Robust encoding by collective bursting in biologically plausible neural networks

2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353) Pub Date : 2000-05-28 DOI:10.1109/ISCAS.2000.856155

D. Blank, A. Kern, R. Stoop

引用次数: 0

Abstract

We describe a novel type of bursting that we observe in simulations of large recurrent networks of biophysically plausible, intrinsically non-bursting neurons. The mechanism responsible for the bursting is a combination of excitatory feedback received from neighbouring neurons, together with an activity-dependent adaptation mechanism that slows down spiking. This collective bursting is shown to encode external inputs in the intervals between bursts. The interspike intervals during each burst are irregular and have a high output rate that is insensitive to the input strength. The encoding is reliable and precise, even when individual neurons have imperfect, varying properties and is robust to failure of large numbers of neurons.

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在生物学上似是而非的神经网络中通过集体爆发实现的鲁棒编码

我们描述了一种新型的爆发，我们在模拟生物物理上合理的、本质上不爆发的神经元的大型循环网络中观察到。产生脉冲的机制是来自邻近神经元的兴奋性反馈，以及一种活动依赖的适应机制，这种机制可以减缓脉冲。这种集体爆发显示在爆发之间的间隔对外部输入进行编码。在每次突发期间的尖峰间隔是不规则的，并且具有对输入强度不敏感的高输出速率。这种编码是可靠和精确的，即使单个神经元有不完美的、不同的特性，并且对大量神经元的故障具有鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)

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