Kaushik J. Lakshminarasimhan

The paper investigates drift in human EEG responses to images across sessions, finding coordinated drift along image identity dimensions and preserved global structure.

The paper shows that eye movements during memory-guided navigation reflect internal beliefs and improve neural model explanations of behavior and neural data.

This paper develops a computational model linking striatal dopamine levels to auditory hallucinations through corticostriatal plasticity and validates it with mouse and human data.

This paper introduces a computational framework explaining habit formation through a trade-off between exploitation and exploration, showing how neural mechanisms control decision-making systems.

The paper identifies optimal corticothalamic connectivity structures that support learning in thalamocortical loops for motor control and working memory tasks.

This study identifies how posterior parietal cortex encodes and dynamically tracks hidden states during navigation, supported by a neural network model.

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

This study shows that humans and macaques rely on optic flow for decision-making in a naturalistic navigation task, demonstrating sensory evidence accumulation during action-perception loops.

This study examines how eye movements during navigation reflect spatiotemporal dynamics of visually-informed planning in complex environments.

This study investigates how sensory modality and control dynamics affect human path integration in virtual reality, finding that vestibular signals alone are insufficient without sustained acceleration.

This study investigates visual navigation in ASD using a computational task, finding increased variability but intact integration, with reduced variance through feedback.

The paper investigates whether neurons in the macaque posterior parietal cortex encode self-motion speed and finds that a fraction of neurons do so linearly, supporting path integration.

The paper infers decoding strategies for two cortical areas in macaque monkeys using a mathematical framework that combines neural activity and inactivation experiments to resolve conflicting conclusions about brain function.

This study uses a dynamic Bayesian observer model to show that biases in visual path integration arise from a slow-speed prior rather than imperfect integration.

The paper examines gamma-band neuronal coherence in macaque primary visual cortex and finds it correlates more with firing rate than LFP oscillations, suggesting adaptive sensitivity changes maintain spatial resolution.