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• W2774743895 abstract "Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its’ ability to generate sequences consistently even in the presence of perturbations." @default.
• W2774743895 created "2017-12-22" @default.
• W2774743895 creator A5031161187 @default.
• W2774743895 creator A5050310538 @default.
• W2774743895 date "2017-12-12" @default.
• W2774743895 modified "2023-09-25" @default.
• W2774743895 title "Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks" @default.
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• W2774743895 doi "https://doi.org/10.3389/fnins.2017.00693" @default.
• W2774743895 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5733011" @default.
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• W2774743895 hasPublicationYear "2017" @default.
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