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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.10397 |
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| _version_ | 1866916026890846208 |
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| author | Shimizu, Genki Toyoizumi, Taro |
| author_facet | Shimizu, Genki Toyoizumi, Taro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10397 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reshaping Neural Representation via Associative, Presynaptic Short-Term Plasticity Shimizu, Genki Toyoizumi, Taro Neurons and Cognition 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. |
| title | Reshaping Neural Representation via Associative, Presynaptic Short-Term Plasticity |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2601.10397 |