Biology subjects

Fujita, I.

Publications and source records attributed to Fujita, I..

9 recordsLinked to original sources

bioRxiv2026

Reassessing Choice Probability: What 59 Macaque Studies Tell Us About Decision-Related Activity in Visual Cortex

Choice probability (CP) quantifies the trial-by-trial covariation between a sensory neurons response and perceptual reports. Despite extensive interest in CP as a window into how perception is linked to the activity of sensory neurons, the usefulness of CP remains debated. On one hand, reported CP magnitudes vary widely across seemingly equivalent studies, questioning its utility as a metric. On the other hand, the absence of clear patterns in CP variability has made it difficult to use it to adjudicate between competing models of visual perception. Here, we performed a meta-analysis of 150 CP estimates from 59 macaque neurophysiology studies to identify factors that systematically influence CP. We confirmed the positive relationship between CP and neuronal sensitivity both across and within individual studies. When controlled for sensitivity, we found remarkable consistency in CP across varying tasks and brain regions with two notable exceptions. First, CPs were higher in tasks involving bistable percepts, reinforcing the link between CP magnitude and subjective perception. Second, CPs were smaller in area V1, supporting prior suggestions about V1s special role in visual processing. We further found a significant effect of stimulus duration on CP, providing evidence against strictly feedforward models and favoring models with substantial feedback and recurrent processing. Finally, we offer recommendations for future studies to enhance the cross-study comparability and theoretical utility of choice signals as the field transitions to large-scale population recordings. More broadly, our findings demonstrate the benefits of meta-studies that expose patterns across many different tasks and animals - yielding insights that complement large-scale population recordings.

neuroscience↗

bioRxiv2026

Correlation-based binocular disparity computations induce representational bottlenecks at the population level

Binocular stereopsis depends on comparing the images seen by the two eyes. Although correlation-based models explain responses of individual binocular neurons in primary visual cortex (V1), it remains elusive whether population representations formed by local correlation activity patterns can support depth perception under ambiguous inputs. Using psychophysics, fMRI, and neural network modeling, we tested human stereopsis with dynamic anticorrelated stimuli that were dominated by binocular mismatches. Humans reliably perceived reversed depth as predicted by correlation-based computations, yet population representations consistent with this percept were detected in V3A, not V1. Shallow and deep neural networks constrained by correlation-like binocular interactions did not capture the full pattern of human depth judgments. Analyses of their internal representations showed greater representational overlap, whereas deep architectures not constrained to explicit correlation interactions exhibited less entangled representations and better aligned with human behavior. These findings suggest that biological stereopsis may rely on population coding beyond correlation-like computations. Significance StatementThe brain must infer depth from binocular inputs that are inherently ambiguous. Although correlation-based models explain disparity tuning of individual neurons in primary visual cortex (V1), whether these local mechanisms support perceptual inference at the population level remains unclear. Using psychophysics, fMRI, and neural network modeling, we show that population representations consistent with perceived depth under ambiguity were detected in mid-dorsal area V3A, not V1. Analyses of how neural networks encode multiple features showed that correlation-based computations represent features into overlapping activity patterns that may constrain downstream readout and degrade depth estimates. In contrast, models not constrained by explicit interocular correlation maintained more distinct population codes and closely matched human perception. These findings suggest that architectures constrained to explicit correlation-like processing can form population representations that are suboptimal to explain human stereopsis, motivating hybrid mechanisms.

neuroscience↗

bioRxiv2024

Correlation and Matching Representations of Binocular Disparity across the Human Visual Cortex

