发现神经脉冲控制中突触束数量超限会导致学习崩溃
Synaptic bundle theory for spike-driven sensor-motor system: More than eight independent synaptic bundles collapse reward-STDP learning
- 通过调节感知到运动的独立突触束数量，研究学习稳定性
- 突触束或运动神经元超过临界值时学习必然失败
- 适合研究脉冲神经网络与生物运动控制的学者
神经脉冲直接驱动肌肉，赋予动物敏捷动作，但在人工传感器-运动系统中应用脉冲控制信号会引发学习崩溃。我们构建了一个可调节感知到运动连接中独立突触束数量的系统。本研究揭示四个发现：(i) 当运动神经元数量或独立突触束数量超过临界值时，学习即崩溃；(ii) 运动神经元越少，学习失败概率越高；(iii) 若学习成功，运动神经元越少则学习越快；(iv) 与最优权重方向相反的权重更新次数可定量解释上述现象。目前对脉冲功能的理解仍有限。明确使用脉冲的学习系统参数范围，将使此前因学习困难而无法研究的脉冲功能得以探索。
原文摘要 · Abstract (English)
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.
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