Synaptic learning in VLSI-based artificial nerve cells

A. Laffely, S. Wolpert
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Abstract

A VLSI method for analog synaptic learning in an electronic neuronal model is presented. This method reduces the size and complexity involved in implementing adaptive neuronally based controllers for robotic motion. It also provides for a continuous range of synaptic weights at both excitatory and inhibitory inputs while anticipating the need to interface to a pulse-driven system. The system is described, and test results indicate that it is able to alter the synaptic coupling on an inhibitory or an excitory input over a wide range.<>
基于vlsi的人工神经细胞的突触学习
提出了一种用于电子神经元模型模拟突触学习的VLSI方法。该方法减小了机器人运动自适应神经控制器的尺寸和复杂度。它还为兴奋性和抑制性输入提供了连续的突触权重范围,同时预测了与脉冲驱动系统接口的需要。对该系统进行了描述,测试结果表明,它能够在大范围内改变抑制性或兴奋性输入的突触耦合。
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