Biology subjects

Wei, X.-X.

Publications and source records attributed to Wei, X.-X..

15 recordsLinked to original sources

bioRxiv2025

Split trial analysis reveals the information capacity of neural population codes

Understanding how correlated neural noise affects neural population coding is a basic question in computational and systems neuroscience [1, 2, 3, 4]. Recent theoretical work suggests that shared noise along the stimulus encoding direction is the primary factor that limits information encoding (i.e., information-limiting noise) [5, 6]. Despite this theoretical insight, it has been difficult to test it experimentally due to the challenges in inferring information-limiting noise from neural data. To overcome this challenge, we have developed a method (i.e., split-trial analysis) to partition the noise in a neural population into information-limiting noise vs. non-information-limiting components. Our method is simple to implement, yet it is highly effective given a limited amount of data. Results from extensive numerical simulations show that split-trial analysis substantially outperforms existing methods in accuracy, efficiency, and robustness. Applications of split-trial analysis to a number of neurophysiological datasets reveals insights into the precision of the neural codes for several systems. First, it reveals a substantial amount of information-limiting noise in the mouse head direction system. Second, it uncovers a small yet positive information-limiting noise in the orientation code in mouse V1. Third, we discover that the information-limiting noise in the macaque pre-frontal cortex is highly consistent over time during a simple saccade task. Split-trial analysis is a general technique that should be widely applicable in analyzing the properties of neural population codes.

neuroscience↗

bioRxiv2025

Drift-diffusion dynamics of the hippocampal replay

Replay in the hippocampus during sharp-wave ripples is thought to play major roles in learning and memory. However, existing analysis methods often lead to inconsistent and inaccurate metrics for characterizing replay dynamics. We develop a novel computational framework that model the replay dynamics using drift-diffusion process. Further, to capture the potentially rich population-level dynamics during sharp-wave ripples, our model allows switching between multiple well-motivated types of dynamics. Applications of our method to rat hippocampal recordings lead to insights into a number of important open questions, including: (i) whether the speed of most replay events is comparable to real-world running; (ii) whether replays follow a random walk; (iii) whether "preplay" events exist. Overall, our approach enables precise characterizations and unambiguous interpretations of population dynamics during sharp-wave ripples, which more broadly can provide a better understanding of the functions and underlying mechanisms of replay.

neuroscience↗

bioRxiv2025

Postprocessing-Enhanced Machine Learning for Reliable Real-Time Sleep Staging in Closed-Loop Neuromodulation

Real-time sleep stage classification is important for closed-loop neuromodulation at certain stages during sleep, yet current models often yield noisy and unstable outputs that risk false triggers. These fluctuations, especially near stage boundaries, can compromise the safety and reliability of stimulation. Existing methods frequently rely on model-specific architectures or require extensive tuning to maximize precision, which limits their generalizability and hinders deployment in real-time systems. To address this, we propose a lightweight, classifier-independent post-processing pipeline that stabilizes predictions without modifying the underlying classifier. Our method first applies temporal smoothing to predicted sleep-stage probabilities, followed by conservative control logic to enhance stimulation precision. We evaluate four smoothing techniques: Moving Average (MA), Exponential Smoothing (ES), Kalman Filtering (KF), and a novel Weighted Exponential Smoothing (WES), on a 29-subject open-source sleep dataset. To capture the trade-offs between stability and responsiveness, we introduce new real-time evaluation metrics. We identify an optimal smoothing intensity range and assess three control strategies: probability thresholding, naive waiting, and entropy constraint, as well as a hybrid method combining high confidence and low uncertainty. Smoothing improves generalization and robustness: under both low and high synthetic noise ({sigma} > 0.2), smoothed outputs retain 10-15% higher precision and recall across all stages. Control logic further enhances reliability: our hybrid method achieves 80% (Wake), 96% (N2), 98% (N3), and 86% (REM) precision. Finally, we introduce a stage-transition constraint matrix to suppress biologically implausible transitions (e.g., REM[->] N3), to further stabilize outputs during evidence accumulation, with potential applications in objective sleep quality assessment. To our knowledge, this is the first study to systematically characterize real-time trade-offs between smoothing, latency, and control logic in sleep staging. Overall, our generalizable framework is expected to improve safety, interpretability, and deployment feasibility of real-time sleep classification for both clinical and wearable closed-loop neuromodulation systems.

