Reconfigurable Computation in Spiking Neural Networks

TL;DR

This paper introduces a reconfigurable spiking neural network model capable of dynamically adjusting the number of active neurons (k) in k-winners-take-all computations through a global parameter, enhancing flexibility and robustness.

Contribution

It presents a novel neural network design that allows dynamic reconfiguration of k in k-winners-take-all tasks using inhibitory pulse-couplings and stable periodic orbit encoding.

Findings

Reconfigurable k-winners-take-all computation achieved.

Robustness to parameter and signal variations demonstrated.

Fast convergence within a few spike emissions per neuron.

Abstract

The computation of rank ordering plays a fundamental role in cognitive tasks and offers a basic building block for computing arbitrary digital functions. Spiking neural networks have been demonstrated to be capable of identifying the largest k out of N analog input signals through their collective nonlinear dynamics. By finding partial rank orderings, they perform k-winners-take-all computations. Yet, for any given study so far, the value of k is fixed, often to k equal one. Here we present a concept for spiking neural networks that are capable of (re)configurable computation by choosing k via one global system parameter. The spiking network acts via pulse-suppression induced by inhibitory pulse-couplings. Couplings are proportional to each units' state variable (neuron voltage), constituting an uncommon but straightforward type of leaky integrate-and-fire neural network. The result of a computation is encoded as a stable periodic orbit with k units spiking at some frequency and others at lower frequency or not at all. Orbit stability makes the resulting analog-to-digital computation robust to sufficiently small variations of both, parameters and signals. Moreover, the computation is completed quickly within a few spike emissions per neuron. These results indicate how reconfigurable k-winners-take-all computations may be implemented and effectively exploited in simple hardware relying only on basic dynamical units and spike interactions resembling simple current leakages to a common ground.