Seeing three-dimensional objects requires multiple stages of representational transformation, beginning in the primary visual cortex (V1). Here, neurons compute binocular disparity from the left and right retinal inputs through a mechanism similar to local cross-correlation. However, correlation-based representation is ambiguous because it is sensitive to disparities in both similar and dissimilar features between the eyes. Along the visual pathways, the representation transforms to a cross-matching basis, eliminating responses to falsely matched disparities. We investigated this transformation across the human visual areas using functional magnetic resonance imaging (fMRI) and computational modeling. By fitting a linear weighted sum of cross-correlation and cross-matching model representations to the brains representational structure of disparity, we found that areas V1-V3 exhibited stronger cross-correlation components, V3A/B, V7, and hV4 were slightly inclined towards cross-matching, and hMT+ was strongly engaged in cross-matching. To explore the underlying mechanism, we identified a deep neural network optimized for estimating disparity in natural scenes that matched human depth judgment in the random-dot stereograms used in the fMRI experiments. Despite not being constrained to match fMRI data, the network units responses progressed from cross-correlation to cross-matching across layers. Activation maximization analysis on the network suggests that the transformation incorporates three phases, each emphasizing different aspects of binocular similarity and dissimilarity for depth extraction. Our findings suggest a systematic distribution of both components throughout the visual cortex, with cross-matching playing a greater role in areas anterior to V3, and that the transformation exploits responses to false matches rather than discarding them. Significant StatementHumans perceive the visual world in 3D by exploiting binocular disparity. To achieve this, the brain transforms neural representation from the cross-correlation of signals from both eyes into a cross-matching representation, filtering out responses to disparities from falsely matched features. The location and mechanism of this transformation in the human brain are unclear. Using fMRI, we demonstrated that both representations were systematically distributed across the visual cortex, with cross-matching exerting a stronger effect in cortical areas anterior to V3. A neural network optimized for disparity estimation in natural scenes replicated human depth judgment in various stereograms and exhibited a similar transformation. The transformation from correlation to matching representation may be driven by performance optimization for depth extraction in natural environments.

neuroscience↗

bioRxiv2023

Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression

Perception of facial expression is crucial in the social life of primates. This visual information is processed along the ventral cortical pathway and the subcortical pathway. Processing of face information in the subcortical pathway is inaccurate, but the architectural and physiological properties that are responsible remain unclear. We analyzed the performance of convolutional neural networks incorporating three prominent properties of this pathway: a shallow layer architecture, concentric receptive fields at the first processing stage, and a greater degree of spatial pooling. The neural networks designed in this way could be trained to classify seven facial expressions with a correct rate of 51% (chance level, 14%). This modest performance was gradually improved by replacing the three properties, one-by-one, two at a time, or all three simultaneously, with the corresponding features in the cortical pathway. Some processing units in the final layer were sensitive to spatial frequencies (SFs) in the retina-based coordinate, whereas others were sensitive to object-based SFs, similar to neurons in the amygdala. Replacement of any one of these properties affected the SF coordinate of units. All three properties constrain the accuracy of facial expression information in the subcortical pathway, and are essential for determining the coordinate of SF representation.

neuroscience↗

bioRxiv2021

A mosaic adeno-associated virus vector as a versatile tool that exhibits high levels of transgene expression and neuron specificity in primate brain

Recent emphasis has been placed on gene transduction mediated through recombinant adeno-associated virus (AAV) vector to manipulate activity of neurons and their circuitry in the primate brain. In the present study, we created a novel AAV vector of which capsid was composed of capsid proteins derived from the serotypes 1 and 2 (AAV1 and AAV2). Following the injection into the frontal cortex of macaque monkeys, this mosaic vector, termed AAV2.1 vector, was found to exhibit the excellence in transgene expression (for the AAV1 vector) and neuron specificity (for the AAV2 vector) simultaneously. To explore its applicability to chemogenetic manipulation and in vivo calcium imaging, the AAV2.1 vector expressing excitatory DREADDs or GCaMP was injected into the striatum or the visual cortex of macaque monkeys, respectively. Our results have defined that such vectors secure intense and stable expression of the target proteins and yield conspicuous modulation and imaging of neuronal activity.

neuroscience↗

bioRxiv2020

Specialization of mid-tier stages of dorsal and ventral pathways in stereoscopic processing

The division of labor between the dorsal and ventral visual pathways is an influential model of parallel information processing in the cerebral cortex. However, direct comparison of the two pathways at the single-neuron resolution has been scarce. Here we compare how MT and V4, mid-tier areas of the two pathways in the monkey, process binocular disparity, a powerful cue for depth perception and visually guided actions. We report a novel tradeoff where MT neurons transmit disparity signals quickly and robustly, whereas V4 neurons markedly transform the nature of the signals with extra time to solve the stereo correspondence problem. Therefore, signaling speed and robustness are traded for computational complexity. The key factor in this tradeoff was the shape of disparity tuning: V4 neurons had more even-symmetric tuning than MT neurons. Moreover, this correlation between tuning shape and signal transformation was present across individual neurons within both MT and V4. Overall, our results reveal both distinct signaling advantages and common tuning-curve features of the dorsal and ventral pathways in stereoscopic processing.

