Modeling latency code processing in the electric sense: from the biological template to its VLSI implementation
Modeling latency code processing in the electric sense: from the biological template to its VLSI implementation
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DOI：
10.1088/1748-3190/11/5/055007
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发表时间：
2016-09
影响因子：
3.4
通讯作者：
J. Engelmann;Tim Walther;K. Grant;E. Chicca;L. Gómez-Sena
中科院分区：
文献类型：
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作者：
J. Engelmann;Tim Walther;K. Grant;E. Chicca;L. Gómez-Sena
在自然行为的时间约束下，对感官信息编码的理解还没有得到很好的解决。越来越多的人认为，尖峰计时或延迟编码可以最大限度地利用神经事件的计时来制造快速计算元件，并且这种机制对大脑中的信息处理功能至关重要。鱼的电感觉提供了一个方便的生物模型，在那里可以研究这种编码方案。感觉输入是一种物理上有序的电流密度空间模式，它被编码在初级传入尖峰的精确定时中。加工通路的神经回路是众所周知的，并且该系统表现出最著名的推论放电的图示，这为解码感觉传入延迟模式提供了参考。一个理论模型已被构建从现有的电生理和神经解剖数据整合的神经处理结构的主要特点，并研究与电机命令驱动的必然放电信号的感觉相互作用。这已被用来探索神经编码策略在网络中的连续阶段，并检查模拟网络的能力，以重现输出神经元的反应。该模型表明，网络有能力解决初级传入尖峰的时间差在亚毫秒范围内，这取决于感觉和必然的放电驱动的门控信号的巧合。在网络的整合和输出阶段，推论放电建立了一个积极的背景过滤器，提供时间结构的兴奋和抑制网络内的平衡，然后由感官输入局部调制。这补充了初始的门控机制，并有助于放大输入模式的lavonium，赋予网络超敏度。这些机制使系统具有强大的能力，能够在广泛的工作范围内以高灵敏度提取电子图像的行为上有意义的特征。由于网络在很大程度上取决于尖峰时间，我们最后讨论了其适用于机器人应用程序的神经形态硬件的基础上实施。
Understanding the coding of sensory information under the temporal constraints of natural behavior is not yet well resolved. There is a growing consensus that spike timing or latency coding can maximally exploit the timing of neural events to make fast computing elements and that such mechanisms are essential to information processing functions in the brain. The electric sense of mormyrid fish provides a convenient biological model where this coding scheme can be studied. The sensory input is a physically ordered spatial pattern of current densities, which is coded in the precise timing of primary afferent spikes. The neural circuits of the processing pathway are well known and the system exhibits the best known illustration of corollary discharge, which provides the reference to decoding the sensory afferent latency pattern. A theoretical model has been constructed from available electrophysiological and neuroanatomical data to integrate the principal traits of the neural processing structure and to study sensory interaction with motor-command-driven corollary discharge signals. This has been used to explore neural coding strategies at successive stages in the network and to examine the simulated network capacity to reproduce output neuron responses. The model shows that the network has the ability to resolve primary afferent spike timing differences in the sub-millisecond range, and that this depends on the coincidence of sensory and corollary discharge-driven gating signals. In the integrative and output stages of the network, corollary discharge sets up a proactive background filter, providing temporally structured excitation and inhibition within the network whose balance is then modulated locally by sensory input. This complements the initial gating mechanism and contributes to amplification of the input pattern of latencies, conferring network hyperacuity. These mechanisms give the system a robust capacity to extract behaviorally meaningful features of the electric image with high sensitivity over a broad working range. Since the network largely depends on spike timing, we finally discuss its suitability for implementation in robotic applications based on neuromorphic hardware.