The switching and learning behavior of an octopus cell implemented on FPGA.

IF 2.6 4区 工程技术 Q1 Mathematics
Alexej Tschumak, Frank Feldhoff, Frank Klefenz
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引用次数: 0

Abstract

A dendrocentric backpropagation spike timing-dependent plasticity learning rule has been derived based on temporal logic for a single octopus neuron. It receives parallel spike trains and collectively adjusts its synaptic weights in the range [0, 1] during training. After the training phase, it spikes in reaction to event signaling input patterns in sensory streams. The learning and switching behavior of the octopus cell has been implemented in field-programmable gate array (FPGA) hardware. The application in an FPGA is described and the proof of concept for its application in hardware that was obtained by feeding it with spike cochleagrams is given; also, it is verified by performing a comparison with the pre-computed standard software simulation results.

在 FPGA 上实现章鱼细胞的开关和学习行为。
我们基于时间逻辑为单个章鱼神经元推导出了一种树枝状反向传播尖峰计时可塑性学习规则。在训练过程中,它接收平行的尖峰列车,并在 [0, 1] 范围内集体调整其突触权重。训练阶段结束后,神经元会对感觉流中的事件信号输入模式作出尖峰反应。章鱼细胞的学习和切换行为是通过现场可编程门阵列(FPGA)硬件实现的。文中介绍了在 FPGA 中的应用,并给出了通过向其输入尖峰耳蜗图而获得的在硬件中应用的概念验证;此外,还通过与预先计算的标准软件仿真结果进行比较进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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