Robust doublet STDP in a floating-gate synapse
Robust doublet STDP in a floating-gate synapse
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浮栅突触中的鲁棒双态 STDP
DOI：
10.1109/ijcnn.2014.6889631
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
A. Basu
中科院分区：
文献类型：
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作者：
Roshan Gopalakrishnan;A. Basu
神经网络中的学习通常通过突触权重的改变或可塑性来实现。因此，基于突触前和突触后脉冲出现的时间差来改变突触强度的可塑性规则被称为脉冲时间依赖可塑性（STDP）。本文描述了一种利用单个浮栅（FG）晶体管的突触的神经形态超大规模集成电路实现方式，该晶体管可用于以非易失性方式存储权重，并展示诸如长时程增强（LTP）、长时程抑制（LTD）和STDP等生物学习规则。先前研究中一个FG突触的实验性STDP曲线（权重变化相对于Δt = tpost - tpre）表明，对于一系列广泛的参数，在Δt的某些正值范围内出现的是抑制而非增强。在本文中，我们提出了一种基于改变FG器件控制栅极波形的简单解决方案，使得权重变化在广泛的参数范围内与生物学观察结果紧密相符。我们展示了一个理论模型的结果以说明修改后的波形的影响。还给出了采用AMS 0.35μm CMOS工艺设计制造的FG突触的实验结果以证明这一说法。
Learning in a neural network typically happens with the modification or plasticity of synaptic weight. Thus the plasticity rule which modifies the synaptic strength based on the timing difference between the pre- and post-synaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP). This paper describes the neuromorphic VLSI implementation of a synapse utilizing a single floating-gate (FG) transistor that can be used to store a weight in a nonvolatile manner and demonstrate biological learning rules such as Long-Term Potentiation (LTP), Long-Term Depression (LTD) and STDP. The experimental STDP plot of a FG synapse (change in weight against Δt = tpost - tpre) from previous studies shows a depression instead of potentiation at some range of positive values of Δt for a wide set of parameters. In this paper, we present a simple solution based on changing control gate waveforms of the FG device that makes the weight change conform closely with biological observations over a wide range of parameters. We show results from a theoretical model to illustrate the effects of the modified waveform. The experimental results from a FG synapse fabricated in AMS 0.35μm CMOS process design are also presented to justify the claim.