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synaptic-weight

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Jiankun-chen / Supervised-SNN-with-GD

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A supervised learning algorithm of SNN is proposed by using spike sequences with complex spatio-temporal information. We explore an error back-propagation method of SNN based on gradient descent. The chain rule proved mathematically that it is sufficient to update the SNN’s synaptic weights by directly using an optimizer. Utilizing the TensorFlo…

tensorflow optimizer supervised-learning gradient-descent spiking-neural-network sar-image-classification spike-sequence synaptic-weight
  • Updated Jan 15, 2022
  • Python

simahashemi / CA3-CA1-Network-Learning-Stability

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Simulates distinct learning dynamics and synaptic stability, highlighting the role of axon-carrying dendrites (AcD) in memory consolidation under inhibitory conditions.

computational-neuroscience sleep neuronal-network inhibition neuron-simulator compartmental-model memory-consolidation synaptic-weight neural-network-modeling ca3-ca1-network poisson-spiking-group synaptic-stability neural-plasticity ripple-phase
  • Updated May 25, 2025
  • Jupyter Notebook

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