associative-presynaptic-plasticity

Fisher information guides presynaptic short-term plasticity by deriving gradient-based updates for synaptic weights and release probability U, maximizing information transfer during spike processing in Tsodyks-Markram neural modeling.

To train associative STP on sequential spike data, process Tsodyks-Markram state updates and apply gradient-based updates to adjust synaptic weights and release probability U for online adaptation in neural modeling.

Yes, you can use Fisher information learning rules to update release probability U in neural modeling by deriving gradient-based updates with adjustable constraints, optimizing information transmission across Tsodyks-Markram synapses.

Yes, associative presynaptic plasticity supports sequence learning in neuromorphic systems by modeling Tsodyks-Markram dynamics to enable online adaptation and optimize temporal coding across simulated spike sequences.

When adjusting release probability U in Tsodyks-Markram synapses, constraints apply to gradient-based weight updates to ensure principled short-term plasticity dynamics and maintain stable information transmission during spike processing.