Biology subjects

Toosi, T.

Publications and source records attributed to Toosi, T..

3 recordsLinked to original sources

bioRxiv2025

Natural Scene Coding Consistency in Genetically-Defined Cell Populations

Understanding how genetically-defined cell populations encode visual information remains a fundamental challenge in systems neuroscience. While extensive research has characterized individual cell responses to simple stimuli such as static gratings, the population-level coding principles that govern naturalistic visual processing across cell types remain largely unexplored. We analyzed population responses from 43,018 neurons across 12 genetically-defined cell types in 243 mice from the Allen Brain Observatory, comparing representational geometry between natural scenes and static gratings. We found that inhibitory cell populations (VIP, SST, PV) cluster distinctly in representational space almost independent of anatomical location when responding to natural scenes but not static gratings, suggesting preserved cell-type specific computational functions specific for natural scenes. To assess coding capacity of a population of neurons, we developed Inter-Individual Representational Similarity (IIRS), which measures consistency of neural representations across different individuals in response to an ensemble of stimuli. All inhibitory populations showed significantly higher IIRS for natural scenes compared to static gratings, indicating consistent encoding of naturalistic visual features across individuals comparable to excitatory populations (Cux2, Rorb, Rbp4). Parallel analysis of neural networks trained on natural images (ImageNet) with different random initializations revealed similar patterns: models showed higher cross-initialization similarity for naturalistic stimuli compared to static gratings, suggesting that cross-individual consistency emerges when experimental stimuli engage the features that neural circuits are adapted to extract. These findings establish IIRS as a metric for identifying coding capacity in cell populations and reveal that inhibitory cell populations encode consistent aspects of natural scenes across individuals, indicating these circuits may have evolved specialized tuning for naturalistic visual environments.

neuroscience↗

bioRxiv2025

Generative inference unifies feedback processing for learning and perception in natural and artificial vision

We understand how neurons respond selectively to patterns in visual input to support object recognition; however, how these circuits support perceptual grouping, illusory percepts, and imagination is not understood. These perceptual experiences are thought to require combining what we have learned about the world with incoming sensory signals, yet the neural mechanism for this integration remains unclear. Here we show that networks tuned for object recognition implicitly learn the distribution of their input, which can be accessed through feedback connections that tune synaptic weights. We introduce Generative Inference, a computational framework in which feedback pathways that adjust connection weights during learning are repurposed during perception to combine learned knowledge with sensory input, fulfilling flexible inference goals such as increasing confidence. Generative Inference enables networks tuned solely for recognition to spontaneously produce perceptual grouping, illusory contours, shape completion, and pattern formation resembling imagination, while preserving their recognition abilities. The framework reproduces neural signatures observed across perceptual experiments: delayed responses in feedback-receiving layers of early visual cortex that disappear when feedback connections are disrupted. We show that, under stated assumptions, gradients of classification error approximate directions that are informative about the data distribution, establishing a theoretical connection between recognition and generation. Together, these findings show that pattern recognition and pattern generation rely on a shared computational substrate through dual use of feedback pathways. This principle explains how neural systems recognize familiar objects reliably while remaining flexible enough to interpret incomplete or ambiguous information, and suggests that reusing learning signals for perception may be a general feature of both biological brains and artificial networks.

neuroscience↗

bioRxiv2020

Conserved core visual object recognition across simian primates: Marmoset image-by-image behavior mirrors that of humans and macaques

Bigger brains are thought to support richer abilities, including perceptual abilities. But bigger brains are typically organized differently (e.g., with more cortical areas). Thus, the extent to which a neural systems size versus organization underlies complex abilities remains unclear. The marmoset monkey is evolutionarily peculiar: it has a small brain, yet many cortical areas. We used this natural experiment to test organization as source of high-level visual abilities independent of size, via large-scale psychophysics comparing marmosets to different species on identical tasks. Marmosets far out--performed rats--a marmoset-sized rodent--on a simple visual recognition task. On another visual task, which is difficult for both humans and machines, marmosets achieved high performance. Strikingly, their image-by-image behavior revealed that they did so in a manner highly similar to humans--marmosets were nearly as human-like as were macaques. These results suggest a key role for brain organization--not simply size--in the evolution of sophisticated abilities.

neuroscience↗