Biologically inspired temporal sequence learning

• Main Authors: Yusoff, Nooraini, Grüning, André
• Format: Article
• Language: English
• Published: Elsevier Ltd. 2012
• Subjects: QA76 Computer software
• Online Access: http://repo.uum.edu.my/12490/1/1-s2.pdf; http://repo.uum.edu.my/12490/; http://dx.doi.org/10.1016/j.proeng.2012.07.179
• Institution: Universiti Utara Malaysia

We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function.The dynamic properties of our model can be attributed to the sparse and recurrent connectivity, synaptic transmission delays, background activity and inter-stimulus interval (ISI).We have tested the learning in visual recognition task, and temporal AND and XOR problems.The network can be trained to associate a stimulus pair with its target response and to discriminate the temporal sequence of the stimulus presentation.