Match-mismatch detection neural circuit based on multistable neurons

Abstract

An ability to detect matches and mismatches between the real world and its representation are both crucial for intelligent autonomous systems to work efficiently. Here we recall our previously proposed model of the neuron which activation characteristic (dependence between an input pattern and output signal) depends on the value of the modulation parameter and varies from a smooth sigmoid-like function to the form of a quasi-rectangular hysteresis loop. Then we propose the neural circuit based on this model of multistable hysteresis neuron. Such a neural circuit can compare expectations represented by downward signals and reality represented by upward signals. In case of matching between them the neural circuit transfers both up and downward signals. In another case, internal inhibitory neurons are activated, and transferring becomes blocked until the expected conditions are met. Besides, the changes in the modulation parameter allow fine-tuning the behavior of this neural circuit. In the end, the results of the numerical simulation are presented.