STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks
STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks
复制标题
DOI：
10.1587/elex.6.148
复制
发表时间：
2009-02-10
影响因子：
0.8
通讯作者：
Raissi, Farshid
中科院分区：
文献类型：
--
作者：
Afifi, Ahmad;Ayatollahi, Ahmad;Raissi, Farshid
实现基于相关性的学习规则，尖峰定时依赖可塑性（STDP），异步神经形态网络的演示使用“忆阻”纳米器件。STDP是在特定时刻使用本地可用的信息来执行的，为此，映射到基于纵横的CMOS-Nano架构（诸如CMOS-MOLecular（CMOL））是相当容易完成的。该学习方法是动态的和在线的，其中突触权重基于神经活动进行修改。所提出的方法的性能进行了分析，为特定形状的尖峰和仿真结果提供了一个突触与STDP属性。
Implementation of a correlation-based learning rule, Spike-Timing- Dependent-Plasticity (STDP), for asynchronous neuromorphic networks is demonstrated using 'memristive' nanodevice. STDP is performed using locally available information at the specific moment of time, for which mapping to crossbar-based CMOS-Nano architectures, such as CMOS-MOLecular (CMOL), is done rather easily. The learning method is dynamic and online in which the synaptic weights are modified based on neural activity. The performance of the proposed method is analyzed for specifically shaped spikes and simulation results are provided for a synapse with STDP properties.