neuroscience↗

bioRxiv2019

Reconstruction of Par polarity in apolar cells reveals a dynamic process of cortical polarization

Cellular polarization is fundamental for various biological processes. The Par network system is conserved for cellular polarization. Its core complex consists of Par3, Par6, and aPKC. However, the dynamic processes that occur during polarization are not well understood. Here, we artificially reconstructed Par-dependent polarity using non-polarized Drosophila S2 cells expressing all three components endogenously in the cytoplasm. The results indicated that elevated Par3 expression induces cortical localization of the Par-complex at the interphase. Its asymmetric distribution goes through three steps: emergence of cortical dots, development of island-like structures with dynamic amorphous shapes, repeating fusion and fission, and polarized clustering of the islands. Our findings also showed that these islands contain a meshwork of unit-like segments. Par-complex patches resembling Par-islands exist in Drosophila mitotic neuroblasts. Thus, this reconstruction system provides an experimental paradigm to study features of the assembly process and structure of Par-dependent cell-autonomous polarity.

cell biology↗

bioRxiv2018

Microstructural properties of the vertical occipital fasciculus explain the variability in human stereoacuity

Stereopsis is a fundamental visual function that has been studied extensively. However, it is not clear why depth discrimination (stereoacuity) varies more significantly among people than other modalities. Previous studies reported the involvement of both dorsal and ventral visual areas in stereopsis, implying that not only neural computations in cortical areas but also the anatomical properties of white matter tracts connecting those areas can impact stereopsis and stereoacuity. Here, we studied how human stereoacuity relates to white matter properties by combining psychophysics, diffusion MRI (dMRI), and quantitative MRI (qMRI). We performed a psychophysical experiment to measure stereoacuity, and in the same participants we analyzed the microstructural properties of visual white matter tracts based on two independent measurements, dMRI (fractional anisotropy, FA) and qMRI (macromolecular tissue volume; MTV). Microstructural properties along the right vertical occipital fasciculus (VOF), a major tract connecting dorsal and ventral visual areas, were highly correlated with measures of stereoacuity. This result was consistent for both FA and MTV, suggesting that the structural-behavioral relationship reflects differences in neural tissue density, rather than differences in the morphological configuration of fibers. fMRI confirmed that binocular disparity stimuli activated the dorsal and ventral visual regions near VOF endpoints. No other occipital tracts explained the variance in stereoacuity. In addition, the VOF properties were not associated with differences in performance on a different psychophysical task (contrast detection). These series of experiments suggest that stereoscopic depth discrimination performance is, at least in part, constrained by dorso-ventral communication through the VOF.

neuroscience↗

bioRxiv2017

Rapid responses of amygdala neurons discriminate facial expressions

The amygdala plays a critical role in detecting potential danger through sensory input [1, 2]. In the primate visual system, a subcortical pathway through the superior colliculus and the pulvinar is thought to provide the amygdala with rapid and coarse visual information about facial emotions [3-6]. A recent electrophysiological study in human patients supported this hypothesis by showing that intracranial event-related potentials discriminated fearful faces from other faces very quickly (within [~]74 ms) [7]. However, several aspects of the hypothesis remain debatable [8]. Critically, evidence for short-latency, emotion-selective responses from individual amygdala neurons is lacking [9-12], and even if this type of response existed, how it might contribute to stimulus detection is unclear. Here, we addressed these issues in the monkey amygdala and found that ensemble responses of single neurons carry robust information about emotional faces-- especially threatening ones--within [~]50 ms after stimulus onset. Similar rapid response was not found in the temporal cortex from which the amygdala receives cortical inputs [13], suggesting a subcortical origin. Additionally, we found that the rapid amygdala response contained excitatory and suppressive components. The early excitatory component might be useful for quickly sending signals to downstream areas. In contrast, the rapid suppressive component sharpened the rising phase of later, sustained excitatory input (presumably from the temporal cortex) and might therefore improve processing of emotional faces over time. We thus propose that these two amygdala responses that originate from the subcortical pathway play dual roles in threat detection.

neuroscience↗