Spiking neural circuits with dendritic stimulus processors : encoding, decoding, and identification in reproducing kernel Hilbert spaces.
Spiking neural circuits with dendritic stimulus processors : encoding, decoding, and identification in reproducing kernel Hilbert spaces.
复制标题
具有树突刺激处理器的尖峰神经电路：在再现内核希尔伯特空间中进行编码、解码和识别。
DOI：
10.1007/s10827-014-0522-8
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发表时间：
2015
影响因子：
1.2
通讯作者：
Slutskiy,YevgeniyB
中科院分区：
文献类型：
--
作者：
Lazar,AurelA;Slutskiy,YevgeniyB
我们提出了一种多输入多输出神经电路结构，用于在锋电位域对刺激进行非线性处理和编码。在这种架构中，一组树突状刺激处理器实现多个时间或时空信号的非线性变换，例如模拟域中的尖峰序列或听觉和视觉刺激。树突刺激处理器可以作用于单个刺激和刺激组，从而执行由于同时接收的信号之间的交互而产生的复杂计算。模拟域计算的结果，然后编码成一个多维的尖峰序列的尖峰神经元建模为非线性动力系统的人口。我们调查的一般条件下，这样的电路忠实地代表刺激和演示算法（一）刺激恢复，或解码，和（ii）识别树突状刺激处理器从观察到的尖峰。两者合计，我们的研究结果表明，一个单一的神经元的树突状刺激处理器的识别和编码的刺激与一个银行的树突状刺激处理器的神经元的人口之间的基本的二元性。这个对偶结果使我们能够推导出要执行的实验数量的下限和识别神经回路所需记录的尖峰总数。
We present a multi-input multi-output neural circuit architecture fornonlinearprocessing and encoding of stimuli in the spike domain. In this architecture a bank of dendritic stimulus processors implements nonlinear transformations of multiple temporal or spatio-temporal signals such as spike trains or auditory and visual stimuli in the analog domain. Dendritic stimulus processors may act on both individual stimuli and on groups of stimuli, thereby executing complex computations that arise as a result of interactions between concurrently received signals. The results of the analog-domain computations are then encoded into a multi-dimensional spike train by a population of spiking neurons modeled as nonlinear dynamical systems. We investigate general conditions under which such circuits faithfully represent stimuli and demonstrate algorithms for (i) stimulus recovery, or decoding, and (ii) identification of dendritic stimulus processors from the observed spikes. Taken together, our results demonstrate a fundamental duality between the identification of the dendritic stimulus processor of a single neuron and the decoding of stimuli encoded by a population of neurons with a bank of dendritic stimulus processors. This duality result enabled us to derive lower bounds on the number of experiments to be performed and the total number of spikes that need to be recorded for identifying a neural circuit.