Neural waves and computation in a neural net model I: Convolutional hierarchies.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2024-02-01 Epub Date: 2024-02-21 DOI:10.1007/s10827-024-00866-2
Stephen Selesnick
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Abstract

The computational resources of a neuromorphic network model introduced earlier are investigated in the context of such hierarchical systems as the mammalian visual cortex. It is argued that a form of ubiquitous spontaneous local convolution, driven by spontaneously arising wave-like activity-which itself promotes local Hebbian modulation-enables logical gate-like neural motifs to form into hierarchical feed-forward structures of the Hubel-Wiesel type. Extra-synaptic effects are shown to play a significant rôle in these processes. The type of logic that emerges is not Boolean, confirming and extending earlier findings on the logic of schizophrenia.

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神经网络模型中的神经波和计算 I:卷积分层。
本文以哺乳动物视觉皮层等分层系统为背景,研究了早先引入的神经形态网络模型的计算资源。研究认为,在自发产生的波状活动驱动下,一种无处不在的自发局部卷积形式--它本身促进了局部海比调制--使逻辑门状神经图案形成了胡贝尔-维塞尔类型的分层前馈结构。突触外效应在这些过程中发挥了重要作用。出现的逻辑类型不是布尔逻辑,这证实并扩展了早先关于精神分裂症逻辑的研究结果。
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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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