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通过计算机模拟对听觉神经元网络进行空间放电模式建模。

Spatial firing patterns of auditory neuron network modelling by computer simulation.

作者信息

Nomoto M

出版信息

Biol Cybern. 1979 May 2;32(4):227-37. doi: 10.1007/BF00337646.

Abstract

This communication examines, in digital computer simulated network, input signals and response patterns established at excitatory neurons' level i.e. the membrane potential of neuron soma. It is restricted to spatial patterns of the auditory neuron networks and time factor for nervous conduction and transmission is neglected compared with long maintained membrane potentials of neuron somas. The model analyzes the change in the spatial patterns of the membrane potential in the two dimensional networks of the auditory system. In order to evaluate the contribution of the various parameters, it is started that the simplest model has only one parameter, lateral inhibition. The other parameters are then added, one at a time, to successive models. The lateral inhibition is a necessary condition in the auditory nervous system if any sharpening of the response areas in the single neurons is to occur. A necessary condition for the validity of the model is that is should be applicable to the other senses such as vision and chemical patterns, taste. The threshold feature of auditory neurons aids in producing a sharpening in the neuron of the auditory relay nuclei. It does this clipping the spatial response patterns in one dimensional arrays of excitatory neurons. Recurrent inhibition seems a necessary condition in the sensory nervous system that any kinds of input signals are to be preserved over a wide range of stimulus intensity. In other words, this network has a wide dynamic range against any kinds of input signals. A simple self-recurrent negative feedback does not contribute to the sharpening, but more complex socalled averaged type does. A neuron network is capable of responding stably to stimuli with a wide range of intensity and with any kind of spatial patterns if there is a simple negative feedback mechanism. When there is no negative feedback, input signals soon disappear or saturate in the neuron network. Therefore, recurrent inhibition is the most important mechanism. Spontaneous activity appears to aid in the sharpening by providing a kind of contrast, that is by reducting the amount of activity in neurons adjacent to the excitatory area. Moreover, the effect of spontaneous activity in the model seems to make repples around the excitatory area and suggests that an introduction of activity at any stage of the networks, from whatever source for example reticulum formation and thalamus, might appreciably alter the response patterns at subsequent neuron network. This suggests that the mechanism of the consciousness that might be controlled by the thalamus and or reticular formation. These two dimensional neuron networks may be expanded to three dimensional neuron networks. The former might simulate the auditory nervous system while the latter might simulate the visual system.

摘要

本通讯在数字计算机模拟网络中,研究了兴奋性神经元水平上建立的输入信号和反应模式,即神经元胞体的膜电位。它仅限于听觉神经元网络的空间模式,与神经元胞体长期维持的膜电位相比,神经传导和传递的时间因素被忽略。该模型分析了听觉系统二维网络中膜电位空间模式的变化。为了评估各种参数的作用,一开始最简单的模型只有一个参数,即侧向抑制。然后将其他参数一次一个地添加到后续模型中。如果单个神经元的反应区域要发生任何锐化,侧向抑制是听觉神经系统中的必要条件。该模型有效性的一个必要条件是它应适用于其他感官,如视觉、化学模式、味觉。听觉神经元的阈值特征有助于在听觉中继核的神经元中产生锐化。它通过在兴奋性神经元的一维阵列中裁剪空间反应模式来实现这一点。循环抑制似乎是感觉神经系统中的必要条件,以便在广泛的刺激强度范围内保留任何种类的输入信号。换句话说,该网络对任何种类的输入信号都有很宽的动态范围。简单的自循环负反馈无助于锐化,但更复杂的所谓平均类型则有助于锐化。如果存在简单的负反馈机制,神经元网络能够对具有广泛强度范围和任何空间模式的刺激做出稳定反应。当没有负反馈时,输入信号在神经元网络中很快消失或饱和。因此,循环抑制是最重要的机制。自发活动似乎通过提供一种对比来帮助锐化,即通过减少兴奋性区域相邻神经元中的活动量。此外,模型中自发活动的效果似乎在兴奋性区域周围产生涟漪,并表明在网络的任何阶段引入活动,无论来源如何,例如网状结构和丘脑,都可能明显改变后续神经元网络的反应模式。这表明意识机制可能由丘脑和/或网状结构控制。这些二维神经元网络可以扩展为三维神经元网络。前者可能模拟听觉神经系统,而后者可能模拟视觉系统。

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