Biology subjects

WU, S.

Publications and source records attributed to WU, S..

6 recordsLinked to original sources

bioRxiv2026

Oscillatory Phase Coding Emerges in Adaptive RNNS for Flexible Working Memory

Oscillatory phase coding (OPC) is widely observed in neural systems and has been implicated in cognitive functions such as memory and sequence processing. However, its computational role and neural substrates remain largely unclear. Here, we studied OPC by training recurrent neural networks (RNNs) with adaptation to perform sequence working memory tasks with variable delays. After training, we found that OPC emerges in the RNN spontaneously: the memorized items recur at distinct phases of a shared rhythm, with oscillation amplitude encoding item identity and phase encoding serial order. Ablation studies identified three key elements required for OPC: adaptation in neural dynamics, variable delay in memory retention, and flexibility in reading out information. Neuron-type analysis revealed the emergence of content and time cells in the trained RNN, and they process item identity and sequence order separately. Recurrent connectivity analysis revealed phase-organized connections among time cells and structured connections within digit-selective content-cell groups. Inspired by these findings, we proposed a neural circuit model which reproduces OPC through interactions between content units and an adaptive time ring. Together, this study reveals how OPC arises from network structures and task demands, offers insights into the implementation and computational mechanisms of OPC in biological systems, and inspires us to develop computational models for human-like cognitive functions.

neuroscience↗

bioRxiv2026

Theta sweeps accelerate cognitive map learning by broadcasting information to unvisited regions

To support flexible behavior, animals need to learn map like representations of environments from limited experiences, but the underlying neural mechanisms remain unclear. Here, we propose that theta sweeps, a prominent feature in the activities of grid and place cells during active movement, can broadcast acquired sensory information to swept unvisited regions, accelerating cognitive map learning. To test this idea, we built a network of direction, grid, and place cell modules, in which theta modulation and firing rate adaptation generate theta sweeps, while Hebbian plasticity enables place cells to bind environmental observations. Simulations and ablations showed that the dorsal ventral grid organization produces multiscale, independent sweeps broadcasting acquired sensory information to a broad, fan shaped unvisited area, enabling unvisited regions to build place environment associations. The model further suggests that declining theta power as the environment becomes familiar acts as an annealing process, shifting from learning general information about the environment to making local refinements. The model makes three testable predictions: strong theta modulation accelerates early learning, environments with broader spatial correlations favor longer theta sweeps, and theta sweeps progressively shorten as environments become familiar. Together, our results support theta sweeps as a mechanism for accelerating cognitive map learning from limited explorations, and it sheds light on developing brain inspired algorithms for building world models.

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↗

bioRxiv2025

The acute sleep-inducing effects of light require histamine neurotransmission in mice

Sleep regulation depends on the complex interplay between homeostatic and circadian processes synchronized by the light/dark cycle. Sleep is also directly regulated by light via the retinal inputs to the preoptic area (POA). Although the light-responsive POA neurons project to several wake-promoting structures, including histaminergic neurons in the tuberomammillary nucleus (TMn), there is no functional evidence for their involvement in light-induced sleep. To bridge this gap, we used histidine decarboxylase (HDC, the histamine-synthetizing enzyme) knockout mice (HDC-/-, n=7) and hM4Di-HDC-cre mice (HDC+/+, n=8) subjected to an ultradian light/dark protocol (LD 1h:1h over 24h), and another group of hM4Di-HDC-cre mice (n=8) exposed to a 1-h light pulse. We found that light pulses during the biological night enhanced slow wave sleep and increased cortical EEG power in the delta range (0.5-3Hz), and that these effects were significantly attenuated both in HDC-/- (83 vs 23 min/6h, p=0.005) under LD 1h:1h condition and in hM4Di-HDC-cre mice after acute chemogenetic silencing of histamine neurons by the DREADD ligand deschloroclozapine (15 vs 6 min/h, p=0.0016) under a 1-h light pulse. In addition, the sleep-inducing effect of light was circadian dependent, with the strongest effect at the beginning and end of the night but no effect at all during the biological day in HDC+/+mice. Our study provides functional evidence that the acute sleep-inducing effects of light on sleep require histamine neurotransmission in mice.

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↗

bioRxiv2021

Cerebral blood flow predicts multiple demand network activity and fluid intelligence across the lifespan

The preservation of cognitive function into old age is a public health priority. Cerebral hypoperfusion is a hallmark of dementia but its impact on maintaining cognitive ability across the lifespan is less clear. We investigated the relationship between baseline cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) response during a fluid reasoning task in a population-based adult lifespan cohort (N=227, age 18-88 years). As age differences in baseline CBF could lead to non-neuronal contributions to the BOLD signal, we introduced commonality analysis to neuroimaging, in order to dissociate performance-related CBF effects from the physiological confounding effects of CBF on the BOLD response. Accounting for CBF, we confirmed that performance- and age-related differences in BOLD responses in the multiple-demand network (MDN) implicated in fluid reasoning. Differences in baseline CBF across the lifespan explained not only performance-related BOLD responses, but also performance-independent BOLD responses. Our results suggest that baseline CBF is important for maintaining cognitive function, while its non-neuronal contributions to BOLD signals reflect an age-related confound. Maintaining perfusion into old age may serve to support brain function with behavioural advantage, regulating brain health.

neuroscience↗