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Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System

机译:SpiNNaker多核神经形态系统的结构可塑性

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摘要

The structural organization of cortical areas is not random, with topographic maps commonplace in sensory processing centers. This topographical organization allows optimal wiring between neurons, multimodal sensory integration, and performs input dimensionality reduction. In this work, a model of topographic map formation is implemented on the SpiNNaker neuromorphic platform, running in realtime using point neurons, and making use of both synaptic rewiring and spike-timing dependent plasticity (STDP). In agreement with Bamford et al. (), we demonstrate that synaptic rewiring refines an initially rough topographic map over and beyond the ability of STDP, and that input selectivity learnt through STDP is embedded into the network connectivity through rewiring. Moreover, we show the presented model can be used to generate topographic maps between layers of neurons with minimal initial connectivity, and stabilize mappings which would otherwise be unstable through the inclusion of lateral inhibition.
机译:皮质区域的结构组织不是随机的,地形图在感觉处理中心很普遍。这种地形组织可实现神经元之间的最佳布线,多峰感觉统合,并降低输入维数。在这项工作中,在SpiNNaker神经形态平台上实现了地形图形成模型,该模型使用点神经元实时运行,并利用了突触重布线和依赖于尖峰时序的可塑性(STDP)。在与Bamford等人的协议中。 (),我们证明突触重布线可以超越STDP的能力来完善最初的粗糙地形图,并且通过重布线将通过STDP学习的输入选择性嵌入到网络连接中。此外,我们显示了提出的模型可用于以最小的初始连接性生成神经元层之间的地形图,并稳定映射,否则通过包含侧向抑制将不稳定。

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