Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron
Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron
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DOI：
10.1093/cercor/bhg095
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发表时间：
2003-11-01
期刊：
影响因子：
3.7
通讯作者：
Seung, HS
中科院分区：
文献类型：
--
作者：
Goldman, MS;Levine, JH;Seung, HS
短期记忆通常与响应于瞬时输入的神经元放电率的持续变化相关。我们模拟持久维护的模拟眼位置信号的眼神经积分器接收瞬态眼球运动命令。这种网络的先前模型依赖于精确调谐的正反馈，对失调的容忍度< 1%，或者使用在眼睛位置变化很小的情况下表现出放电率大的不连续性的神经元。我们分析如何使用神经元与多个神经元树突隔室可以提高眼睛的固定失调的鲁棒性，同时再现近似线性和连续的神经元放电率和眼睛的位置之间的关系，以及依赖于神经元对放电率关系的方向上的前眼跳。该模型对连续变化的输入的响应使得前庭眼反射的性能的预测是可检验的。我们的研究结果表明，树突状双稳态可以稳定工作记忆系统中观察到的持续神经活动。
Short-term memory is often correlated with persistent changes in neuronal firing rates in response to transient inputs. We model the persistent maintenance of an analog eye position signal by an oculomotor neural integrator receiving transient eye movement commands. Previous models of this network rely on precisely tuned positive feedback with < 1% tolerance to mistuning, or use neurons that exhibit large discontinuities in firing rate with small changes in eye position. We show analytically how using neurons with multiple bistable dendritic compartments can enhance the robustness of eye fixations to mistuning while reproducing the approximately linear and continuous relationship between neuronal firing rates and eye position, and the dependence of neuron pair firing rate relationships on the direction of the previous saccade. The response of the model to continuously varying inputs makes testable predictions for the performance of the vestibuloocular reflex. Our results suggest that dendritic bistability could stabilize the persistent neural activity observed in working memory systems.