14 citations · 25 across the 7 of their papers we have counts for
7 papers
Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli
Christopher Wang, Adam Uri Yaari, Aaditya K Singh +10
We present the Brain Treebank, a large-scale dataset of electrophysiological neural responses, recorded from intracranial probes while 10 subjects watched one or more Hollywood mov…
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…
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex
Spandan Madan, Will Xiao, Mingran Cao +3
We characterized the generalization capabilities of DNN-based encoding models when predicting neuronal responses from the visual cortex. We collected \textit{MacaqueITBench}, a lar…
Sparse Distributed Memory is a Continual Learner
Trenton Bricken, Xander Davies, Deepak Singh +2
Continual learning is a problem for artificial neural networks that their biological counterparts are adept at solving. Building on work using Sparse Distributed Memory (SDM) to co…
BrainBERT: Self-supervised representation learning for intracranial recordings
Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari +4
We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition…
A normalization model of visual search predicts single trial human fixations in an object search task
Thomas Miconi, Laura Groomes, Gabriel Kreiman
When searching for an object in a scene, how does the brain decide where to look next? Theories of visual search suggest the existence of a global attentional map, computed by inte…