bioengineering↗

bioRxiv2025

Identifiability of Bayesian Models of Cognition

Inferring the underlying computational processes from behavioral measurements is a fundamental approach in cognitive science and neuroscience. Although Bayesian decision theory has become a major normative framework for modeling cognition, it is unclear to what extent its modeling components (i.e., prior belief, likelihood function, and the loss function) can be recovered from behavioral data. Here, we systematically investigated the problem of inferring such Bayesian models from behavioral tasks. In contrast to a pessimistic picture often painted in previous research, our analytical results guarantee in-principle identifiability under broadly applicable conditions, without any a priori knowledge of prior or encoding. Simulations and applications on the basis of behavioral datasets validate that the predictions of this theory apply in realistic settings. Importantly, our results demonstrate that reliable recovery of the model often requires having data from multiple noise levels. This is a crucial insight that will guide future experimental design.

neuroscience↗

bioRxiv2024

Luminance invariant encoding in primary visual cortex

The retina maintains sensitivity over a large range of luminance intensities by switching between rod and cone photoreceptors. This luminance adaptation has been shown to alter the receptive fields and interneuronal correlations of retinal ganglion cells (RGCs). While these adaptations allow the retina to encode visual information across environmental conditions, they present a challenge to downstream processing areas for which it is important that representations are invariant to light level. We measured the effects of scotopic versus photopic luminance adaptation on thalamic and cortical activity by tracking neuronal populations across light levels. While changes in the output of the retina are evident in the lateral geniculate nucleus (LGN), the representation in primary visual cortex (V1) is largely invariant to the changes in luminance. We show that an invariant V1 code can emerge through the integration of parallel functional pathways at the geniculocortical synapse.

neuroscience↗

bioRxiv2024

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Although grid cells are one of the most well studied functional classes of neurons in the mammalian brain, whether there is a single orientation and spacing value per grid module has not been carefully tested. We analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the encoding of local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that grid property variability of a similar magnitude to the analyzed data leads to significantly decreased decoding error. This holds even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

neuroscience↗

bioRxiv2023

Representational geometry explains puzzling error distributions in behavioral tasks

Measuring and interpreting errors in behavioral tasks is critical for understanding cognition. Conventional wisdom assumes that encoding/decoding errors for continuous variables in behavioral tasks should naturally have Gaussian distributions, so that deviations from normality in the empirical data indicate the presence of more complex sources of noise. This line of reasoning has been central for prior research on working memory. Here we re-assess this assumption, and find that even in ideal observer models with Gaussian encoding noise, the error distribution is generally non-Gaussian, contrary to the commonly held belief. Critically, we find that the shape of the error distribution is determined by the geometrical structure of the encoding manifold via a simple rule. In the case of a high-dimensional geometry, the error distributions naturally exhibit flat tails. Using this novel insight, we apply our theory to visual short-term memory tasks, and find that it can account for a large array of experimental data with only two free parameters. Our results call attention to the geometry of the representation as a critically important, yet underappreciated factor in determining the character of errors in human behavior.

neuroscience↗

bioRxiv2022

Neural dynamics and geometry for transitive inference

Relational cognition -- the ability to infer relationships that generalize to novel combinations of objects -- is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) adopted different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs expressing an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

neuroscience↗

bioRxiv2022

Unsupervised approach to decomposing neural tuning variability

Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture trial-to-trial tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex- a paradigmatic case for which the tuning curve approach has been scientific essential- we discovered a novel simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of new structure of the neural code, capturing the influence of the stimulus drive and internal states simultaneously.

neuroscience↗

bioRxiv2021

Population dynamics of the thalamic head direction system during drift and reorientation

The head direction (HD) system is classically modeled as a ring attractor network1,2 which ensures a stable representation of the animals head direction. This unidimensional description popularized the view of the HD system as the brains internal compass3,4. However, unlike a globally consistent magnetic compass, the orientation of the HD system is dynamic, depends on local cues and exhibits remapping across familiar environments5. Such a system requires mechanisms to remember and align to familiar landmarks, which may not be well described within the classic 1-dimensional framework. To search for these mechanisms, we performed large population recordings of mouse thalamic HD cells using calcium imaging, during controlled manipulations of a visual landmark in a familiar environment. First, we find that realignment of the system was associated with a continuous rotation of the HD network representation. The speed and angular distance of this rotation was predicted by a 2nd dimension to the ring attractor which we refer to as network gain, i.e. the instantaneous population firing rate. Moreover, the 360-degree azimuthal profile of network gain, during darkness, maintained a memory trace of a previously displayed visual landmark. In a 2nd experiment, brief presentations of a rotated landmark revealed an attraction of the network back to its initial orientation, suggesting a time-dependent mechanism underlying the formation of these network gain memory traces. Finally, in a 3rd experiment, continuous rotation of a visual landmark induced a similar rotation of the HD representation which persisted following removal of the landmark, demonstrating that HD network orientation is subject to experience-dependent recalibration. Together, these results provide new mechanistic insights into how the neural compass flexibly adapts to environmental cues to maintain a reliable representation of the head direction.

