Biology subjects

Ratan Murty, N. A.

Publications and source records attributed to Ratan Murty, N. A..

9 recordsLinked to original sources

bioRxiv2026

Braintree: An agentic framework for in-silico vision neuroscience

Vision neuroscience has many of the ingredients needed to conduct experiments entirely in-silico. We need AI systems that can reason over scientific knowledge, generative methods for creating precisely controlled stimuli, image-computable models that can predict neural responses with increasing accuracy, and tools that can analyze and interpret data. Here we take the first step toward making a vision neuroscience experiment end-to-end computable. We introduce Braintree, an agentic framework that can autonomously run multi-experiment studies directly with in-silico models of human visual cortex. Given a hypothesis, Braintree can reason over the current literature, design an experiment, build a stimulus set from algorithmically manipulated or synthesized stimuli, run the experiment on candidate brain encoding models, analyze the responses and then use the results to design even stronger tests. The entire loop runs autonomously and can accept human input at any stage. We evaluated Braintree on three increasingly open-ended scientific problems. First, Braintree reproduced published findings using their original stimuli and then asked whether the same effects could be replicated with entirely new stimulus sets that it constructed. Second, it designed new experiments to find stimulus dimensions that could drive two brain regions differently, even when those regions otherwise had similar response profiles. Third, it searched for experiments that could tell competing models of the same brain region apart. Experiments discovered using just two models also exposed differences across a much broader set of models, suggesting that Braintree had found more general dimensions along which brain models disagree. Braintree can augment the capabilities of a vision neuroscientist as a verifier for existing hypotheses, a test-bed for new ideas, and/or a tool for generating stimuli that can discriminate between competing hypotheses or models of the brain. Braintree shows how AI agents and brain models can work together to make in-silico vision neuroscience experiment computable, allowing vision scientists to explore many more experiment conditions and identify the most informative ones to test on the human brain.

neuroscience↗

bioRxiv2026

Standardized human tests show that most vision models are face-blind

Face recognition ability varies enormously across humans. Individuals with face-blindness (prosopagnosia) struggle to recognize even close family members while super-recognizers can identify strangers with exceptional accuracy. Where do ANNs fall within the human face recognition spectrum? Here, we administered the standardized tests used to characterize human face recognition ability to a diverse set of models, allowing us to contextualize a model's performance within the distribution of human behavior. We found that the majority 55% of models to be classified as face-blind and that even the best face-trained models do not cross the human threshold to be considered a super-recognizer. Interrogating the internal representations of these models showed that models that performed well on standardized face recognition tasks were more identity-selective and viewpoint-invariant. We also found that low-performing models did contain some identity information in independent representational subspaces. Removing viewpoint-dependent subspace improved face recognition abilities in 49 of the 53 models tested. Targeted unit ablations further identified opposing contributions, with viewpoint-dependent units disrupting identity coding and viewpoint-tolerant units supporting it. Together, our results show that most AI models are face-blind with worse face recognition ability than humans, and demonstrate how differences across AI models can be used generate testable hypotheses about the computational basis of human face recognition in humans which can then be probed in future studies.

neuroscience↗

bioRxiv2026

Zero-shot evaluations expose structured generalization limits in predictive models of the brain

Artificial neural networks (ANNs) can predict neural responses to natural images with remarkable accuracy, making them promising models of human vision. However, prediction scores alone reveal little about what computations these models have captured or how well they generalize beyond the images on which they are trained. Here, we introduce a principled zero-shot model evaluation framework in which ANN-based encoding models are frozen before being tested on not just held out images but entirely new datasets. Our strongest tests repurpose decades of cognitive neuroscience experiments as diagnostic benchmarks for identifying which computations current models capture and where they fail. To implement this framework we built encoding models of human category-selective regions (FFA, PPA, and EBA) using several pretrained ANNs and evaluated these fixed models across 7 independent fMRI datasets and 31 cognitive experiments from 10 published studies. This framework revealed three broad patterns. First, zero-shot evaluations exposed systematic limits to model predictivity that were largely hidden by standard within-dataset cross-validation. These limits were structured varying across brain regions and model classes and depending strongly on the similarity between the images used to build and test the models. Second, current models reproduced some, but not all, classical findings from cognitive neuroscience, providing a diagnosis of the computations that current models have yet to capture. Finally, models that performed well on prediction tests also tended to succeed at reproducing cognitive neuroscience findings, suggesting that prediction and explanation are closely linked. Unlike prediction scores however, cognitive tests help diagnose where and why models fail. Together, the zero-shot evaluation framework provides a scalable and unified approach for evaluating brain models across prediction and cognitive neuroscience tests, which can help us better understand what current models capture and what still remains to be explained.

neuroscience↗

bioRxiv2026

Category selectivity observed in the human brain is distinct from category selectivity observed in artificial neural networks

