Devika Narain

This study examines Bayesian inference in cerebellar degeneration by testing patients on timing tasks and developing a model of cerebellar microcircuits.

The paper investigates how cortico-cerebellar beta-band dynamics predict flexible motor timing during a context-dependent task.

This paper introduces a novel dimensionality reduction technique that generates low-dimensional embeddings while preserving the global structure within the data for a variety of biological and non-biological datasets.

The paper investigates how cerebellar Purkinje cells encode prior knowledge of temporal statistics through predictive eyeblink behavior and identifies a novel complex spike signal associated with uncertainty.

The paper demonstrates that the eyeblink system can learn rapidly and flexibly, challenging previous views of implicit learning as slow and inflexible.

The paper shows how prior beliefs influence behavior by shaping cortical latent dynamics through neural activity in monkeys during a time interval reproduction task.

The paper shows that sensorimotor computations in monkeys are controlled by dynamical system principles, using inputs and initial conditions to adjust behavior.

This paper develops a computational model showing how the cerebellum could learn prior distributions of time intervals and support Bayesian temporal estimation.

The paper investigates how neural networks in the medial frontal cortex of primates control timing flexibility by showing that firing rate profiles are temporally stretched or compressed for different intervals, with speed control originating within cortical networks.