Jin Dezhe Z
Howard Hughes Medical Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 1):021905. doi: 10.1103/PhysRevE.69.021905. Epub 2004 Feb 26.
Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons.
许多脑区的感觉神经元会以精确的时间对具有时间结构的刺激产生放电，并将时间上复杂的刺激编码为时空放电。下游神经元如何解读这种神经编码是一个重要的未解决问题。在本文中，我们描述了一种使用脉冲循环神经网络的解码方案。该网络由形成同步发放链的兴奋性神经元，以及两种不同类型的全局抑制性中间神经元组成，它们分别提供延迟的前馈抑制和快速的反馈抑制。当同步发放链上最后一个兴奋性神经元放电时，网络发出对特定时空序列的识别信号，当且仅当该序列出现在输入脉冲流中时才会发生这种情况。识别方案对于输入脉冲之间在一定范围内的间隔变化具有不变性。网络的计算可以映射到有限状态机的计算中。我们的网络提供了一种用不同类型神经元解码时空脉冲的简单方法。
