Rajesh P. N. Rao

This study shows that data heterogeneity limits the scaling benefits of pretraining neural data transformers in neuroscience datasets, suggesting that careful session selection can improve performance.

This paper introduces a hierarchical model of sequence learning and prediction in the neocortex, showing how it develops temporal response hierarchies and supports memory recall.

The paper analyzes neural dynamics in macaque motor cortex across behavioral states using LFP and spike data, finding distinct frequency band patterns and coupling during different states.

This study examines how cortical neurons in awake primates respond to intracortical microstimulation, revealing complex dynamics including excitatory responses and inhibitory periods.

This paper proposes a theory of the neocortex based on active predictive coding, demonstrating how it can solve various sensory-motor problems through hierarchical modeling and planning.

This paper develops cross-modal, self-supervised neural decoders using multiple data streams to achieve high decoding accuracy without labeled data.

AJILE12 provides a large dataset of intracranial neural recordings and upper body pose data from 12 participants over 55 days, with metadata on movement and behavior.

This paper introduces APCNs, a neural network framework that learns reference frames and parses visual scenes into part-whole hierarchies, demonstrating compositional representation and transfer learning on standard datasets.

The paper shows that perceptual confidence and its deviations from accuracy can be explained by a Bayesian framework with incomplete knowledge of the environment.

The paper analyzes neural variability during naturalistic arm movements using intracranial recordings and machine learning, finding significant variability across subjects and events.

This paper introduces a dynamic predictive coding model using hypernetworks to predict spatiotemporal patterns in natural image sequences, showing it learns visual cortex-like receptive fields.

This paper introduces HTNet, a neural decoder that generalizes across participants and recording modalities, achieving performance comparable to tailored decoders with minimal data.

The paper shows humans use Bayesian inference to model group dynamics in decision-making, outperforming existing models in explaining behavior in the Volunteers Dilemma.