Danil Tyulmankov

The paper shows that switching between tonic and burst firing in neural networks creates a burst-induced attractor in synaptic weight dynamics, which stabilizes synapses and supports memory consolidation.

This paper develops a computational model of striatal circuits that explains decision-making mechanisms and links them to neuropsychiatric disorders through altered information processing.

The paper introduces a two-stage synaptic plasticity rule that enables memory consolidation during burst firing regimes by stabilizing prior learning and improving generalization.

This paper identifies computational strategies for cost-benefit decision-making in humans and rodents, showing their shared use and adaptation based on factors like hunger and sex.

This paper introduces a computational method to infer synaptic plasticity rules from neural and behavioral data, revealing an active forgetting component in reward learning in Drosophila.

The paper develops a neural network model with anti-Hebbian plasticity to detect familiarity in images, showing better performance than Hebbian models and replicating experimental results.