Biology subjects

Yamins, D.

Publications and source records attributed to Yamins, D..

4 recordsLinked to original sources

bioRxiv2025

Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective

How the human brain supports diverse behaviours has been debated for decades. The canonical account posits distinct pathways in perceptual processing, such as the "what" and "where/how" visual streams, though their developmental origins and interdependency remain contested. Here, we show that families of deep neural network models develop features that accurately predict hours of human neural and behavioural recordings. By using these models as proxies for the brain and systematically probing developmental objectives, we identify two fundamental computations: object and appearance-free motion recognition, which drive the visual processing hierarchy and explain away alternative accounts. Strikingly, a single objective underlies both: they emerge from optimising for understanding world dynamics, with their organisation highly distributed and continuous across cortex rather than segregating into stream-like modes. Our results suggest that the human brains ability to integrate complex perceptual information across seemingly distinct pathways may originate from the single goal of modelling the world.

neuroscience↗

bioRxiv2022

When and why grid cells appear or not in trained path integrators

Recent work has claimed that the emergence of grid cells from trained path-integrator circuits is a more fragile phenomenon than previously reported. In this note we critically assess the main analysis and simulation results underlying this claim, within the proper context of previously published theoretical work. Our assessment reveals that the emergence of grid cells is entirely consistent with this prior theory: hexagonal grid cells robustly emerge precisely when prior theory predicts they should, and dont when prior theory predicts they should not.

neuroscience↗

bioRxiv2020

Unsupervised Neural Network Models of the Ventral Visual Stream

Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using todays best supervised methods, and that the mapping of these neural network models hidden layers is neuroanatomically consistent across the ventral stream. Moreover, we find that these methods produce brain-like representations even when trained on noisy and limited data measured from real childrens developmental experience. We also find that semi-supervised deep contrastive embeddings can leverage small numbers of labelled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results suggest that deep contrastive embedding objectives may be a biologically-plausible computational theory of primate visual development.

neuroscience↗

bioRxiv2019

Relating visual production and recognition of objects in human visual cortex

Drawing is a powerful tool that can be used to convey rich perceptual information about objects in the world. What are the neural mechanisms that enable us to produce a recognizable drawing of an object, and how does this visual production experience influence how this object is represented in the brain? Here we evaluate the hypothesis that producing and recognizing an object recruit a shared neural representation, such that repeatedly drawing the object can enhance its perceptual discriminability in the brain. We scanned participants using fMRI across three phases of a training study: during training, participants repeatedly drew two objects in an alternating sequence on an MR-compatible tablet; before and after training, they viewed these and two other control objects, allowing us to measure the neural representation of each object in visual cortex. We found that: (1) stimulus-evoked representations of objects in visual cortex are recruited during visually cued production of drawings of these objects, even throughout the period when the object cue is no longer present; (2) the object currently being drawn is prioritized in visual cortex during drawing production, while other repeatedly drawn objects are suppressed; and (3) patterns of connectivity between regions in occipital and parietal cortex supported enhanced decoding of the currently drawn object across the training phase, suggesting a potential substrate for learning how to transform perceptual representations into representational actions. Taken together, our study provides novel insight into the functional relationship between visual production and recognition in the brain.\n\nSignificance StatementHumans can produce simple line drawings that capture rich information about their perceptual experiences. However, the mechanisms that support this behavior are not well understood. Here we investigate how regions in visual cortex participate in the recognition of an object and the production of a drawing of it. We find that these regions carry diagnostic information about an object in a similar format both during recognition and production, and that practice drawing an object enhances transmission of information about it to downstream regions. Taken together, our study provides novel insight into the functional relationship between visual production and recognition in the brain.

neuroscience↗