Recently, brain-inspired computing models have shown great potential to outperform today’s deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown
However, given that the memory-related training in our multi-domain training program was adopted from neuropsychological tests (i.e., the word learning test and digit span test) instead of WM tasks (i.e., N-back or delayed match to sample task) that were usually applied in previous studies...
我们首先反思时序脉冲神经动态的本质,并提出了一种将SNN模型与其循环ANN对应物统一起来的方法,类似于参考文献60-62中的并行发展。然后,我们提出将LIF类型脉冲神经元的生物学启发动态抽象为一个简单的循环ANN单元,称为脉冲神经单元:SNU。这种方法导致ANN构造,如果使用阶跃函数激活,则可以在离散时域中重现SNN的行为,或者在...
After treatment, we detected the spatial memory and discriminative learning memory by MWM and Y maze test. The MWM test includes two stages. As shown in Fig. 4A, after five days of acquired training, mice were subjected to the probe test, followed by one-week rest. Y maze test was then...
ANN mimics the human nervous system to solve problems in a parallel manner. ANN are known to be adaptable with situations, flexible with data and efficient enough for predicting any kind of problems. The limitation of ANN lies into the overdependence on data for learning the problem. Also ...
对比ANN、SNN、SNU、LSTM、所有网络都使用BPTT算法 对于手写数字识别任务,7层SNN的平均识别准确率达到了98.47%。与各种循环神经网络(LSTM和GRU)相比,具有相似结构的SNU-based网络在准确性上达到最高水平。使用4层网络和sSNUs获得了最好的结果,准确率达到了98.5%。
Figure 3. Estimated Memory Trajectories by Food Security Status in Primary Analytic Sample View LargeDownload Estimates were made based on the regression model fit in the primary analysis. The analytical sample size is 7012 individuals, and 18 356 is the person-wave observations. Table. Baseline Ch...
In mis contribution we present a connectdonist method to store these values off... D Butz,K Noack,W Brauer - Springer London 被引量: 1发表: 1993年 Page-oriented holographic memory with the full learning capacity of a neural network Many technical tasks in control engineering require the ...
J. Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 18, 283–328 (2006). Article MathSciNet PubMed MATH Google Scholar McNab, F. & Klingberg, T. Prefrontal cortex and basal ganglia control access to working memory. ...
Here we report a duplex device structure based on a ferroelectric field-effect transistor and an atomically thin MoS2 channel, and realize a universal in-memory computing architecture for in situ learning. By exploiting the tunability of the ferroelectric energy landscape, the duplex building block ...