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A COLUMNAR MODEL OF SOMATOSENSORY REORGANIZATIONAL PLASTICITY BASED ON HEBBIAN AND NON-HEBBIAN LEARNING RULES

机译:基于Hebbian和非Hebbian学习规则的体感组织可塑性柱模型

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Topographical and functional aspects of neuronal plasticity were studied in the primary somatosensory cortex of adult rats in acute electrophysiological experiments. Under these experimental conditions, we observed short-term reversible reorganization induced by intracortical microstimulation or by an associative pairing of peripheral tactile stimulation. Both types of stimulation generate large-scale and reversible changes of the representational topography and of single cell functional properties. We present a model to simulate the spatial and functional reorganizational aspects of this type of short-term and reversible plasticity. The columnar structure of the network architecture is described and discussed from a biological point of view. The simulated architecture contains three main levels of information processing. The first one is a sensor array corresponding to the sensory surface of the hind paw. The second level, a pre-cortical relay cell array, represents the thalamo-cortical projection with different levels of excitatory and inhibitory relay cells and inhibitory nuclei. The array of cortical columns, the third level, represents stellate, double bouquet, basket and pyramidal cell interactions. The dynamics of the network are ruled by two integro-differential equations of the lateral-inhibition type. In order to implement neuronal plasticity, synaptic weight parameters in those equations are variables. The learning rules are motivated by the original concept of Hebb, but include a combination of both Hebbian and non-Hebbian rules, which modifies different intra- and inter-columnar interactions. We discuss the implications of neuronal plasticity from a behavioral point of view in terms of information processing and computational resources. [References: 76]
机译:在急性电生理实验中,在成年大鼠的主要体感皮层中研究了神经元可塑性的形貌和功能方面。在这些实验条件下,我们观察到由皮层内微刺激或周围触觉刺激的配对引起的短期可逆重组。两种类型的刺激都会产生代表性形貌和单细胞功能特性的大规模且可逆的变化。我们提出一个模型来模拟这种短期和可逆可塑性的空间和功能重组方面。从生物学的角度描述和讨论了网络体系结构的柱状结构。模拟的体系结构包含三个主要级别的信息处理。第一个是对应于后爪的感觉表面的传感器阵列。第二层是皮质前中继细胞阵列,代表具有不同水平的兴奋性和抑制性中继细胞以及抑制性核的丘脑皮质投影。皮质圆柱阵列(第三层)代表星状,双束,篮状和锥体细胞相互作用。网络的动力学由两个横向抑制类型的积分微分方程决定。为了实现神经元可塑性,这些方程式中的突触权重参数是变量。学习规则是由Hebb的原始概念激发的,但包含了Hebbian和非Hebbian规则的组合,从而修改了不同的列内和列间交互。我们从行为的角度从信息处理和计算资源的角度讨论神经元可塑性的含义。 [参考:76]

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