Biology subjects

Zhang, W.-H.

Publications and source records attributed to Zhang, W.-H..

10 recordsLinked to original sources

bioRxiv2026

Scale-mixture Langevin sampling in the subspaces of recurrent cortical circuit dynamics

Recurrent cortical circuits are hypothesized to perform Bayesian posterior sampling through their stochastic dynamics. Yet identifying which sampling algorithm a circuit intrinsically implements, as opposed to mapping manually designing algorithms onto circuits, requires rigorous theoretical analysis of the biological circuit dynamics, and remains a fundamental open challenge because of the analytical intractability of nonlinear recurrent dynamics. Here, we tackle this challenge through comprehensive theoretical analysis of analytically tractable nonlinear recurrent circuit dynamics used in neuroscience research-- the continuous attractor network (CAN). Surprisingly, we find that the circuit, without any non-Gaussian components in its connectivity or internal variability, intrinsically emerges heavy-tailed subspaces dynamics described as scale-mixture Langevin posterior sampling. The mechanism emerges from the circuits own nonlinear dynamics: divisive normalization renders the overall population activation Gamma-distributed which multiplicatively modulates the time constant of the Langevin sampling in the stimulus feature subspace. Eventually, this scale-mixture structure produces a heavy-tailed (Student-t) sampling step size distribution, significantly accelerating the sampling speed. Moreover, the scale-mixture Langevin sampling approaches a Levy-like regime in the weak input limit. Our results analytically identify an intrinsic circuit algorithm embedded in subspaces of nonlinear recurrent dynamics, and establish a minimal circuit mechanism for endogenous heavy-tail statistics without any explicit non-Gaussian components.

neuroscience↗

bioRxiv2023

A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation

Time perception is fundamental in our daily life. An important feature of time perception is temporal scaling (TS): the ability to generate temporal sequences (e.g., movements) with different speeds. However, it is largely unknown about the mathematical principle underlying TS in the brain. The present theoretical study investigates temporal scaling from the Lie group point of view. We propose a canonical nonlinear recurrent circuit dynamics, modeled as a continuous attractor network, whose neuronal population responses embed a temporal sequence that is TS equivariant. We find the TS group operators can be explicitly represented by a time-invariant control input to the network, whereby the input gain determines the TS factor (group parameter), and the spatial offset between the control input and the network state on the continuous attractor manifold gives rise to the generator of the Lie group. The recurrent circuits neuronal responses are consistent with experimental data. The recurrent circuit can drive a feedforward circuit to generate complex sequences with different temporal scales, even in the case of negative temporal scaling ("time reversal"). Our work for the first time analytically links the abstract temporal scaling group and concrete neural circuit dynamics.

neuroscience↗

bioRxiv2023

Network pharmacological analysis for the identification of the molecular mechanism of Lilium brownii (Baihe) against gastric cancer: 3-Demethylcolchicine targeting androgen receptor

Lilium brownii (Baihe) contains several bioactive compounds with anti-cancer properties. This study aimed to predict the anticancer targets and related pathways of Baihe for the treatment of gastric cancer (GC) by using network pharmacology and to further explore its potential mechanism in GC. The active compounds and their target proteins were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The OMIM, CTD, and GeneCards databases provided information on GC-related targets. After the overlap, the targets of Baihe against GC were collected. The STRING network platform and Cytoscape software were used for protein-protein interaction (PPI) network and core target investigations. Network pharmacology predicted that the principal targets were retrieved from the Starbase database in connection with the GC overall survival. Molecular docking was also used to validate Baihe and the targets high affinity. Finally, the DAVID online tool was used for the overlapping target Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The TCMSP database showed that Baihe has seven bioactive components. Apoptosis and p53 signaling pathways were primarily enriched in overlapping genes according to KEGG analysis. Androgen receptor (AR) was identified as a major target by combining the PPI network, KEEG enrichment, and target gene prognostic analysis. Molecular docking results verified that the Baihes 3-demethylcolchicine has a high affinity for the GC target AR. Based on the results of network pharmacology analysis based on data mining and molecular docking methods, the multi-target drug Baihe may be a promising therapeutic candidate for GC, but further in vivo/ex vivo research is required. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/536129v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@125dc69org.highwire.dtl.DTLVardef@a99703org.highwire.dtl.DTLVardef@1f97161org.highwire.dtl.DTLVardef@182bd38_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

Representational geometries reveal differential effects of response correlations on population codes in neurophysiology and functional magnetic resonance imaging

Two sensory neurons usually display trial-by-trial response correlations given the repeated representations of an identical stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime, multivariate pattern analysis (MVPA) has been the leading analysis approach in functional magnetic resonance imaging (fMRI), but the effects of response correlations in voxel populations remain underexplored. Here, instead of conventional MVPA analysis, we calculate linear Fisher information of population responses in human visual cortex and hypothetically remove response correlations between voxels. We found that voxelwise response correlations generally enhance stimulus information, a result standing in stark contrast to the detrimental effects of response correlations reported in neurophysiological literature. By voxel-encoding modeling, we further show that these two seemingly opposite effects actually can coexist. Furthermore, we use principal component analysis to decompose stimulus information in population responses onto different principal dimensions in a high representational space. Interestingly, response correlations simultaneously reduce and enhance information on high- and low-variance principal dimensions, respectively. The relative strength of the two antagonistic effects within the same computational framework produces the apparent discrepancy in the effect of response correlations in neuronal and voxel populations. Our results suggest that multivariate fMRI data contain rich statistical structures that are directly related to sensory information representation, and the general computational framework to analyze neuronal and voxel population responses can be applied in many types of neural measurements.

