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使用分布式且可局部重新编程的地址事件接收器构建大型发育性感受野。

Large developing receptive fields using a distributed and locally reprogrammable address-event receiver.

作者信息

Bamford Simeon A, Murray Alan F, Willshaw David J

机构信息

Institute of Integrated Micro and Nano Systems, Neuroinformatics Doctoral Training Centre, University of Edinburgh, Edinburgh, UK.

出版信息

IEEE Trans Neural Netw. 2010 Feb;21(2):286-304. doi: 10.1109/TNN.2009.2036912. Epub 2010 Jan 12.

Abstract

A distributed and locally reprogrammable address-event receiver has been designed, in which incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change the address of their presynaptic neuron, allowing the distributed implementation of a biologically realistic learning rule, with both synapse formation and elimination (synaptic rewiring). Probabilistic synapse formation leads to topographic map development, made possible by a cross-chip current-mode calculation of Euclidean distance. As well as synaptic plasticity in rewiring, synapses change weights using a competitive Hebbian learning rule (spike-timing-dependent plasticity). The weight plasticity allows receptive fields to be modified based on spatio-temporal correlations in the inputs, and the rewiring plasticity allows these modifications to become embedded in the network topology.

摘要

一种分布式且可本地重新编程的地址事件接收器已被设计出来,在该接收器中,所有突触会同时监测传入的地址事件,从而允许任意大的轴突扇出而不降低通道容量。突触可以改变其突触前神经元的地址,这使得生物现实学习规则的分布式实现成为可能,包括突触形成和消除(突触重新布线)。概率性突触形成导致地形图的发展,这通过欧几里得距离的跨芯片电流模式计算得以实现。除了重新布线中的突触可塑性外,突触还使用竞争性赫布学习规则(尖峰时间依赖性可塑性)来改变权重。权重可塑性允许基于输入中的时空相关性来修改感受野,而重新布线可塑性则允许这些修改嵌入到网络拓扑结构中。

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