246 citations · 552 across the 25 of their papers we have counts for
25 papers
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
Yingtian Tang, Sogand Salehi, Ming Zhou +3
The human brain processes dynamic visual input through hierarchically organized, functionally specialized regions. While recent in silico brain encoding models can synthesize optim…
Reward Valuation in Large Language Models: Causal Induction of Anhedonia
Melika Honarmand, Samin Mahdipour Aghabagher, Martin Schrimpf
Recent frontier models mimic complex aspects of human cognition. Here we ask whether this alignment extends into reward valuation, which we assess in a mechanistic framework. Speci…
Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model
Badr AlKhamissi, Johannes Mehrer, Lara Marinov +3
Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems. Recent topographic models reproduce aspect…
MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding
Abdulkadir Gokce, Badr AlKhamissi, Martin Schrimpf
Recent progress in task-optimized neural networks has established encoding models as a powerful tool for predicting brain responses to naturalistic stimuli, yet most existing appro…
Model-Guided Microstimulation Steers Primate Visual Behavior
Johannes Mehrer, Ben Lonnqvist, Anna Mitola +3
Brain stimulation is a powerful tool for understanding cortical function and holds promise for therapeutic interventions in neuropsychiatric disorders. Initial visual prosthetics a…
Inducing Dyslexia in Vision Language Models
Melika Honarmand, Ayati Sharma, Badr AlKhamissi +2
Dyslexia, a neurodevelopmental disorder characterized by persistent reading difficulties, is often linked to reduced activity of the visual word form area (VWFA) in the ventral occ…
Evaluating Contrast Localizer for Identifying Causal Units in Social & Mathematical Tasks in Language Models
Yassine Jamaa, Badr AlKhamissi, Satrajit Ghosh +1
This work adapts a neuroscientific contrast localizer to pinpoint causally relevant units for Theory of Mind (ToM) and mathematical reasoning tasks in large language models (LLMs)…
Mixture of Cognitive Reasoners: Modular Reasoning with Brain-Like Specialization
Badr AlKhamissi, C. Nicolò De Sabbata, Greta Tuckute +3
Human cognitive behavior arises from the interaction of specialized brain networks dedicated to distinct functions, such as language, logic, and social reasoning. Inspired by this…
Contour Integration Underlies Human-Like Vision
Ben Lonnqvist, Elsa Scialom, Abdulkadir Gokce +3
Despite the tremendous success of deep learning in computer vision, models still fall behind humans in generalizing to new input distributions. Existing benchmarks do not investiga…
From Language to Cognition: How LLMs Outgrow the Human Language Network
Badr AlKhamissi, Greta Tuckute, Yingtian Tang +3
Large language models (LLMs) exhibit remarkable similarity to neural activity in the human language network. However, the key properties of language shaping brain-like representati…
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
Abdulkadir Gokce, Martin Schrimpf
When trained on large-scale object classification datasets, certain artificial neural network models begin to approximate core object recognition behaviors and neural response patt…
The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units
Badr AlKhamissi, Greta Tuckute, Antoine Bosselut +1
Large language models (LLMs) exhibit remarkable capabilities on not just language tasks, but also various tasks that are not linguistic in nature, such as logical reasoning and soc…
Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible Data
Badr AlKhamissi, Yingtian Tang, Abdülkadir Gökce +2
While today's large language models exhibit impressive abilities in generating human-like text, they require massive amounts of data during training. We here take inspiration from…
TopoLM: brain-like spatio-functional organization in a topographic language model
Neil Rathi, Johannes Mehrer, Badr AlKhamissi +3
Neurons in the brain are spatially organized such that neighbors on tissue often exhibit similar response profiles. In the human language system, experimental studies have observed…
Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network
Badr AlKhamissi, Greta Tuckute, Antoine Bosselut +1
Large Language Models (LLMs) have been shown to be effective models of the human language system, with some models predicting most explainable variance of brain activity in current…
Instruction-tuning Aligns LLMs to the Human Brain
Khai Loong Aw, Syrielle Montariol, Badr AlKhamissi +2
Instruction-tuning is a widely adopted finetuning method that enables large language models (LLMs) to generate output that more closely resembles human responses. However, no studi…
Beyond linear regression: mapping models in cognitive neuroscience should align with research goals
Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti +3
Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to repres…
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
Chuang Gan, Jeremy Schwartz, Seth Alter +21
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between m…
Frivolous Units: Wider Networks Are Not Really That Wide
Stephen Casper, Xavier Boix, Vanessa D'Amario +4
A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network's width is increased. Recent evidence suggests…
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Jonas Kubilius, Martin Schrimpf, Kohitij Kar +11
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially…
Continual Learning with Self-Organizing Maps
Pouya Bashivan, Martin Schrimpf, Robert Ajemian +3
Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-specific stationary environments where they a…
A Flexible Approach to Automated RNN Architecture Generation
Martin Schrimpf, Stephen Merity, James Bradbury +1
The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recu…
Recurrent computations for visual pattern completion
Hanlin Tang, Martin Schrimpf, Bill Lotter +6
Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occlude…
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
Nicholas Cheney, Martin Schrimpf, Gabriel Kreiman
Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images…