5 citations · 7 across the 9 of their papers we have counts for
7 papers · 1 filter
Network of Theseus (like the ship)
Vighnesh Subramaniam, Colin Conwell, Boris Katz +2
A standard assumption in deep learning is that the inductive bias introduced by a neural network architecture must persist from training through inference. The architecture you tra…
Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli
Andrii Zahorodnii, Christopher Wang, Geeling Chau +7
High-resolution neural datasets enable foundation models for the next generation of brain-computer interfaces and neurological treatments. The community requires rigorous benchmark…
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?
David Mayo, Christopher Wang, Asa Harbin +4
When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powe…
Training the Untrainable: Introducing Inductive Bias via Representational Alignment
Vighnesh Subramaniam, David Mayo, Colin Conwell +4
We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. We call a network…
Revealing Vision-Language Integration in the Brain with Multimodal Networks
Vighnesh Subramaniam, Colin Conwell, Christopher Wang +4
We use (multi)modal deep neural networks (DNNs) to probe for sites of multimodal integration in the human brain by predicting stereoencephalography (SEEG) recordings taken while hu…
Population Transformer: Learning Population-level Representations of Neural Activity
Geeling Chau, Christopher Wang, Sabera Talukder +5
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with ne…