Neural waves and computation in a neural net model III: preplay, working memory and bursts.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
S A Selesnick
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引用次数: 0

Abstract

Evidence in favor of an earlier conjecture, namely that the low frequency autonomic regime of neural waves acts as a governing or operating system, processing incoming stimuli in various ways for the purposes of conducting computations, is presented in the context of our network model. The rôle of this low frequency regime in the implementation of preplay compares favorably with recent experimental findings in mice. This is followed by a discussion and analysis of three problems arising from considerations of Working Memory processes. Namely, distinguishability, garbage collection and distractor avoidance. The rôle of inhibitory bursts arises spontaneously in the last two scenarios.

神经网络模型中的神经波和计算III:预演,工作记忆和突发。
在我们的网络模型的背景下,提出了支持早期猜想的证据,即神经波的低频自主机制作为一个控制或操作系统,以各种方式处理传入的刺激以进行计算。这种低频状态在预播放中的rôle效果与最近在小鼠身上的实验结果比较有利。接下来是对工作记忆过程中产生的三个问题的讨论和分析。即,可区分性,垃圾收集和干扰避免。在后两种情况下,自发地产生了rôle抑制爆发。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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