Biology subjects

Tsao, D. Y.

Publications and source records attributed to Tsao, D. Y..

9 recordsLinked to original sources

bioRxiv2026

Rapid alternation of conscious and suppressed stimulus representations in the primate brain

The neural correlates of consciousness remain elusive [1]. Previous studies found that high-level visual cortex is weakly modulated by conscious perception [2,3], yet a pure signal of conscious perception has not been found anywhere in the brain. Here, we employed Neuropixels recordings across multiple face patches in inferotemporal and prefrontal cortex during a binocular flash suppression paradigm [4], in which one of two images is consciously perceived while the other, though continuously present, is suppressed from awareness. We found that, during a constant stimulus and percept, the brain rapidly alternates, about 15 times per second, between conscious and suppressed micro-epochs. During conscious micro-epochs, a pure signal of conscious perception was present, allowing accurate reconstruction of the conscious image from neural activity. During suppressed micro-epochs this reversed: the pure signal now tracked the suppressed stimulus, allowing accurate reconstruction of the suppressed image. At the single-cell level, neurons responded selectively to the conscious stimulus and not to the suppressed stimulus during conscious micro-epochs; conversely, during suppressed micro-epochs, neurons responded selectively to the suppressed stimulus. Despite this apparent symmetry, the two epochs could be reliably distinguished, with feature tuning amplified during conscious micro-epochs. Recordings in prefrontal cortex yielded qualitatively similar results. Together, these findings show that both inferotemporal and prefrontal cortex rapidly interleave conscious with suppressed content, indicating that conscious access requires temporal selection beyond localization.

neuroscience↗

bioRxiv2025

Mapping function in the tree shrew visual system using functional ultrasound imaging.

We adapted functional ultrasound imaging (fUSI) for awake, head-fixed northern tree shrews and used it to produce functional maps of visual processing at [~]100 {micro}m spatial resolution and [~]100 ms temporal resolution. Using classical retinotopic stimuli, full-field noise, motion localizers, and object versus scrambled object contrasts, we demonstrate robust, spatially specific hemodynamic responses across primary and extrastriate visual cortex, superior colliculus and subcortical structures. fUSI reliably reveals retinotopic reversals, laterality, and stimulus-selective modules, and yields high signal-to-noise %CBV changes that enable single-session mapping and targeting of electrophysiology or perturbations. These mesoscale maps provide a systems-level complement to recent high-density electrophysiological surveys of tree shrew visual cortex, which reported a compressed ventral-stream hierarchy and surprisingly early emergence of object coding in V2 (Lanfranchi et al., 2025). Together, our results establish fUSI as a powerful, scalable tool for functional mapping in the tree shrew, bridging large-scale circuit measurement and single-neuron electrophysiology and accelerating this species utility as a bridge between rodent genetics and primate vision.

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bioRxiv2024

A shared code for perceiving and imagining objects in human ventral temporal cortex

Mental imagery is a remarkable phenomenon that allows us to remember previous experiences and imagine new ones. Animal studies have yielded rich insight into mechanisms for visual perception, but the neural mechanisms for visual imagery remain poorly understood. Here, we first determined that [~]80% of visually responsive single neurons in human ventral temporal cortex (VTC) use a distributed axis code to represent objects. We then used that code to reconstruct objects and generate maximally effective synthetic stimuli. Finally, we recorded responses from the same neural population while subjects imagined specific objects and found that [~]40% of axis-tuned VTC neurons recapitulated the visual code. Our findings reveal that visual imagery is supported by reactivation of the same neurons involved in perception, providing single neuron evidence for the existence of a generative model in human VTC. One Sentence SummarySingle neurons in human temporal cortex use feature axes to encode objects, and imagery reactivates this code.

neuroscience↗

bioRxiv2023

Rapid, concerted switching of the neural code in inferotemporal cortex

A fundamental paradigm in neuroscience is the concept of neural coding through tuning functions1. According to this idea, neurons encode stimuli through fixed mappings of stimulus features to firing rates. Here, we report that the tuning of visual neurons can rapidly and coherently change across a population to attend to a whole and its parts. We set out to investigate a longstanding debate concerning whether inferotemporal (IT) cortex uses a specialized code for representing specific types of objects or whether it uses a general code that applies to any object. We found that face cells in macaque IT cortex initially adopted a general code optimized for face detection. But following a rapid, concerted population event lasting < 20 ms, the neural code transformed into a face-specific one with two striking properties: (i) response gradients to principal detection-related dimensions reversed direction, and (ii) new tuning developed to multiple higher feature space dimensions supporting fine face discrimination. These dynamics were face specific and did not occur in response to objects. Overall, these results show that, for faces, face cells shift from detection to discrimination by switching from an object-general code to a face-specific code. More broadly, our results suggest a novel mechanism for neural representation: concerted, stimulus-dependent switching of the neural code used by a cortical area.