neuroscience↗

bioRxiv2022

Firing rate adaptation in continuous attractor neural networks accounts for theta phase shift of hippocampal place cells

Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between "bimodal cells" showing interleaved phase precession and procession, and "unimodal cells" in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.

neuroscience↗

bioRxiv2021

Degrading intestinal DAF-2 nearly doubles Caenorhabditis elegans lifespan without affecting development or reproduction

Twenty-eight years following the breakthrough discovery that a single-gene mutation of daf-2 can double the lifespan of Caenorhabditis elegans, it remains unclear where this gene, which encodes an insulin/IGF-1 receptor, is expressed and where it acts to regulate aging. Here, by inserting DNA sequences of fluorescent tags into the genomic locus of daf-2 and that of its downstream transcription factor daf-16, we determined that both genes are expressed in most or all tissues from embryos through adulthood, in line with their diverse functions. Using tissue-specific auxin-induced protein degradation, we determined that both DAF-2 and DAF-16 act in the intestine to regulate organismal aging. Strikingly, loss of DAF-2 in the intestine nearly doubled C. elegans lifespan but did not produce the adverse developmental or reproductive phenotypes associated with genetic daf-2 mutants. These findings unify the mechanism of lifespan regulation by genes and that by dietary restriction, and begin to focus anti-aging research on nutrient supply. HighlightsO_LIdaf-2 and daf-16 are expressed in most or all cells of C. elegans using genome editing. C_LIO_LIDAF-2 and DAF-16 both regulate lifespan from the intestine as determined using auxin-induced protein degradation. C_LIO_LIReduced insulin signaling in the intestine nearly doubles C. elegans lifespan without adverse effects on development or reproduction. C_LIO_LILifespan regulation by genes and dietary restriction are unified by intestinal supply of nutrients and metabolism. C_LI

cell biology↗

bioRxiv2020

A spiking neural circuit model for learning multi-sensory integration

Neurons in visual and vestibular information integration areas of macaque brain such as medial superior temporal (MSTd) and ventral intraparietal (VIP) have been classified into congruent neurons and opposite neurons, which prefer congruent inputs and opposite inputs from the two sensory modalities, respectively. In this work, we propose a mechanistic spiking neural model that can account for the emergence of congruent and opposite neurons and their interactions in a neural circuit for multi-sensory integration. The spiking neural circuit model is adopted from an established model for the circuits of the primary visual cortex with little changes in parameters. The network can learn, based on the basic Hebbian learning principle, the correct topological organization and behaviors of the congruent and opposite neurons that have been proposed to play a role in multi-sensory integration. This work explore the constraints and the conditions that lead to the development of a proposed neural circuit for cue integration. It also demonstrates that such neural circuit might indeed be a canonical circuit shared by computations in many cortical areas.

neuroscience↗

bioRxiv2019

Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

Multisensory integration areas such as dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas in macaques combine visual and vestibular cues to produce better estimates of self-motion. Congruent and opposite neurons, two types of neurons found in these areas, prefer congruent inputs and opposite inputs from the two modalities, respectively. A recently proposed computational model of congruent and opposite neurons reproduces their tuning properties and shows that congruent neurons optimally integrate information while opposite neurons compute disparity information. However, the connections in the network are fixed rather than learned, and in fact the connections of opposite neurons, as we will show, cannot arise from Hebbian learning rules. We therefore propose a new model of multisensory integration in which congruent neurons and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, and show that these neurons exhibit experimentally observed tuning properties.

neuroscience↗

bioRxiv2019

Emergence of opposite neurons in a firing-rate model of multisensory integration

Opposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.

neuroscience↗

bioRxiv2018

Concurrent Multisensory Integration and Segregation with Complementary Congruent and Opposite Neurons

Our brain perceives the world by exploiting multiple sensory modalities to extract information about various aspects of external stimuli. If these sensory cues are from the same stimulus of interest, they should be integrated to improve perception; otherwise, they should be segregated to distinguish different stimuli. In reality, however, the brain faces the challenge of recognizing stimuli without knowing in advance whether sensory cues come from the same or different stimuli. To address this challenge and to recognize stimuli rapidly, we argue that the brain should carry out multisensory integration and segregation concurrently with complementary neuron groups. Studying an example of inferring heading-direction via visual and vestibular cues, we develop a concurrent multisensory processing neural model which consists of two reciprocally connected modules, the dorsal medial superior temporal area (MSTd) and the ventral intraparietal area (VIP), and that at each module, there exists two distinguishing groups of neurons, congruent and opposite neurons. Specifically, congruent neurons implement cue integration, while opposite neurons compute the cue disparity, both optimally as described by Bayesian inference. The two groups of neurons provide complementary information which enables the neural system to assess the validity of cue integration and, if necessary, to recover the lost information associated with individual cues without re-gathering new inputs. Through this process, the brain achieves rapid stimulus perception if the cues come from the same stimulus of interest, and differentiates and recognizes stimuli based on individual cues with little time delay if the cues come from different stimuli of interest. Our study unveils the indispensable role of opposite neurons in multisensory processing and sheds light on our understanding of how the brain achieves multisensory processing efficiently and rapidly.\n\nSignificance StatementOur brain perceives the world by exploiting multiple sensory cues. These cues need to be integrated to improve perception if they come from the same stimulus and otherwise be segregated. To address the challenge of recognizing whether sensory cues come from the same or different stimuli that are unknown in advance, we propose that the brain should carry out multisensory integration and segregation concurrently with two different neuron groups. Specifically, congruent neurons implement cue integration, while opposite neurons compute the cue disparity, and the interplay between them achieves rapid stimulus recognition without information loss. We apply our model to the example of inferring heading-direction based on visual and vestibular cues and reproduce the experimental data successfully.

neuroscience↗