Computational models for generic cortical microcircuits
Computational models for generic cortical microcircuits
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DOI：
10.1201/9780203494462.ch18
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
W. Maass;T. Natschläger;H. Markram
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文献类型：
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作者：
W. Maass;T. Natschläger;H. Markram
人类神经系统处理来自快速变化的环境的连续多模态输入流。神经建模的一个关键挑战是解释神经微电路（柱，微柱等）如何在神经网络中运行。在大脑皮层中，解剖学和生理学结构在许多大脑区域和物种中非常相似，它们完成了这一巨大的计算任务。我们提出了一个计算模型，可以解释潜在的通用计算能力，并不需要一个任务依赖的神经回路的建设。相反，它是基于高维动力系统的原理与统计学习理论相结合，并可以在通用的进化或发现循环电路上实现。这种在微观层面上理解神经计算的新方法也提出了在更大的神经系统中建模认知处理的新方法。特别是，它质疑了传统的神经编码思维方式。
The human nervous system processes a continuous stream of multi-modal input from a rapidly changing environment. A key challenge for neural modeling is to explain how the neural microcircuits (columns, minicolumns, etc.) in the cerebral cortex whose anatomical and physiological structure is quite similar in many brain areas and species achieve this enormous computational task. We propose a computational model that could explain the potentially universal computational capabilities and does not require a task-dependent construction of neural circuits. Instead it is based on principles of high dimensional dynamical systems in combination with statistical learning theory, and can be implemented on generic evolved or found recurrent circuitry. This new approach towards understanding neural computation on the micro-level also suggests new ways of modeling cognitive processing in larger neural systems. In particular it questions traditional ways of thinking about neural coding.