Adaptive WTA with an analog VLSI neuromorphic learning chip
Adaptive WTA with an analog VLSI neuromorphic learning chip
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DOI：
10.1109/tnn.2006.884676
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发表时间：
2007-03-01
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通讯作者：
Hafliger, Philipp
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文献类型：
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作者：
Hafliger, Philipp
在本文中，我们演示了一个特定的基于尖峰的学习规则（尖峰模型神经元的输入和输出尖峰之间的确切时间关系确定突触权重的变化）可以被调整，以表达基于速率的经典赫布学习行为（其中平均输入和输出尖峰速率足以描述突触的变化）。这种行为上的转变由输入统计量和单个时间常数控制。学习规则已实现在神经形态的超大规模集成电路（VLSI）芯片的神经启发的尖峰信号图像处理系统的一部分。后者是欧盟研究项目卷积AER实时视觉架构（CAVIAR）的结果。由于它被实现为基于尖峰的学习规则（这在整个基于尖峰的系统中是最方便的），因此即使它被调谐为显示速率行为，也不会在芯片上计算显式的长期平均信号。我们展示了规则的基于速率的Hebbian学习能力，在模拟和芯片实验中的分类任务，首先与人工刺激，然后与传感器输入的CAVIAR系统。
In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.