Reshaping Neural Representation via Associative, Presynaptic Short-Term Plasticity

TL;DR

This paper develops an information-theoretic model of associative short-term synaptic plasticity, revealing how presynaptic release probability plasticity enables rapid reconfiguration of temporal coding in neural circuits.

Contribution

It introduces a normative theory linking associative STP to stimulus information maximization, deriving new learning rules for synaptic parameters based on Fisher information.

Findings

Associative STP depends on pre- and postsynaptic coactivation.

Release-probability plasticity enables rapid reconfiguration of temporal coding.

STP enhances response offset and phase selectivity in neural circuits.

Abstract

Short-term synaptic plasticity (STP) is often regarded as a presynaptic filter of spikes, independent of postsynaptic activity. Recent experiments, however, indicate an associative STP that depends on pre- and postsynaptic coactivation. We develop a normative, information-theoretic theory of associative STP. Extending Fisher-information-based learning to Tsodyks-Markram synapses, we derive learning rules for baseline weight and release probability that maximize stimulus information under resource constraints. The rules split into a postsynaptic term tracking local firing and a presynaptic, phase-advanced term that selectively detects stimulus onset. For slowly varying inputs, this onset sensitivity favors anti-causal connectivity and enhances response offset during drive and reverse replay after drive removal in recurrent circuits. Linear-response analysis shows that STP yields frequency-dependent phase selectivity and that release-probability constraints tune temporal asymmetry. These results identify release-probability plasticity as a principled substrate for rapidly reconfigurable temporal coding.