Biology subjects

HONG, B.

Publications and source records attributed to HONG, B..

7 recordsLinked to original sources

bioRxiv2026

LAND: Latent Aligned Neural-Behavioral Dynamics via Flow Matching forGeneralizable Movement Decoding

Generalizable movement decoding remains a central challenge for invasive brain--computer interfaces (BCIs), as decoders trained under limited calibration conditions often fail to generalize to unseen movement speeds, limbs, and subjects. Existing decoding methods are typically trained on paired data collected under restricted conditions. How to incorporate behavioral structure from unpaired data for robust out-of-distribution (OOD) decoding therefore remains unresolved. To address this, we propose LAND (Latent Aligned Neural-behavioral Dynamics), a framework that aligns latent neural and behavioral dynamics through flow matching. By learning a neural-to-behavioral transport map and using behavioral-dynamics priors from unpaired data to encourage structured neural manifolds, LAND regularizes representation geometry to promote cross-domain generalization. We evaluate LAND on synthetic neural data, epidural BCI recordings from a tetraplegia participant, and multi-electrode array (MEA) recordings from nonhuman primates (NHPs). Across these settings, LAND improves zero-shot generalization to OOD movement speeds and yields speed-modulated manifolds. With limited target-domain fine-tuning, it further improves transfer across limbs and subjects. These results support flow-based neural--behavioral alignment with unpaired kinematic priors as an approach for learning transferable neural representations and robust movement decoding across behavioral and recording domains.

neuroscience↗

bioRxiv2025

Localized Space Coding and Phase Coding Complement Each Other to Achieve Robust and Efficient Spatial Representation

Localized space coding and phase coding are two distinct strategies responsible, respectively, for representing abstract structure and sensory observations in neural cognitive maps. In spatial representation, localized space coding is implemented by place cells in the hippocampus (HPC), while phase coding is implemented by grid cells in the medial entorhinal cortex (MEC). Both strategies have their own advantages and disadvantages, and neither of them meets the requirement of representing space robustly and efficiently in the brain. Here, we show that through reciprocal connections between HPC and MEC, place and grid cells can complement each other to overcome their respective shortcomings. Specifically, we build a coupled network model, in which a continuous attractor neural network (CANN) with position coordinate models place cells, while multiple CANNs with phase coordinates model grid cell modules with varying spacings. The reciprocal connections between place and grid cells encode the correlation prior between the sensory cues processed by HPC and MEC, respectively. Using this model, we show that: 1) place and grid cells interact to integrate sensory cues in a Bayesian manner; 2) place cells complement grid cells in coding accuracy by eliminating non-local errors of the latter; 3) grid cells complement place cells in coding efficiency by enlarging the number of environmental maps stored stably by the latter. We demonstrate that the coupled network model explains the seemingly contradictory experimental findings about the remapping phenomena of place cells when grid cells are either inactivated or depolarized. This study gives us insight into understanding how the brain employs collaborative localized and phase coding to realize both robust and efficient information representation.

neuroscience↗

bioRxiv2024

Spatiotemporal Decomposition of Whole-Brain Alpha Traveling Waves

Spontaneously emerging traveling waves are present within the spatiotemporal patterns of alpha-band EEG oscillations, but current analysis methods are limited in parsing the diversity of global wave structures and their correlation with brain functions. To address this limitation, we constructed a rigorous mathematical framework, Weakly Orthogonal Conjugate Contrast Analysis (WOCCA), which decomposes the whole-brain EEG alpha oscillations into directionally independent traveling waves. For the first time, we systematically characterized propagating components in alpha-band resting-state EEG as a combination of rotational, longitudinal, and horizontal traveling wave patterns. The intensity, directionality, and morphological characteristics of these wave patterns account for the differences between cognitive states during rest and consciousness levels under sedation. Moreover, our WOCCA decomposition encompassed the state transition dynamics captured by EEG Microstate Analysis, a conventional analysis framework for alpha waves. These results not only established a novel approach for identifying and analyzing traveling waves but also provided evidence for the relationship between wave directionality and cooperative interactions in brain network.