Category selectivity for images of faces, scenes, and bodies is among the most striking and reproducible findings in vision neuroscience. Artificial neural networks (ANNs) trained on visual tasks also develop category-selective units, which has led to the suggestion that ANNs may capture important aspects of how the brain processes visual categories. But the mere presence of category-selective units in ANNs does not mean that those units are selective in the same way as the brain. Here, we distinguish between the presence of category-selective units in ANNs from the form of selectivity they express, and show that the selectivity that emerges in ANN units differs in meaningful and systematic ways from that observed in the human brain with fMRI. To this end, we first identified category-selective units in a wide range of ANN models using standard fMRI localizers, and found that selective units emerged reliably in trained, but not in untrained, ANNs. We then identified category-selective regions in the human brain using the same localizer and found that their response tuning to a broad range of images was strikingly consistent across individuals. Thus, category-selective regions exhibit a stable representational signature shared across subjects. Category-selective ANN units did not match this structure. Their responses diverged in both univariate tuning and multivariate representational geometry, fell well below the human-human ceiling, varied substantially across models, and depended strongly on the localizer used to identify them. We also found that the category-selective ANN units were neither necessary nor sufficient for predicting neural responses using an encoding model. Further stimulus-level analyses revealed clear and interpretable mismatches between ANN selectivity and human fMRI responses, which can be used to test and compare better ANN models in the future. Taken together, these results show that the full range of response tuning in category-selective regions provides a highly demanding and discriminative test of brain-model alignment than previously appreciated. Although current ANNs contain category-selective units, the selectivity they express is more fragile and does not capture the stable and shared form of selectivity observed in the human brain.

neuroscience↗

bioRxiv2026

Can category-selective cortex predict categorisation behaviour?

One of the distinctive features of the human visual system is the presence in occipito-temporal cortex (OTC) of regions that show preferential activation to specific categories of visual objects. To understand how this selectivity relates to categorisation behaviour, studies have employed a distance-to-bound approach (DTB), where multivariate brain activity is used to estimate a decision boundary, from which behavioural performance can be predicted. Using this approach, correlations have been found between activity in OTC, and behavioural performance when carrying out certain categorisation tasks. However, it remains unclear what determines where in OTC this correlations can be found, and with which categorisation tasks they can be found. Here, we bridged this gap by relating category-selective regions of OTC, to behavioural performance while participants categorised images as belonging or not to their preferred categories. We adopted a more basic approach and considered simple, univariate activity, rather than relying on decoding to build our DTB. Our results show that activation in regions selective to faces (FFA & OFA), bodies (EBA), and scenes (PPA), is sufficient to predict behavioural performance while categorising images as being faces, bodies, or scenes, respectively. These results are largely consistent across reaction time and motor movements, and generalise to animacy classification. Overall, our data adds to evidence that category-selective regions in OTC can serve to guide categorisation behaviour, and underlines the validity of the DTB approach to address this relationship.

neuroscience↗

bioRxiv2025

Human-like individual differences emerge from random weight initializations in neural networks

Much of AI research targets the behavior of an average human, a focus that traces to Turings imitation game. Yet, no two human individuals behave exactly alike. In this study, we show that artificial neural networks (ANNs) trained with different random initializations exhibit substantial individual differences that resemble those in humans. Using a large dataset (N = 60) of human responses (accuracy, confidence, & response time) in a digit recognition task, we trained multiple instances of three ANN architectures on the same task, creating as many ANN instances as human subjects. We found that these ANN instances vary significantly from one another. Critically, ANN instances showed consistent variation in their alignment with specific human subjects. This consistency in alignment between ANN instances and humans extended across behavioral metrics, indicating that an ANN instance mimicking an individual on one metric also does so on others. Finally, we showed that leveraging these alignments improves predictions of individual human responses. Our findings highlight the potential of ANNs to capture human variability, opening new directions to develop models that go beyond aligning the average human and instead aligning the idiosyncratic behavior of specific individuals.

neuroscience↗

bioRxiv2023

BOLD Moments: modeling short visual events through a video fMRI dataset and metadata

Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips of visual events across ten human subjects. We use the videos extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.

neuroscience↗

bioRxiv2022

A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition

Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of visual cortex, or what others might exist that scientists have not thought to look for. Further, standard voxel-wise tests cannot detect distinct neural selectivities that coexist within voxels. To overcome these limitations, we used data-driven voxel decomposition methods to identify the main components underlying fMRI responses to thousands of complex photographic images (Allen et al 2021). Our hypothesis-neutral analysis rediscovered components selective for faces, places, bodies, and words, validating our method and showing that these selectivities are dominant features of the ventral visual pathway. The analysis also revealed an unexpected component with a distinct anatomical distribution that responded highly selectively to images of food. Alternative accounts based on low to mid-level visual features like color, shape or texture failed to account for the food selectivity of this component. High-throughput testing and control experiments with matched stimuli on a highly accurate computational model of this component confirm its selectivity for food. We registered our methods and hypotheses before replicating them on held-out participants and in a novel dataset. These findings demonstrate the power of data-driven methods, and show that the dominant neural responses of the ventral visual pathway include not only selectivities for faces, scenes, bodies, and words, but also the visually heterogeneous category of food, thus constraining accounts of when and why functional specialization arises in the cortex.

neuroscience↗

bioRxiv2020

Visual Experience is not Necessary for the Development of Face Selectivity in the Lateral Fusiform Gyrus

Here we show robust face-selectivity in the lateral fusiform gyrus of congenitally blind participants during haptic exploration of 3D-printed stimuli, indicating that neither visual experience, nor fovea-biased input, nor visual expertise is necessary for face-selectivity to arise in its characteristic location. Similar resting fMRI correlation fingerprints in individual blind and sighted participants suggest a role for long-range connectivity in the specification of the cortical locus of face-selectivity.

neuroscience↗