Cristina Savin

This study identifies a corticostriatal circuit in rats that updates beliefs about hidden task states through specific neural encoding and perturbations.

This study shows that reward timing and sensory cue reliability influence how monkeys allocate foraging effort in a naturalistic task.

This study shows that rat orbitofrontal cortex updates beliefs about hidden reward states during a temporal wagering task, with neural signatures of state inference identified through electrophysiology and population analysis.

The paper shows that mice with ASD-related mutations exhibit similar computational and neural anomalies in prior updating during decision-making.

This study investigates how hippocampal neurons in mice encode different variables during a task, finding that spiking activity aligns with internal action plans rather than external cues.

This study uses recurrent neural networks to show how circuit compensation and re-learning mitigate inactivation effects, with varying impacts across tasks and network architectures.

This paper improves RNN performance on complex tasks by using compositional pretraining, matching rat behavior in a temporal wagering task.

The paper shows that targeted gain modulation in V1 neurons supports task-adaptive sensory decisions by enabling rapid downstream readout of task-relevant information.

This paper introduces a recurrent circuit model that uses dendritic nonlinearities and stochastic integration to perform probabilistic inference, demonstrating its ability to sample from low-dimensional manifolds and multimodal posteriors.

The paper reveals structured cell-to-cell interactions in hippocampal CA1 that optimize spatial coding in familiar and novel environments through statistical modeling and maximum entropy frameworks.

The paper develops a computational framework to analyze how dynamical systems in recurrent circuits evolve during learning across different tasks and algorithms, finding a common path to task mastery.

The paper shows that dynamic reconfiguration of functional connectivity in prefrontal cortex supports context-invariant beliefs during natural behavior.

This study examines how parietal cortex neurons transform acoustic information into decision variables during auditory tasks, showing task-dependent changes in neural decoding and clustering.

The paper investigates how neural circuits in sensory, parietal, and frontal cortices encode latent variables during virtual navigation tasks in monkeys.

The paper identifies subpopulations of lOFC neurons that encode reward history and choice during decision-making in rats.

This paper introduces a probabilistic method to align neural responses across animals and identifies odor-specific dynamics, improving neural-based chemical detection.

This study develops a stochastic recurrent neural network model with reward-modulated Hebbian plasticity to learn efficient, task-specific sensory representations in two tasks: stimulus estimation and discrimination.

This paper introduces a decoding method that uses stochastic modulation to identify task-relevant neurons and achieves performance close to an optimal decoder in simulations.