reshaping-neural-representation-presynaptic-plasticity

You implement associative presynaptic short-term plasticity by configuring a Tsodyks-Markram STP model that jointly depends on pre- and postsynaptic coactivation. This integrates flexible synaptic dynamics directly into recurrent neural network simulations.

Fisher information-based learning is an information-theoretic approach that maximizes stimulus information transmission subject to resource constraints. It optimizes neural dynamics by reshaping synaptic representations based on presynaptic plasticity.

Presynaptic plasticity enhances temporal coding by enabling phase-sensitive onset detection and rapid reconfiguration in recurrent circuits. This allows neural models to dynamically process temporal sequences more realistically.

Yes, you can configure Tsodyks-Markram synapse parameters within a framework that applies Fisher information-based learning rules. This combination optimizes information transmission under resource constraints for short-term plasticity modeling.

Yes, associative STP requires both pre- and postsynaptic coactivation to trigger flexible synaptic dynamics. This joint dependency enables more realistic neural modeling than isolated presynaptic mechanisms.

Short-term plasticity for phase-advanced detection is limited by resource constraints inherent in information-theoretic learning. Synaptic dynamics depend on Tsodyks-Markram parameters, which may cap maximum information transmission in recurrent networks.