Baram Yoram
Computer Science Department, Technion- Israel Institute of Technology, 32000 Haifa, Israel.
Cogn Neurodyn. 2020 Feb;14(1):125-135. doi: 10.1007/s11571-019-09552-x. Epub 2019 Aug 21.
Neuronal membrane and synapse polarities have been attracting considerable interest in recent years. Certain functional roles for such polarities have been suggested, yet, they have largely remained a subject for speculation and debate. Here, we note that neural circuit polarity codes, defined as sets of polarity permutations, divide into primal-size circuit polarity subcodes, which, sharing certain connectivity attributes, are called Two long-debated, seemingly competing paradigms of neuronal self-feedback, namely, axonal discharge and synaptic mediation, are shown to jointly define the distinction between these categories. However, as the second paradigm contains the first, it is mathematically sufficient for complete specification of all categories. The analysis of primal-size circuit polarity categories is found to reveal, explain and extend experimentally observed cortical information capacity values termed "magical numbers", associated with "working memory". While these have been previously argued on grounds of psychological experiments, here they are further supported on analytic grounds by the so-called Hebbian memory paradigm. The information dimensionality associated with these capacities is found to be a consequence of prime factorization of composite circuit polarity code sizes. Different categories of circuit polarity, identical in size and neuronal parameters, are shown to generate different firing rate dynamics.
近年来,神经元膜和突触极性一直备受关注。人们已经提出了这些极性的某些功能作用,但在很大程度上,它们仍然是推测和争论的主题。在这里,我们注意到,神经回路极性编码(定义为极性排列集)分为原始大小的回路极性子编码,这些子编码具有某些共同的连接属性,被称为 长期以来备受争议的两种看似相互竞争的神经元自我反馈范式,即轴突放电和突触介导,被证明共同定义了这些类别之间的区别。然而,由于第二种范式包含第一种范式,从数学上来说,它足以完整地指定所有类别。对原始大小的回路极性类别的分析表明,它揭示、解释并扩展了实验观察到的与“工作记忆”相关的皮质信息容量值,即所谓的“神奇数字”。虽然这些值此前已基于心理学实验进行了论证,但在这里,它们通过所谓的赫布记忆范式在分析基础上得到了进一步支持。发现与这些容量相关的信息维度是复合回路极性编码大小的质因数分解的结果。大小和神经元参数相同的不同类别的回路极性被证明会产生不同的放电率动态。