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Danil Tyulmankov

2 accepted papers

2024

Model Based Inference of Synaptic Plasticity Rules

NeurIPS 2024poster

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a paramet…

Cited by 4SourcePDFScholar
2021

Biological learning in key-value memory networks

NeurIPS 2021poster

In neuroscience, classical Hopfield networks are the standard biologically plausible model of long-term memory, relying on Hebbian plasticity for storage and attractor dynamics for recall. In contrast, memory-augmented neural networks in machine learning commonly use a key-value mechanism to store a…

Cited by 50SourcePDFScholar