dorsal/arxiv
View SchemaReshaping Neural Representation via Associative, Presynaptic Short-Term Plasticity
| Authors | Genki Shimizu, Taro Toyoizumi |
|---|---|
| Categories | |
| ArXiv ID | 2601.10397vv1 |
| URL | https://arxiv.org/abs/2601.10397 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Short-term synaptic plasticity (STP) is traditionally viewed as a purely presynaptic filter of incoming spike trains, independent of postsynaptic activity. Recent experiments, however, reveal an associative form of STP in which presynaptic release probability changes alongside long-term potentiation, implying a richer computational role for presynaptic plasticity. Here we develop a normative theory of associative STP using an information-theoretic framework. Extending Fisher-information-based learning to Tsodyks-Markram synapses, we derive analytic update rules for baseline synaptic strength and release probability that maximize encoded stimulus information under resource constraints. The learning rules separate into a conventional postsynaptic term tracking local firing and a distinct presynaptic term with a phase-advanced structure that selectively detects stimulus onset; critically, differences between plasticity of baseline strength and release probability arise within this presynaptic component. For stimulus variations slower than the EPSP time constant, onset sensitivity biases optimal connectivity toward anti-causal associations, strengthening synapses from neurons activated later to those activated earlier. In recurrent circuits, these rules yield ramp-like sustained representations and reverse replay after drive removal. Linear-response analysis further shows that STP confers frequency-dependent phase selectivity on presynaptic drive and that constraints on total release probability systematically tune temporal asymmetry. Together, our results provide a principled account of associative STP and identify presynaptic plasticity of release probability as a substrate for rapidly reconfigurable temporal coding.
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"abstract": "Short-term synaptic plasticity (STP) is traditionally viewed as a purely presynaptic filter of incoming spike trains, independent of postsynaptic activity. Recent experiments, however, reveal an associative form of STP in which presynaptic release probability changes alongside long-term potentiation, implying a richer computational role for presynaptic plasticity. Here we develop a normative theory of associative STP using an information-theoretic framework. Extending Fisher-information-based learning to Tsodyks-Markram synapses, we derive analytic update rules for baseline synaptic strength and release probability that maximize encoded stimulus information under resource constraints. The learning rules separate into a conventional postsynaptic term tracking local firing and a distinct presynaptic term with a phase-advanced structure that selectively detects stimulus onset; critically, differences between plasticity of baseline strength and release probability arise within this presynaptic component. For stimulus variations slower than the EPSP time constant, onset sensitivity biases optimal connectivity toward anti-causal associations, strengthening synapses from neurons activated later to those activated earlier. In recurrent circuits, these rules yield ramp-like sustained representations and reverse replay after drive removal. Linear-response analysis further shows that STP confers frequency-dependent phase selectivity on presynaptic drive and that constraints on total release probability systematically tune temporal asymmetry. Together, our results provide a principled account of associative STP and identify presynaptic plasticity of release probability as a substrate for rapidly reconfigurable temporal coding.",
"arxiv_id": "2601.10397",
"authors": [
"Genki Shimizu",
"Taro Toyoizumi"
],
"categories": [
"q-bio.NC"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Reshaping Neural Representation via Associative, Presynaptic Short-Term Plasticity",
"url": "https://arxiv.org/abs/2601.10397",
"version": "v1"
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