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成年大鼠皮质桶状柱中经验依赖性可塑性的计算研究

Computational study of experience-dependent plasticity in adult rat cortical barrel-column.

作者信息

Benusková L, Ebner F F, Diamond M E, Armstrong-James M

机构信息

Department of Computer Science and Engineering, Slovak Technical University, Bratislava.

出版信息

Network. 1999 Nov;10(4):303-23.

PMID:10695761
Abstract

We model experience-dependent plasticity in the adult rat S1 cortical representation of the whiskers (the barrel cortex) which has been produced by trimming all whiskers on one side of the snout except two. This manipulation alters the pattern of afferent sensory activity while avoiding any direct nerve damage. Our simplified model circuitry represents multiple cortical layers and inhibitory neurons within each layer of a barrel-column. Utilizing a computational model we show that the evolution of the response bias in the barrel-column towards spared whiskers is consistent with synaptic modifications that follow the rules of the Bienenstock, Cooper and Munro (BCM) theory. The BCM theory postulates that a neuron possesses a dynamic synaptic modification threshold, thetaM, which dictates whether the neuron's activity at any given instant will lead to strengthening or weakening of the synapses impinging on it. However, the major prediction of our model is the explanation of the delay in response potentiation in the layer-IV neurons through a masking effect produced by the thresholded monotonically increasing inhibition expressed by either the logarithmic function, h(x) = mu log(1 + x), or by the power function, h(x) = mu x(0.8-0.9), where mu is a constant. Furthermore, simulated removal of the supragranular layers (layers II/III) reduces plasticity of neurons in the remaining layers (IV-VI) and points to the role of noise in synaptic plasticity.

摘要

我们对成年大鼠初级躯体感觉皮层(S1)中触须(桶状皮层)的经验依赖性可塑性进行建模,该可塑性是通过修剪口鼻一侧除两根触须外的所有触须而产生的。这种操作改变了传入感觉活动的模式,同时避免了任何直接的神经损伤。我们简化的模型电路代表了桶状柱每层中的多个皮层层和抑制性神经元。利用计算模型,我们表明桶状柱中对保留触须的反应偏差的演变与遵循比嫩斯托克、库珀和蒙罗(BCM)理论规则的突触修饰一致。BCM理论假设神经元具有动态突触修饰阈值thetaM,该阈值决定神经元在任何给定时刻的活动是否会导致撞击它的突触增强或减弱。然而,我们模型的主要预测是通过对数函数h(x)=μlog(1 + x)或幂函数h(x)=μx(0.8 - 0.9)(其中μ是常数)所表达的阈值单调增加抑制产生的掩盖效应来解释IV层神经元反应增强的延迟。此外,模拟去除颗粒上层(II/III层)会降低其余层(IV - VI层)中神经元的可塑性,并指出噪声在突触可塑性中的作用。

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