Modeling fast stimulus-response association learning along the occipito-parieto-frontal pathway following rule instructions
Modeling fast stimulus-response association learning along the occipito-parieto-frontal pathway following rule instructions
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DOI：
10.1016/j.brainres.2011.09.028
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发表时间：
2012-01-24
期刊：
影响因子：
2.9
通讯作者：
Bugmann, Guido
中科院分区：
文献类型：
--
作者：
Bugmann, Guido
在指令的基础上，人类能够在几秒钟内建立由几个神经元中继分开的大脑感觉和运动区域之间的联系。本文提出了一个快速学习的模型，沿着背路径，从初级视觉区到前运动皮层。提出了一种新的突触学习规则，其中突触功效快速收敛到由神经元的活动输入的数量确定的特定值，尊重神经元可用的总突触输入功效方面的资源限制的原则。关于尖峰序列中尖峰的重复到达，效力是稳定的。这条规则再现了在长时程增强（LTP）实验中观察到的初始和最终突触功效之间的反比关系。学习实验的模拟在一个多层网络的泄漏积分和火灾（LIF）尖峰神经元模型。有人提出，皮层反馈连接传达了一个自上而下的学习使能信号，指导自下而上的学习“隐藏”的神经元，不直接暴露于输入或输出活动。对相同刺激-反应对的重复呈现的模拟表明，在具有概率突触传递的快速学习条件下，网络倾向于在每次呈现时招募新的子网络来表示关联，而不是重新使用先前训练的子网络。神经资源分配的增加导致执行时间逐渐缩短，与实验观察到的响应时间减少一致。这篇文章是题为“神经编码”的特刊的一部分。(C)2011 Elsevier B. V.保留所有权利。
On the basis of instructions, humans are able to set up associations between sensory and motor areas of the brain separated by several neuronal relays, within a few seconds. This paper proposes a model of fast learning along the dorsal pathway, from primary visual areas to pre-motor cortex. A new synaptic learning rule is proposed where synaptic efficacies converge rapidly toward a specific value determined by the number of active inputs of a neuron, respecting a principle of resource limitation in terms of total synaptic input efficacy available to a neuron. The efficacies are stable with regards to repeated arrival of spikes in a spike train. This rule reproduces the inverse relationship between initial and final synaptic efficacy observed in long-term potentiation (LTP) experiments. Simulations of learning experiments are conducted in a multilayer network of leaky integrate-and-fire (LIF) spiking neuron models. It is proposed that cortical feedback connections convey a top-down learning-enabling signal that guides bottom-up learning in "hidden" neurons that are not directly exposed to input or output activity. Simulations of repeated presentation of the same stimulus-response pair, show that, under conditions of fast lear ning with probabilistic synaptic transmission, the networks tend to recruit a new sub-network at each presentation to represent the association, rather than re-using a previously trained one. This increasing allocation of neural resources results in progressively shorter execution times, in line with experimentally observed reduction in response time with practice. This article is part of a Special Issue entitled: Neural Coding. (C) 2011 Elsevier B.V. All rights reserved.