Boris S Gutkin

This study investigates how midbrain dopamine neurons control male mouse copulatory behavior, finding that their activity is experience-independent and critical for sustaining thrusting actions during mating.

The paper examines the link between reward impulsivity and addictive-like behaviors towards different food categories, finding significant correlations for sweet food and sugary drinks but not for fatty, salty, or starchy food.

The paper shows that modifying local brain dynamics can compensate for structural connectivity damage, effectively restoring functional connectivity without repairing the physical structure.

This paper introduces BRyBI, a brain-rhythm-based model that integrates rhythmic processes to simulate speech comprehension, showing it can handle speech variations and aligns with human experiments.

The paper models cognitive conflict in addiction by proposing an algorithmic framework that explains how drug-seeking persists despite negative outcomes.

The paper demonstrates that sublinear dendritic summation enables neurons to perform linearly non-separable computations by showing increased spiking probability with scattered synaptic activation.

This study shows that brain stimulation effects depend on dynamic states and phases, with machine learning improving prediction by 40%.

This study shows that phasic dopamine signaling in VTA neurons regulates force exertion during learning, not reward prediction error.

This paper extends homeostatic regulation into a reinforcement learning framework to model sodium-seeking behavior and dopamine signaling in rats.

The paper introduces Spikebench, a benchmark for spike train classification, showing that hand-crafted features match deep learning performance.

This study examines how learning affects the robustness of working memory to distractors in a Sternberg-like task, showing that training impacts accuracy and reaction times differently.

This paper simulates semantic dementia in a brain-constrained model to explain language impairments and predict category-specific effects of the disease.

The paper develops a predictive coding framework for recurrent spiking networks, showing that heterogeneous networks are more efficient and robust than homogeneous ones.

This study uses a computational model to show that beta and alpha oscillations differentially control working memory stability by modulating competition between stimulus and distractor encoding circuits.

The paper investigates how cholinergic activation of interneurons disinhibits CA1 pyramidal neurons, leading to synaptic plasticity in hippocampal excitatory synapses.

The paper models how changes in inhibitory and excitatory synaptic timing drive a sudden switch in cortical network dynamics during development.

The paper investigates neural and computational mechanisms of bidding behavior in auctions using model-based neuroimaging and finds directional learning models outperform reinforcement learning in predicting human bidding strategies.

This study develops a minimal computational model of the VTA circuit to explore how reward prediction errors are computed and how nicotine affects these processes through nicotinic receptors.

This paper develops a neural mass model incorporating spike-frequency adaptation to study seizure dynamics, showing adaptation and inhibition control pathological synchronization in epilepsy.

The paper models how genetic modifications and nicotine affect neural activity states in the prefrontal cortex, linking them to schizophrenia-related hypofrontality.

This paper models circuit-level mechanisms of ethanol-induced dopamine release from the ventral tegmental area to nucleus accumbens, showing dose-dependent firing changes and the role of synchrony and glutamatergic input.