Bistable memory and binary counters in spiking neural network

Abstract

Information can be encoded in spiking neural network (SNN) by precise spike-time relations. This hypothesis can explain cell assembly formation, such as polychronous group (PNG), a notion created to explain how groups of neurons fire time-locked to each other, not necessarily synchronously. In this paper we present a set of PNGs capable of retaining triggering events in bistable states. Triggering events may be data or computational controls. Both, data and control signals are memorized as a result of intrinsic operational PNG attributes, and no neural plasticity mechanisms are involved. This behavior can be fundamental for several computational operations in SNNs. It is shown how bistable neural pools can perform tasks such as binary and stack-like counting, and how they can realize hierarchical organization in parallel computing.