J. Swaroop Guntupalli

Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

This paper introduces hybrid hyperalignment, a method that aligns both neural response and functional connectivity data across brains using a single model, showing improved alignment for both types of data compared to previous methods.

The neural basis of intelligence in fine-grained cortical topographies

This study predicts general intelligence using fine-grained cortical connectivity patterns, finding stronger predictions than coarse-grained patterns.

Learning cognitive maps for vicarious evaluation

This paper introduces a cognitive graph structure called CSCG to model how the hippocampus forms abstract cognitive maps, explaining various cognitive phenomena and place cell behaviors.

The role of auxiliary parameters in evaluating voxel-wise encoding models for 3T and 7T BOLD fMRI data

This study evaluates how auxiliary parameters affect voxel-wise encoding model validation in 3T and 7T BOLD fMRI data using two open datasets.

Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

This paper introduces a cloned HMM to model hippocampal cognitive maps as higher-order graphs, showing it captures key properties of hippocampal cells in rodents and humans.

Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models

This paper introduces a biologically plausible learning algorithm for Cloned HMMs that enables rapid memorization and gradual generalization of higher-order sequential structures.

Reliable individual differences in fine-grained cortical functional architecture

This study uses hyperalignment to examine individual differences in cortical functional architecture, showing it improves reliability across functional indices by resolving topographic idiosyncrasies.

A computational model of shared fine-scale structure in the human connectome

The paper develops a high-dimensional model of the human connectome that captures shared fine-scale structure, revealing it as a major component of connectivity previously overlooked by coarse-scale models.

Explaining Visual Cortex Phenomena using Recursive Cortical Network

This paper uses the Recursive Cortical Network to model and explain visual cortex phenomena like subjective contours and border-ownership competition.

Cortical Microcircuits from a Generative Vision Model

The paper derives cortical circuit models based on a generative vision model, assigning computational roles to different connections and pathways.

The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception

This study uses fMRI and multivariate pattern analysis to show that familiarity and face identity are represented in overlapping and distinct regions of the core and extended face processing systems.

Attention selectively reshapes the geometry of distributed semantic representation

The paper investigates how attention affects the discriminability of distributed neural representations for behavior and taxonomy in humans.

BIDS Apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

This paper introduces BIDS Apps, a framework for creating portable neuroimaging analysis tools compatible with BIDS data, improving reproducibility and ease of use.

An extension of the studyforrest dataset for vision research

The paper extends the studyforrest dataset to include vision and multi-sensory research with additional studies on visual function and brain mapping.

Disentangling the representation of identity from head view along the human face processing pathway.

This study uses fMRI decoding to show that identity representation becomes disentangled from head view in the right inferior frontal cortex along the human face processing pathway.