.png){kind=icon}
返回
Learning Spike Time Codes Through Morphological Learning With Binary Synapses
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2015.2447011.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this brief, a neuron with nonlinear dendrites (NNLDs) and binary synapses that is able to learn temporal features of spike input patterns is considered. Since binary synapses are considered, learning happens through formation and elimination of connections between the inputs and the dendritic branches to modify the structure or morphology of the NNLD. A morphological learning algorithm inspired by the tempotron, i.e., a recently proposed temporal learning algorithm is presented in this brief. Unlike tempotron, the proposed learning rule uses a technique to automatically adapt the NNLD threshold during training. Experimental results indicate that our NNLD with 1-bit synapses can obtain accuracy similar to that of a traditional tempotron with 4-bit synapses in classifying single spike random latency and pairwise synchrony patterns. Hence, the proposed method is better suited for robust hardware implementation in the presence of statistical variations. We also present results of applying this rule to real-life spike classification problems from the field of tactile sensing.
Keyword:
Binary synapse
dendrites
learning
plasticity
spiking neuron
tempotron
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications用于数据存储和神经形态应用的功能性混合忆阻器交叉阵列/CMOS系统.png)
NANO LETTERS
NANO LETTERSIF9.1
PREAI
分享收藏A Learning-Enabled Neuron Array IC Based Upon Transistor Channel Models of Biological Phenomena基于生物现象的晶体管通道模型的具有学习功能的神经元阵列IC
PREAI
分享收藏
分享收藏Impact of active dendrites and structural plasticity on the memory capacity of neural tissueNEURONOAAI
NEURONIF15
OAAI分享收藏
查看更多内容