Biology subjects

Hachisuka, A.

Publications and source records attributed to Hachisuka, A..

3 recordsLinked to original sources

bioRxiv2026

Probing the neural geometry of conscious object perception in time

Conscious object recognition is a critical brain function, key to making sense of the plethora of visual inputs and guiding adaptive behavior. Yet, the sequence of neural processing underlying conscious object recognition remains unclear. Existing proposals alternatively suggest that conscious object recognition proceeds in a "coarse-to-fine" sequence or a "fine-to-coarse" sequence. Here, we recorded high-density EEG while participants of both sexes performed a threshold-level object recognition task under different viewing conditions. Both superordinate and basic-level category information emerged early in neural activity, followed by a late superordinate category representation in an abstract format that is disentangled from low-level object features. Strikingly, in the early period, while basic-level category information positively correlated with perceptual awareness, superordinate category information negatively predicted perceptual awareness. By contrast, both superordinate and basic-level category information positively correlated with objective categorization accuracy in the early period. In the late period, both levels of category information positively correlated with awareness. These results suggest that neural processing underlying conscious object perception proceeds from the basic to the superordinate category level.

neuroscience↗

bioRxiv2025

Neural and Computational Mechanisms Underlying One-shot Perceptual Learning in Humans

The ability to quickly learn and generalize is one of the brains most impressive feats and recreating it remains a major challenge for modern artificial intelligence research. One of the most mysterious one-shot learning abilities displayed by humans is one-shot perceptual learning, whereby a single viewing experience drastically alters visual perception in a long-lasting manner. Where in the brain one-shot perceptual learning occurs and what mechanisms support it remain enigmatic. Combining psychophysics, 7T fMRI, and intracranial recordings, we identify high-level visual cortex as the most likely neural substrate wherein neural plasticity supports one-shot perceptual learning. We further develop a novel deep neural network model incorporating top-down feedback into a vision transformer, which recapitulates and predicts human behavior. The prior knowledge learnt by this model is highly similar to the neural code in the human high-level visual cortex. These results reveal the neurocomputational mechanisms underlying one-shot perceptual learning in humans.

neuroscience↗

bioRxiv2021

Moving bar of light evokes vectorial spatial selectivity in the immobile rat hippocampus

Visual cortical neurons encode the position and motion direction of specific stimuli retrospectively, without any locomotion or task demand1. Hippocampus, a part of visual system, is hypothesized to require self-motion or cognitive task to generate allocentric spatial selectivity that is scalar, abstract2,3, and prospective4-7. To bridge these seeming disparities, we measured rodent hippocampal selectivity to a moving bar of light in a body-fixed rat. About 70% of dorsal CA1 neurons showed stable activity modulation as a function of the bars angular position, independent of behavior and rewards. A third of tuned cells also encoded the direction of revolution. In other experiments, neurons encoded the distance of bar, with preference for approaching motion. Collectively, these demonstrate visually evoked vectorial selectivity (VEVS). Unlike place cells, VEVS was retrospective. Changes in the visual stimulus or its trajectory did not cause remapping but only caused gradual changes. Most VEVS tuned neurons behaved like place cells during spatial exploration and the two selectivity were correlated. Thus, VEVS could form the basic building block of hippocampal activity. When combined with self-motion, reward, or multisensory stimuli8, it can generate the complexity of prospective representations including allocentric space9, time10,11, and episodes12.

neuroscience↗