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基于电导模型神经元的循环网络中眼位记忆的稳定性。

Stability of the memory of eye position in a recurrent network of conductance-based model neurons.

作者信息

Seung H S, Lee D D, Reis B Y, Tank D W

机构信息

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139, USA.

出版信息

Neuron. 2000 Apr;26(1):259-71. doi: 10.1016/s0896-6273(00)81155-1.

Abstract

Studies of the neural correlates of short-term memory in a wide variety of brain areas have found that transient inputs can cause persistent changes in rates of action potential firing, through a mechanism that remains unknown. In a premotor area that is responsible for holding the eyes still during fixation, persistent neural firing encodes the angular position of the eyes in a characteristic manner: below a threshold position the neuron is silent, and above it the firing rate is linearly related to position. Both the threshold and linear slope vary from neuron to neuron. We have reproduced this behavior in a biophysically plausible network model. Persistence depends on precise tuning of the strength of synaptic feedback, and a relatively long synaptic time constant improves the robustness to mistuning.

摘要

对多种脑区短期记忆的神经关联研究发现,短暂输入可通过一种未知机制导致动作电位发放率的持续变化。在一个负责在注视期间保持眼睛静止的运动前区,持续的神经放电以一种独特方式编码眼睛的角位置:低于阈值位置时神经元沉默,高于该位置时发放率与位置呈线性相关。阈值和线性斜率因神经元而异。我们在一个具有生物物理合理性的网络模型中重现了这种行为。持续性取决于突触反馈强度的精确调整,相对较长的突触时间常数提高了对失调的鲁棒性。

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