Derives information-maximizing rules for baseline weights and release probabilities in Tsodyks-Markram synapses, producing onset-sensitive presynaptic terms and anti-causal connectivity in recurrent networks.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
citing papers explorer
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Reshaping Neural Representation via Associative, Presynaptic Short-Term Plasticity
Derives information-maximizing rules for baseline weights and release probabilities in Tsodyks-Markram synapses, producing onset-sensitive presynaptic terms and anti-causal connectivity in recurrent networks.
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Hypothesis generation and updating in large language models
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.