Computing threshold functions using dendrites

TL;DR

This paper introduces the non-Linear Threshold Unit (nLTU), a neuron model that uses dendritic subunits to compute threshold functions more efficiently, reducing weight precision requirements and increasing computational capacity.

Contribution

The paper presents the nLTU model that leverages dendritic subunits to compute threshold functions with lower weight precision and greater functional diversity than traditional LTUs.

Findings

nLTU computes all threshold functions with smaller weight precision

nLTU can compute more functions than LTU with single synapse inputs

nLTU enables networks with binary synapses

Abstract

Neurons, modeled as linear threshold unit (LTU), can in theory compute all thresh- old functions. In practice, however, some of these functions require synaptic weights of arbitrary large precision. We show here that dendrites can alleviate this requirement. We introduce here the non-Linear Threshold Unit (nLTU) that integrates synaptic input sub-linearly within distinct subunits to take into account local saturation in dendrites. We systematically search parameter space of the nTLU and TLU to compare them. Firstly, this shows that the nLTU can compute all threshold functions with smaller precision weights than the LTU. Secondly, we show that a nLTU can compute significantly more functions than a LTU when an input can only make a single synapse. This work paves the way for a new generation of network made of nLTU with binary synapses.