The Neurosciences Institute San Diego, CA, USA.
Front Neurorobot. 2013 Jun 6;7:10. doi: 10.3389/fnbot.2013.00010. eCollection 2013.
Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.
动物行为通常涉及从经验中学习的按时间顺序排列的动作序列。在这里，我们描述了连接的放电神经元网络的模拟，这些网络学会了以正确的时间顺序产生活动模式。该模拟由数千个表现出短期突触可塑性和尖峰时间依赖型突触可塑性的兴奋性和抑制性神经元的大规模网络组成。每个区域内的神经结构被安排为引发持续数十毫秒的全峰竞争（WTA）神经活动模式。为了以正确的时间顺序生成和切换连续的发射模式，需要在这些区域之间进行信号的再传入交换。为了展示这种排列的能力，我们使用模拟来训练基于大脑的设备，该设备通过自主产生运动动作的时间序列来响应视觉输入。
