Effect of spike-timing-dependent plasticity on neural assembly computing

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• 作者：Elahe Eskandari^a ; ^{eeskandari90@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Arash Ahmadi^a ; ^{aahmadi@razi.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; ^{aahmadi70@yahoo.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Shaghayegh Gomar^b ; ^{gomar@uwindsor.ca" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Izhikevich model ; Neural assembly computing (NAC) ; Spike timing-dependent plasticity (STDP) ; Spiking neural network (SNN)
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：26 May 2016
• 年：2016
• 卷：191
• 期：Complete
• 页码：107-116
• 全文大小：3897 K

文摘

Spiking neural network (SNNs) are practical and realistic neural models, which have attracted considerable attention and many valuable physical realizations have been implemented. Neural assembly computing (NAC) is a new approach to SNNs, which is known to be a promising mechanism for explaining large-scale neural behavior, and it has been used to examine and explore the computational activities of neural cell assemblies. To obtain algorithms based on NAC, neural coalitions are considered to be responsible for patterns, memorizing them, and controlling their hierarchical relatives. In addition, spike timing-dependent plasticity (STDP) can be employed as a synaptic plasticity learning rule to modify the synaptic weights in neural networks, thereby allowing the convergence of neural activities to a spatiotemporal neuron-network pattern. Thus, applying STDP to NAC can be a powerful tool in neuroscience computing. Investigations in this area are also useful for understanding biological systems. In this study, we investigated the effect of applying STDP to NAC. Our simulation results showed that applying STDP rule increased the average number of firings for each event in neural assemblies by adjusting the weights of the connections. Moreover, the firing of neural assemblies resulted in a sequence of events in a closed loop. We determined correlations to measure the similarity between the patterns in each assembly, which showed that STDP made the network fire with a more distinctive and similar pattern. In addition, after memorizing a pattern, the frequency of events increased and the firing patterns became faster. Therefore, STDP can improve and accelerate the overall NAC process. Thus, our simulation results demonstrate that STDP can help NAC to obtain a more distinctive pattern in the network output.