neuroscience↗

bioRxiv2020

A Variable Clock Underlies Internally Generated Hippocampal Sequences

Humans have the ability to store and retrieve memories with various degrees of specificity, and recent advances in reinforcement learning have identified benefits to learning when past experience is represented at different levels of temporal abstraction. How this flexibility might be implemented in the brain remains unclear. We analyzed the temporal organization of rat hippocampal population spiking to identify potential substrates for temporally flexible representations. We examined activity both during locomotion and during memory-associated population events known as sharp wave-ripples (SWRs). We found that spiking during SWRs is rhythmically organized with higher event-to-event variability than spiking during locomotion-associated population events. Decoding analyses using clusterless methods further indidate that similar spatial experience can be replayed in multiple SWRs, each time with a different rhythmic structure whose periodicity is sampled from a lognormal distribution. This variability increases with experience despite the decline in SWR rates that occurs as environments become more familiar. We hypothesize that the variability in temporal organization of hippocampal spiking provides a mechanism for storing experiences with various degrees of specificity.

neuroscience↗

bioRxiv2019

A zero-inflated gamma model for post-deconvolved calcium imaging traces

Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing \"pre-processing\" tools applied to calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, computational modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements. Surprisingly, these issues have to date received significantly less attention. To fill this gap, we examine the statistical properties of the deconvolved activity estimates, and propose several density models for these random signals. These models include a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass which serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model out-performs simpler models (e.g., Poisson or Bernoulli models) in the context of both simulated and real neural data, and can therefore play a useful role in bridging calcium imaging analysis methods with tools for analyzing activity in large neural populations.

neuroscience↗

bioRxiv2019

Understanding multivariate brain activity: evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

Previous studies have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We first replicate the classical finding that TCNCs impair population codes in a standard neuronal population. We then extend our analysis to fMRI data, and show that voxelwise TCNCs do not impair and can even improve MVPA performance when TCNCs are strong or the number of voxels is large. We also confirm these results using standard information-theoretic analyses in computational neuroscience. Further computational analyses demonstrate that the discrepancy between the effect of TCNCs in neuronal and voxel populations can be explained by tuning heterogeneity and pool sizes. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

neuroscience↗

bioRxiv2018

Dynamic self-organized error-correction of grid cells by border cells

Grid cells in the entorhinal cortex are believed to establish their regular, spatially correlated firing patterns by path integration of the animals motion. Mechanisms for path integration, e.g. in attractor network models, predict stochastic drift of grid responses, which is not observed experimentally. We demonstrate a biologically plausible mechanism of dynamic self-organization by which border cells, which fire at environmental boundaries, can correct such drift in grid cells. In our model, experience-dependent Hebbian plasticity during exploration allows border cells to learn connectivity to grid cells. Border cells in this learned network reset the phase of drifting grids. This error-correction mechanism is robust to environmental shape and complexity, including enclosures with interior barriers, and makes distinctive predictions for environmental deformation experiments. Our work demonstrates how diverse cell types in the entorhinal cortex could interact dynamically and adaptively to achieve robust path integration.

neuroscience↗

bioRxiv2016

A new law of human perception

Perception is a subjective experience that depends on the expectations and beliefs of an observer1. Psychophysical measures provide an objective yet indirect characterization of this experience by describing the dependency between the physical properties of a stimulus and the corresponding perceptually guided behavior2. Two fundamental psychophysical measures characterize an observers perception of a stimulus: how well the observer can discriminate the stimulus from similar ones (discrimination threshold) and how strongly the observers perceived stimulus value deviates from the true stimulus value (perceptual bias). It has long been thought that these two perceptual characteristics are independent3. Here we demonstrate that discrimination threshold and perceptual bias show a surprisingly simple mathematical relation. The relation, which we derived from assumptions of optimal sensory encoding and decoding4, is well supported by a wide range of reported psychophysical data5-16 including perceptual changes induced by spatial17,18 and temporal19-23 context, and attention24. The large empirical support suggests that the proposed relation represents a new law of human perception. Our results imply that universal rules govern the computational processes underlying human perception.

animal behavior and cognition↗