Learning Spike time codes through Morphological Learning with Binary Synapses

TL;DR

This paper introduces a morphological learning algorithm for a neuron model with nonlinear dendrites and binary synapses, enabling temporal feature learning and robust hardware implementation, with comparable accuracy to multi-bit synapse models.

Contribution

The paper presents a novel morphological learning algorithm for NNLD neurons with binary synapses, including automatic threshold adaptation, suitable for hardware implementation.

Findings

Binary synapses achieve similar accuracy to multi-bit synapses in pattern classification.

The proposed method is robust to hardware variations.

Successful application to tactile sensing spike classification.

Abstract

In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses that is able to learn temporal features of spike input patterns is considered. Since binary synapses are considered, learning happens through formation and elimination of connections between the inputs and the dendritic branches to modify the structure or "morphology" of the NNLD. A morphological learning algorithm inspired by the 'Tempotron', i.e., a recently proposed temporal learning algorithm-is presented in this work. Unlike 'Tempotron', the proposed learning rule uses a technique to automatically adapt the NNLD threshold during training. Experimental results indicate that our NNLD with 1-bit synapses can obtain similar accuracy as a traditional Tempotron with 4-bit synapses in classifying single spike random latency and pair-wise synchrony patterns. Hence, the proposed method is better suited for robust hardware implementation in the presence of statistical variations. We also present results of applying this rule to real life spike classification problems from the field of tactile sensing.