Recurrent neural network architecture with pre-synaptic inhibition for incremental learning
Neural Networks - 2006 Special issue: Neurobiology of decision making
Coding mechanisms in hippocampal networks for learning and memory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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The hippocampus plays an important role in the course of establishing long-term memory, i.e., to make short-term memory of spatially and temporally associated input information. In 1996 (Tsukada et al. 1996), the spatiotemporal learning rule was proposed based on differences observed in hippocampal long-term potentiation (LTP) induced by various spatiotemporal pattern stimuli. One essential point of this learning rule is that the change of synaptic weight depends on both spatial coincidence and the temporal summation of input pulses. We applied this rule to a single-layered neural network and compared its ability to separate spatiotemporal patterns with that of other rules, including the Hebbian learning rule and its extended rules. The simulated results showed that the spatiotemporal learning rule had the highest efficiency in discriminating spatiotemporal pattern sequences, while the Hebbian learning rule (including its extended rules) was sensitive to differences in spatial patterns.