Vijay Balasubramanian

This paper introduces an information compass framework to model probabilistic reasoning in biological and artificial systems using information geometry.

The paper quantifies human inference variability using an information-bottleneck framework, finding that performance differences stem from variable information capacity while efficiency remains consistent.

This paper introduces a unified recurrent network model that co-emerges grid and place cells without supervision or pre-existing spatial-cell representations, reproducing experimental observations.

The paper shows that spatial remapping in the hippocampal formation follows low-dimensional geometric transformations across environments.

This paper shows that place and time cells emerge from the same recurrent network model under different input conditions, suggesting a shared origin but task-driven differences.

This paper shows that visually naive chicks prefer specific multipoint correlation patterns, similar to humans and rats, and that early visual experience can alter these preferences.

This study explains how different ocular dominance patterns in the visual cortex arise from local Hebbian competition, depending on eye projection density and cortical interactions.

This paper proposes a system-level theory of MEC-HC wiring to explain how grid and place cell patterns enable cue-triggered goal retrieval and path planning, validated through simulations.

The paper shows that place cells emerge in networks trained to reconstruct temporally continuous sensory experiences, reproducing key hippocampal phenomena like remapping and place field orthogonality.

This study identifies universal low-frequency temporal features in vocalizations across species, showing they span a common acoustic space and predict future time courses.

The paper uses a geometric approach to analyze how changes in place cell firing properties affect the contextual capacity of the hippocampus, revealing a trade-off between spatial resolution and context storage.

The paper shows that learning temporally regular features improves auditory object discrimination compared to conventional methods.

This study shows humans prefer simpler explanations for uncertain data, aligning with formal model selection theories that penalize excessive flexibility.

The paper introduces a scale-dependent measure of system dimensionality and applies it to neural recordings, showing differences in effective dimensionality between behaviorally engaged and spontaneous brain states.

The paper develops a large-scale computational model of the olfactory bulb network, showing how connectivity and dynamics shape odor processing and perception.

This study shows that rats' sensitivity to image statistics decreases from 2- to 4-point correlations, mirroring human findings and supporting efficient coding principles.

The paper proposes a statistical mechanism for how context changes odor perception through feedback and cortical gating, predicting convergence and divergence trends based on odor similarity.

The paper introduces a decoding method for olfactory systems by focusing on non-responsive receptors to identify what an odor is not, improving accuracy in determining odorants and their concentrations.

The paper shows that visual representations become more stable along rodent visual processing hierarchies, with intrinsic dynamics contributing to this stability.

The paper tests the 'variance is salience' hypothesis by showing it accurately predicts visual sensitivity to second-order grayscale image statistics in texture-segregation tasks.

The paper investigates the balance between complexity and accuracy in inference strategies and finds a complexity dividend in moderately uncertain environments.

The paper proposes a geometric mechanism for self-organization of entorhinal grid modules through inhibition and excitation interactions in the mEC.

The paper develops a competitive binding model to predict olfactory receptor responses to complex odorant mixtures, achieving 15% accuracy using single-odorant responses.

The paper explains how olfactory receptor abundances adapt to efficiently encode odor information, showing context-dependent changes predicted by efficient coding theory.

This study shows that environmental deformations cause dynamic shifts in grid cell spatial metrics, contributing to the appearance of rescaling.

This paper introduces a model where border cells correct drift in grid cells through Hebbian plasticity, demonstrating a dynamic self-organization mechanism for robust path integration.

The paper shows that optogenetic manipulation of inhibition and excitation in the auditory cortex can paradoxically alter stimulus selectivity and behavioral frequency discrimination acuity, with cortical activity changes predicting behavioral outcomes.

The paper proposes a theory of olfactory circuit architecture in Drosophila that uses disorder to process complex odors, tested with empirical data.

The paper uses statistical physics to show that grid cell modularity optimizes spatial coverage in the brain.