are analyzed. The first one is to obtain synaptic weights for the spiking network by training a formal network. We show the results obtained with this approach. The second one is a creation of a supervised learning algorithm for spiking networks that would be based on biologically plausible plasticity rules. We discuss possible ways to construct such algorithms.