Most existing studies on computational modeling of neural plasticity have focused on synaptic plasticity. However, regulation of the internal weights in the reservoir based on synaptic plasticity often results in unstable learning dynamics. In this article, a structural synaptic plasticity learning rule is proposed to train the weights and add or remove neurons within the reservoir, which is shown to be able to alleviate the instability of the synaptic plasticity, and to contribute to increase the memory capacity of the network as well. Our experimental results also reveal that a few stronger connections may last for a longer period of time in a constantly changing network structure, and are relatively resistant to decay or disruptions in the learning process. These results are consistent with the evidence observed in biological systems. Finally, we show that an echo state network (ESN) using the proposed structural plasticity rule outperforms an ESN using synaptic plasticity and three state-of-the-art ESNs on four benchmark tasks.
Echo State Network Synaptic Plasticity Structural Plasticity Structural Synaptic Plasticity Benchmark Tasks Modeling Of Plasticity Structural Rule Regulation Of Weights Modeling Of Synaptic Plasticity Synaptic Learning Rule
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Round-ups are the summaries of handpicked papers around trending topics published every week. These would enable you to scan through a collection of papers and decide if the paper is relevant to you before actually investing time into reading it.
Climate change Research Articles published between Nov 21, 2022 to Nov 27, 2022
Nov 28, 2022
Articles Included: 2
No potential conflict of interest was reported by the authors. The conception and design of the study, acquisition of data, analysis and interpretatio...Read More
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