Bamford Simeon A, Murray Alan F, Willshaw David J
Institute of Integrated Micro and Nano Systems, Neuroinformatics Doctoral Training Centre, University of Edinburgh, Edinburgh, UK.
IEEE Trans Neural Netw. 2010 Feb;21(2):286-304. doi: 10.1109/TNN.2009.2036912. Epub 2010 Jan 12.
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.
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。
