A correlation-based network for hardware implementations

J. Ngole, L. Asplund
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Abstract

An architecture and learning rules for a correlation-based network are proposed. Hidden activity predictors dynamically compute local temporal receptive field centres through a decorrelation process. Temporal feedback loops between units in the hidden layer are then used to synchronise the activities of similar near by units. The simultaneous activation of different topologically overlapping unit groupings results in a continual reorganisation of units in the hidden layer: the dependence of hidden intra-layer communication on cross-correlations gives it the image of an analogue spiking neural network. The predominantly feedforward nature of the architecture makes it attractive for implementation in parallel hardware. Some suggestions on how this can be accomplished are also proposed, together with some software simulation results on a problem of instantaneous separation of two sine waves with different phases.
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用于硬件实现的基于关联的网络
提出了一种基于关联的网络结构和学习规则。隐式活动预测器通过去相关过程动态计算局部颞感受野中心。隐藏层中单位之间的时间反馈循环被用来同步附近类似单位的活动。不同拓扑重叠单元组的同时激活导致隐藏层中单元的持续重组:隐藏层内通信对互相关的依赖使其具有模拟尖峰神经网络的图像。该体系结构的主要前馈特性使其对并行硬件的实现具有吸引力。本文还对如何实现这一目标提出了一些建议,并给出了两个不同相位的正弦波的瞬时分离问题的一些软件仿真结果。
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