neuroscience↗

bioRxiv2024

Memorize multiple phase pattern attractors in nonlinear coupled oscillators dynamic via heterogeneous connectivity strength

Whole brain neural oscillation activities exhibit multiple wave patterns and seem to be supported by the common circuit network structure. We proposed a Hebbian-like Kuramoto model based entirely on heterogeneous connectivity strength rather than phase delay, which encodes the multiple phase patterns as attractors. We systematically investigated how the model dynamic landscape influenced by attractors and their corresponding eigenvalues, as well as how to control the stability of equilibrium points and the occurrence of high dimension bifurcations. This framework enables us to reproduce the dominant wave activity components in human brain functional MRI signal, and provides a canonical model for the multi body physical system spatio-temporal pattern attractor dynamics.

neuroscience↗

bioRxiv2023

Coordinated transformation of object representations across human visual cortex

Our brain constructs increasingly sophisticated representations along the ventral visual pathway to support object recognition. To understand how these representations unfold over time, we recorded human intracranial electroencephalography responses to 120 object images at five consecutive stages of the ventral pathway from V1 to occipitotemporal areas. Using representational similarity analysis, we confirmed that response patterns were more strongly driven by low-order stimulus properties at early stages and high-order category information at late stages, respectively. Interestingly, response patterns also became less stimulus-driven and more categorical at all stages over time, from [~]100 to 200 ms post-stimulus. During this period, we found significant noise correlation between single-trial response patterns across stages, indicating tight inter-areal coupling. Thus, multi-areal recurrent processes may be essential in building high-order object representations.

neuroscience↗

bioRxiv2023

Leading and Following: Noise Differently Affects Semantic and Acoustic Processing during Naturalistic Speech Comprehension

Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and were correlated with comprehension performance and perceived speech intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments. HighlightsO_LILeading responses were observed in the semantic-level neural tracking, with more leading latencies as noise increased. C_LIO_LIFollowing responses were observed in the acoustic-level neural tracking, with more delayed latencies as noise increased. C_LIO_LISemantic-level neural tracking is correlated with comprehension performance and perceived intelligibility. C_LIO_LIDistinct frequency bands were involved in speech semantic and acoustic processing. C_LI

neuroscience↗

bioRxiv2021

A distinctive neural nexus in blind individuals supports Braille reading

Natural Braille reading, a demanding cognitive skill, poses a huge challenge for the brain network of the blind. Here, with behavioral measurement and functional MRI imaging data, we pinpointed the neural pathway and investigated the neural mechanisms of individual differences in Braille reading in late blindness. Using resting state fMRI, we identified a distinct neural link between the higher-tier visual cortex--the lateral occipital cortex (LOC), and the inferior frontal cortex (IFC) in the late blind brain, which is significantly stronger than sighted controls. Individual Braille reading proficiency positively correlated with the left-lateralized LOC-IFC functional connectivity. In a natural Braille reading task, we found an enhanced bidirectional information flow with a stronger top-down modulation of the IFC-to-LOC effective connectivity. Greater top-down modulation contributed to higher Braille reading proficiency via a broader area of task-engaged LOC. Together, we established a model to predict Braille reading proficiency, considering both functional and effective connectivity of the LOC-IFC pathway. This two-tale model suggests that developing the underpinning neural circuit and the top-down cognitive strategy contributes uniquely to superior Braille reading performance. SIGNIFICANCE STATEMENTFor late blind humans, one of the most challenging cognitive skills is natural Braille reading. However, little is known about the neural mechanisms of significant differences in individual Braille reading performance. Using functional imaging data, we identified a distinct neural link between the left lateral occipital cortex (LOC) and the left inferior frontal cortex (IFC) for natural Braille reading in the late blind brain. To better predict individual Braille reading proficiency, we proposed a linear model with two variables of the LOC-IFC link: the resting-state functional connectivity and the task-engaged top-down effective connectivity. These findings suggest that developing the underpinning neural pathway and the top-down cognitive strategy contributes uniquely to superior Braille reading performance.

neuroscience↗