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.

翻译：神经元脉冲直接驱动肌肉并赋予动物敏捷的运动能力，但将基于脉冲的控制信号应用于人工传感-运动系统的执行器时，不可避免地会导致学习崩溃。我们开发了一种能够改变传感-运动连接中独立突触束数量的系统。本文论证了以下四个发现：(i) 一旦运动神经元数量或独立突触束数量超过临界极限，学习即告崩溃。(ii) 运动神经元数量较少会增加学习失败的概率，而(iii) 若学习成功，较少的运动神经元数量会带来更快的学习速度。(iv) 沿最优权重反方向移动的权重更新次数能够定量解释这些结果。脉冲的功能在很大程度上仍属未知。界定使用脉冲的学习系统可构建的参数范围，将使得研究那些因学习困难而此前无法探究的脉冲功能成为可能。