11 citations · 22 across the 9 of their papers we have counts for
12 papers
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models
Jialin Wu, Meenakshi Khosla
Comparing large language models can reveal shared representational structure and systematic differences between them. However, aggregate similarity measures can obscure how agreeme…
Neural networks are more modular than single neurons suggest
Shreya Saha, Benjamin Bergen, Meenakshi Khosla
Neural networks routinely develop representations in which individual neurons contribute to multiple distinct functions, leading to the widespread view that such networks lack modu…
NeuroGen: activation optimized image synthesis for discovery neuroscience
Zijin Gu, Keith W. Jamison, Meenakshi Khosla +6
Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a prio…
Multi-parameter Optimization for Ground-state Cooling of Mechanical Mode using Quantum Dots
Neelesh Kumar Vij, Meenakshi Khosla, Shilpi Gupta
Cooling a mechanical mode to its motional ground state opens up avenues for both scientific and technological advancements in the field of quantum meteorology and information proce…
Neural encoding with visual attention
Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2
Visual perception is critically influenced by the focus of attention. Due to limited resources, it is well known that neural representations are biased in favor of attended locatio…
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
Gia H. Ngo, Meenakshi Khosla, Keith Jamison +2
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many mac…
A shared neural encoding model for the prediction of subject-specific fMRI response
Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2
The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models…
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human…
Machine learning in resting-state fMRI analysis
Meenakshi Khosla, Keith Jamison, Gia H. Ngo +2
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various u…
Ensemble learning with 3D convolutional neural networks for connectome-based prediction
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on pre-processing choices, such as the parcellation scheme used to define regions of in…
3D Convolutional Neural Networks for Classification of Functional Connectomes
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, a…
Polarons Explain Luminescence Behavior of Colloidal Quantum Dots at Low Temperature
Meenakshi Khosla, Sravya Rao, Shilpi Gupta
Luminescence properties of colloidal quantum dots have found applications in imaging, light-sources, and displays. Despite wide interest, several experimental observations from the…