publications

2024

1. arXiv

   Nonparametric Covariance Regression for Massive Neural Data on Restricted Covariates via Graph

   Ganchao Wei

   2024

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2. arXiv

   Stream-level flow matching from a Bayesian decision theoretic perspective

   Ganchao Wei, and Li Ma

   2024

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3. ICLR

   Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures

   Ganchao Wei

   In The Twelfth International Conference on Learning Representations, 2024

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4. J. Neurosci.

   Calibrating Bayesian Decoders of Neural Spiking Activity

   Ganchao Wei, Zeinab Tajik Mansouri, Xiaojing Wang, and 1 more author

   The Journal of Neuroscience, May 2024

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   Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain–machine interfaces that more accurately reflect confidence levels when identifying external variables.

2023

1. arXiv

   A Flexible Bayesian Clustering of Dynamic Subpopulations in Neural Spiking Activity

   Ganchao Wei, Ian H. Stevenson, and Xiaojing Wang

   May 2023

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2. Neural Comput.

   Dynamic Modeling of Spike Count Data With Conway-Maxwell Poisson Variability

   Ganchao Wei, and Ian H. Stevenson

   Neural Computation, Jun 2023

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   In many areas of the brain, neural spiking activity covaries with features of the external world, such as sensory stimuli or an animal’s movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To flexibly track time-varying neural response properties, we developed a dynamic model with Conway-Maxwell Poisson (CMP) observations. The CMP distribution can flexibly describe firing patterns that are both under- and overdispersed relative to the Poisson distribution. Here we track parameters of the CMP distribution as they vary over time. Using simulations, we show that a normal approximation can accurately track dynamics in state vectors for both the centering and shape parameters (λ and ν). We then fit our model to neural data from neurons in primary visual cortex, “place cells” in the hippocampus, and a speed-tuned neuron in the anterior pretectal nucleus. We find that this method outperforms previous dynamic models based on the Poisson distribution. The dynamic CMP model provides a flexible framework for tracking time-varying non-Poisson count data and may also have applications beyond neuroscience.

2022

1. J. Neurosci.

   Predictable fluctuations in excitatory synaptic strength due to natural variation in presynaptic firing rate

   Naixin Ren, Ganchao Wei, Abed Ghanbari, and 1 more author

   Journal of Neuroscience, Jun 2022

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   Many controlled, in vitro studies have demonstrated how postsynaptic responses to presynaptic spikes are not constant but depend on short-term synaptic plasticity (STP) and the detailed timing of presynaptic spikes. However, the effects of short-term plasticity (depression and facilitation) are not limited to short, sub-second timescales. The effects of STP appear on long timescales as changes in presynaptic firing rates lead to changes in steady-state synaptic transmission. Here we examine the relationship between natural variations in the presynaptic firing rates and spike transmission in vivo. Using large-scale spike recordings in awake mice from the Allen Institute Neuropixels dataset, we first detect putative excitatory synaptic connections based on cross-correlations between the spike trains of millions of pairs of neurons. For the subset of pairs where a transient, excitatory effect was detected, we use a model-based approach to track fluctuations in synaptic efficacy and find that efficacy varies substantially on slow (\sim1 minute) timescales over the course of these recordings. For many connections, the efficacy fluctuations are correlated with fluctuations in the presynaptic firing rate. To understand the potential mechanisms underlying this relationship, we then model the detailed probability of postsynaptic spiking on a millisecond timescale, including both slow changes in postsynaptic excitability and monosynaptic inputs with short-term plasticity. The detailed model reproduces the slow efficacy fluctuations observed with many putative excitatory connections, suggesting that these fluctuations can be both directly predicted based on the time-varying presynaptic firing rate and, at least partly, explained by the cumulative effects of STP.Competing Interest StatementThe authors have declared no competing interest.

2. NeurIPS

   Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers

   Ganchao Wei, Ian H Stevenson, and Xiaojing Wang

   In Advances in Neural Information Processing Systems, Jun 2022

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2021

1. Neural Comput.

   Tracking Fast and Slow Changes in Synaptic Weights From Simultaneously Observed Pre- and Postsynaptic Spiking

   Ganchao Wei, and Ian H. Stevenson

   Neural computation, Sep 2021

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   Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a combination of both short- and long-term plasticity. Here we develop an extension of the common generalized linear model to infer both short- and long-term changes in the coupling between a pre- and postsynaptic neuron based on observed spiking activity. We model short-term synaptic plasticity using additive effects that depend on the presynaptic spike timing, and we model long-term changes in both synaptic weight and baseline firing rate using point process adaptive smoothing. Using simulations, we first show that this model can accurately recover time-varying synaptic weights (1) for both depressing and facilitating synapses, (2) with a variety of long-term changes (including realistic changes, such as due to STDP), (3) with a range of pre and postsynaptic firing rates, and (4) for both excitatory and inhibitory synapses. We then apply our model to two experimentally recorded putative synaptic connections. We find that simultaneously tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing is essential. Omitting any one of these factors can lead to spurious inferences for the others. Altogether, this model provides a flexible framework for tracking short- and long-term variation in spike transmission.