Jia Liu B

This paper investigates working memory as a form of recurrent fast-weight computation, showing that a model based on linear Transformer computation can replicate neural dynamics observed in macaque prefrontal cortex during a visuospatial task.

The paper investigates how low-dimensional frontal feedback resolves visual ambiguity in the human brain using fMRI, EEG, and computational models.

The paper shows that stimulus-driven changes in neuronal connectivity and thermodynamic states lead to more efficient coding in mouse V1 by reorganizing neural activity onto low-dimensional manifolds.

The paper investigates how the entorhinal and hippocampal circuits coordinate during navigation, finding 3-fold hippocampal periodicity phase-locked with entorhinal 6-fold periodicity, and shows this is modeled by a PhaseSync model.

The paper investigates how the brain categorizes non-linearly separable stimuli by analyzing neural manifolds in macaques during a motion-induced illusory contour classification task, revealing a transformation from sensory to perceptual manifolds through geometric operations.

This paper introduces a computational model to study the emergence of cognitive modules, finding that a face-processing module arises due to structural constraints rather than task demands.

The paper examines how trait motivation, specifically the behavioral inhibition system, relates to the structure and function of the face processing system in Chinese youth.

The paper investigates how neurons encode interdependent features of head direction and angular head velocity in mice, revealing distinct encoding strategies and their functional implications.

The paper analyzes single-neuron network topology in mouse V1 and finds it exhibits extreme small-world, modular, and rich-club properties compared to macroscopic brain networks.

This study uses machine learning to analyze neural signatures of feature and spatial attention, finding shared and distinct brain networks involved in each process.

The paper investigates gamma-frequency oscillations in mouse anterodorsal thalamus HD cells, revealing two mechanisms governing gamma coordination.

This study investigates how structured interconnectivity in neural networks balances selectivity and generality in object recognition using neurophysiological data and computational modeling.

The paper shows that neural geometries in the dorsal prefrontal cortex transform from an asymmetric sensory manifold to a symmetric intermediate state and then to an asymmetric mnemonic manifold during a visuospatial working memory task.

This study uses fMRI to show that the hippocampus encodes relational memory with a functional gradient, where posterior regions handle detailed memory formation and anterior regions handle abstract knowledge for decision making.

This study explores how human body size influences perception of environmental affordances, finding that body size defines a boundary for action possibilities and that this boundary is not exclusive to embodied organisms.

The paper investigates how humans infer object stability using a stochastic world model of gravity, finding that gravity's direction is represented as a Gaussian distribution, which explains perceptual illusions and supports efficient stability inference.

This study investigates how criticality in cortical subsystems changes during cognitive tasks using computational models and fMRI data, finding heterogeneous modulation in specific brain regions.

The paper shows that hippocampal replay and entorhinal grid cell activity share a hexagonal directional geometry during human navigation.

This study shows that real-world object size is an axis in object space, supported by both computational models and human fMRI experiments.

The paper constructs a hybrid SOM model to explain the topological organization of object selectivities in the ventral temporal cortex using a wiring cost minimization principle.

This paper identifies a whole-brain functional connectivity network linked to non-verbal number acuity and arithmetic skills using connectome-based predictive modeling on resting-state fMRI data.

This study shows that a deep convolutional neural network deprived of face experience still develops face-selective modules, suggesting domain-specificity can emerge without genetic predisposition.

This study reveals how imbalanced face datasets cause AI bias in DCNNs by showing that more frequent faces are better represented, leading to the other-race effect.

This study shows that deep convolutional neural networks develop semantic relatedness among objects during training, similar to human WordNet structures, without explicit top-down guidance.

This study compares internal representations of human observers and a deep convolutional neural network in gender classification tasks, finding similar representations at low spatial frequencies.

This study shows that deep convolutional neural networks use a hierarchical sparse coding scheme similar to the primate brain, with increasing sparseness and correlation with object recognition performance.

DNNBrain provides a Python toolbox for exploring internal representations in deep neural networks and the brain, enabling cross-disciplinary research between deep learning and cognitive neuroscience.

The paper identifies genes correlated with the sentence-processing network in the human cortex using gene expression, fMRI, and neuroimaging meta-analysis.

This study examines the functional interaction between hippocampus and caudate in human navigation using fMRI and behavioral tasks, finding a positive correlation with navigation ability.