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bioRxiv2022

Rapid learning with highly localized synaptic plasticity

The brains of all animals are plastic, allowing us to form new memories, adapt to new environments, and to learn new tasks. What is less clear is how much plasticity is required to perform these cognitive functions: does learning require widespread plasticity across the brain, or can learning occur with more rigid networks, in which plasticity is highly localized? Here, we use biologically-inspired recurrent neural network (RNN) models to show that rapid multitask learning can be accomplished in reservoir-style networks, in which synaptic plasticity is sparse and highly localized. Crucially, only RNNs initialized with highly specific combinations of network properties, such as topology, normalization and reciprocal connection strength, are capable of such learning. Finally, we show that this rapid learning with localized plasticity can be accomplished with purely local error signals, without backpropagation, using a reinforcement learning setup. This work suggests that rapid learning in artificial (and potentially biological) agents can be accomplished with mostly-rigid networks, in which synaptic plasticity is highly constrained.

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bioRxiv2021

Mice and primates use distinct strategies for visual segmentation

The rodent visual system has attracted great interest in recent years due to its experimental tractability, but the fundamental mechanisms used by the mouse to represent the visual world remain unclear. In the primate, researchers have argued from both behavioral and neural evidence that a key step in visual representation is "figure-ground segmentation," the delineation of figures as distinct from backgrounds [1-4]. To determine if mice also show behavioral and neural signatures of figure-ground segmentation, we trained mice on a figure-ground segmentation task where figures were defined by gratings and naturalistic textures moving counterphase to the background. Unlike primates, mice were severely limited in their ability to segment figure from ground using the opponent motion cue, with segmentation behavior strongly dependent on the specific carrier pattern. Remarkably, when mice were forced to localize naturalistic patterns defined by opponent motion, they adopted a strategy of brute force memorization of texture patterns. In contrast, primates, including humans, macaques, and mouse lemurs, could readily segment figures independent of carrier pattern using the opponent motion cue. Consistent with mouse behavior, neural responses to the same stimuli recorded in mouse visual areas V1, RL, and LM also did not support texture-invariant segmentation of figures using opponent motion. Modeling revealed that the texture dependence of both the mouses behavior and neural responses could be explained by a feedforward neural network lacking explicit segmentation capabilities. These findings reveal a fundamental limitation in the ability of mice to segment visual objects compared to primates.

neuroscience↗

bioRxiv2021

The geometry of face memory

The ability to recognize familiar visual objects is critical to survival. A central assumption of neuroscience is that long-term memories are represented by the same brain areas that encode sensory stimuli (1). Neurons in inferotemporal (IT) cortex represent the sensory percept of visual objects using a distributed axis code (2-4). Whether and how the same IT neural population represents the long-term memory of visual objects remains unclear. Here, we examined how familiar faces are encoded in face patch AM and perirhinal cortex. We found that familiar faces were represented in a distinct subspace from unfamiliar faces. The familiar face subspace was shifted relative to the unfamiliar face subspace at short latency and then distorted to increase neural distances between familiar faces at long latency. This distortion enabled markedly improved discrimination of familiar faces in both AM and PR. Inactivation of PR did not affect these memory traces in AM, suggesting that the memory traces arise from intrinsic recurrent processes within IT cortex or interactions with downstream regions outside the medial temporal lobe (5, 6). Overall, our results reveal that memories of familiar faces are represented in IT and perirhinal cortex by a distinct long-latency code that is optimized to distinguish familiar identities.

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bioRxiv2020

Representation of conscious percept without report in the macaque face patch network

A powerful paradigm to identify the neural correlates of consciousness is binocular rivalry, wherein a constant visual stimulus evokes a varying conscious percept. It has recently been suggested that activity modulations observed during rivalry could represent the act of report rather than the conscious percept itself. Here, we performed single-unit recordings from face patches in macaque inferotemporal (IT) cortex using a no-report paradigm in which the animals conscious percept was inferred from eye movements. We found high proportions of IT neurons represented the conscious percept even without active report. Population activity in single trials, measured using a new 128-site Neuropixels-like electrode, was more weakly modulated by rivalry than by physical stimulus transitions, but nevertheless allowed decoding of the changing conscious percept. These findings suggest that macaque face patches encode both the physical stimulus and the animals conscious visual percept, and the latter encoding does not require active report.

neuroscience↗

bioRxiv2018

Representation of multiple objects in macaque category-selective areas

Object recognition in the natural world usually occurs in the presence of multiple surrounding objects, but responses of neurons in inferotemporal (IT) cortex, the large brain area responsible for object recognition, have mostly been studied only to isolated objects. We study rules governing responses to multiple objects by cells in two category-selective regions of macaque IT cortex, the middle lateral face patch (ML) and the middle body patch (MB). We find that responses of single ML and MB cells to pairs of objects could be explained by the widely-accepted framework of normalization, with one added ingredient: homogeneous category selectivity of neighboring neurons forming the normalization pool. This rule leads to winner-take-all, contralateral-take-all, or weighted averaging behavior in single cells, depending on the category, spatial configuration, and relative contrast of the two objects. The winner-take-all behavior suggests a potential mechanism for clutter-invariant representation of face and bodies under certain conditions.

neuroscience↗