Synaptic bundle theory for spike-driven sensor-motor system: More than eight independent synaptic bundles collapse reward-STDP learning

TL;DR

This study investigates how the number of independent synaptic bundles affects spike-driven learning in sensor-motor systems, revealing critical limits and conditions for successful learning and providing insights into spike functions.

Contribution

It introduces a system allowing variation in synaptic bundle count and identifies critical parameters influencing spike-based learning success and failure.

Findings

Learning collapses beyond a critical number of synaptic bundles.

Fewer motor neurons increase learning failure probability.

Fewer motor neurons lead to faster learning when successful.

Abstract

Neuronal spikes directly drive muscles and endow animals with agile movements, but applying the spike-based control signals to actuators in artificial sensor-motor systems inevitably causes a collapse of learning. We developed a system that can vary \emph{the number of independent synaptic bundles} in sensor-to-motor connections. This paper demonstrates the following four findings: (i) Learning collapses once the number of motor neurons or the number of independent synaptic bundles exceeds a critical limit. (ii) The probability of learning failure is increased by a smaller number of motor neurons, while (iii) if learning succeeds, a smaller number of motor neurons leads to faster learning. (iv) The number of weight updates that move in the opposite direction of the optimal weight can quantitatively explain these results. The functions of spikes remain largely unknown. Identifying the parameter range in which learning systems using spikes can be constructed will make it possible to study the functions of spikes that were previously inaccessible due to the difficulty of learning.