Voelker Aaron R, Eliasmith Chris
Centre for Theoretical Neuroscience and David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Neural Comput. 2018 Mar;30(3):569-609. doi: 10.1162/neco_a_01046. Epub 2017 Dec 8.
Researchers building spiking neural networks face the challenge of improving the biological plausibility of their model networks while maintaining the ability to quantitatively characterize network behavior. In this work, we extend the theory behind the neural engineering framework (NEF), a method of building spiking dynamical networks, to permit the use of a broad class of synapse models while maintaining prescribed dynamics up to a given order. This theory improves our understanding of how low-level synaptic properties alter the accuracy of high-level computations in spiking dynamical networks. For completeness, we provide characterizations for both continuous-time (i.e., analog) and discrete-time (i.e., digital) simulations. We demonstrate the utility of these extensions by mapping an optimal delay line onto various spiking dynamical networks using higher-order models of the synapse. We show that these networks nonlinearly encode rolling windows of input history, using a scale invariant representation, with accuracy depending on the frequency content of the input signal. Finally, we reveal that these methods provide a novel explanation of time cell responses during a delay task, which have been observed throughout hippocampus, striatum, and cortex.
构建脉冲神经网络的研究人员面临着一项挑战，即在保持对网络行为进行定量表征能力的同时，提高其模型网络的生物合理性。在这项工作中，我们扩展了神经工程框架（NEF）背后的理论，这是一种构建脉冲动态网络的方法，以允许使用广泛的突触模型，同时在给定阶数内保持规定的动态特性。该理论增进了我们对低级突触特性如何改变脉冲动态网络中高级计算准确性的理解。为了完整性，我们为连续时间（即模拟）和离散时间（即数字）模拟都提供了表征。我们通过使用突触的高阶模型将最优延迟线映射到各种脉冲动态网络上来证明这些扩展的实用性。我们表明，这些网络使用尺度不变表示对输入历史的滚动窗口进行非线性编码，其准确性取决于输入信号的频率内容。最后，我们揭示这些方法为在延迟任务期间的时间细胞反应提供了一种新的解释，这种反应在整个海马体、纹状体和皮层中都有观察到。
