The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective. Here, we derive importance weighted extensions to AVB and AAE. These latent variable models use implicitly defined inference networks whose approximate posterior density q_ϕ(z|x) cannot be directly evaluated, an essential ingredient for importance weighting. We show improved training and inference in latent variable models with our adversarially trained importance weighting method, and derive new theoretical connections between adversarial generative model training criteria and marginal likelihood based methods. We apply these methods to the important problem of inferring spiking neural activity from calcium imaging data, a challenging posterior inference problem in neuroscience, and show that posterior samples from the adversarial methods outperform factorized posteriors used in VAEs.
We present efficient Bayesian methods for extracting neuronal spiking information from calcium imaging data. The goal of our methods is to sample from the posterior distribution of spike trains and model parameters (baseline concentration, spike amplitude etc) given noisy calcium imaging data. We present discrete time algorithms where we sample the existence of a spike at each time bin using Gibbs methods, as well as continuous time algorithms where we sample over the number of spikes and their locations at an arbitrary resolution using Metropolis-Hastings methods for point processes. We provi…
Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intra-cellular calcium signals. An on-going challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time-series. In this manuscript, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimenta…
Centre for Cognitive Science, Technical University Darmstadt（达姆施塔特工业大学认知科学中心）；DeepMind；research center caesar, an associate of the Max Planck Society（凯撒研究中心，马克斯·普朗克学会合作机构）；IMPRS Brain and Behavior Bonn/Florida（波恩/佛罗里达国际马克斯·普朗克脑与行为研究学院）；HHMI Janelia Research Campus（霍华德·休斯医学研究所珍妮莉亚研究园区）；Columbia University（哥伦比亚大学）
Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence measurements. Bayesian model inversion can be used to solve this problem, but typically requires either computationally expensive MCMC sampling, or faster but approximate maximum-a-posteriori optimization. Here, we introduce a flexible algorithmic framework for fast, efficient and accurate extraction of neural spikes from imaging data. Using the f…
Negative Binomial Variational Autoencoders for Overdispersed Latent Modeling
Southeast University（东南大学）；China Medical University Shenyang（中国医科大学沈阳）；University of Chinese Academy of Science（中国科学院大学）；Alibaba Cloud（阿里云）；Center for Applied Statistics and School of Statistics, Renmin University of China（中国人民大学应用统计中心与统计学院）
Calcium imaging has become a powerful and popular technique to monitor the activity of large populations of neurons in vivo. However, for ethical considerations and despite recent technical developments, recordings are still constrained to a limited number of trials and animals. This limits the amount of data available from individual experiments and hinders the development of analysis techniques and models for more realistic sizes of neuronal populations. The ability to artificially synthesize realistic neuronal calcium signals could greatly alleviate this problem by scaling up the number of …
Integration of Calcium Imaging Traces via Deep Generative Modeling
Dept. of Biomedical Sciences \& Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain Dept. of Condensed Matter Physics, Universitat de Barcelona, Barcelona, Spain
University of Edinburgh（爱丁堡大学）；MIT–IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；University of Oslo（奥斯陆大学）
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry. In this work, we present a Bayesian modelling approach for localizing the source of individual spikes on high-density, microelectrode arrays. To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on both biophysically realistic simulated and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting per…
Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under $\ell_1$ constraints allows joint discoveries of governing equations and latent coordinate systems from spatio-temporal data, including simulated video frames. However, it is challenging for $\ell_1$-based sparse inference to perform correct identification for real data due to the noisy measurements and often limited sample sizes. To address the data-driven discovery of physics in the low-data and high-noise regimes, we propose Bayesian SINDy autoencoders, which incorporate a hierarchical Bayesian sp…
Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data
Fordham University（福特汉姆大学）；Georgia Institute of Technology（佐治亚理工学院）；Icahn School of Medicine at Mount Sinai（Mount Sinai医学院）；University of California, San Diego（加州大学圣地亚哥分校）
This article provides a brief introduction to seven papers that are included in this special section on Statistics in Neuroscience: (1) Xiaoyan Shi, Joseph G. Ibrahim, Jeffrey Lieberman, Martin Styner, Yimei Li and Hongtu Zhu: Two-state empirical likelihood for longitudinal neuroimaging data (2) Vincent Q. Vu, Pradeep Ravikumar, Thomas Naselaris, Kendrick N. Kay, Jack L. Gallant and Bin Yu: Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models (3) Sourabh Bhattacharya and Ranjan Maitra: A nonstationary nonparametric Bayesian approach to dynamically modeling…
We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a link between dictionary learning and neural networks. Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias). The forward pass of the encoder approximates the sufficient statistics of the E…
In this paper we propose mmFall - a novel fall detection system, which comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud along with the body centroid, and (ii) a variational recurrent autoencoder (VRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is claimed to have occurred when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor provides several advantages, such as privacycompliance and high-sensitivity to motion, over the traditional sen…
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University（海德堡大学曼heim医学院附属中央精神卫生研究所（CIMH））；Faculty of Physics and Astronomy, Heidelberg University（海德堡大学物理与天文学学院）；Interdisciplinary Center for Scientific Computing, Heidelberg University（海德堡大学跨学科科学计算中心）；Hector Institute for AI in Psychiatry, CIMH（中央精神卫生研究所赫克托精神病学人工智能研究所）
Understanding how activity in neural circuits reshapes following task learning could reveal fundamental mechanisms of learning. Thanks to the recent advances in neural imaging technologies, high-quality recordings can be obtained from hundreds of neurons over multiple days or even weeks. However, the complexity and dimensionality of population responses pose significant challenges for analysis. Existing methods of studying neuronal adaptation and learning often impose strong assumptions on the data or model, resulting in biased descriptions that do not generalize. In this work, we use a varian…
Southern University of Science and Technology（南方科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of the Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）；Dalian University of Technology（大连理工大学）；Pengcheng Laboratory（鹏城实验室）
Artificial neural network (ANN) is a versatile tool to study the neural representation in the ventral visual stream, and the knowledge in neuroscience in return inspires ANN models to improve performance in the task. However, it is still unclear how to merge these two directions into a unified framework. In this study, we propose an integrated framework called Deep Autoencoder with Neural Response (DAE-NR), which incorporates information from ANN and the visual cortex to achieve better image reconstruction performance and higher neural representation similarity between biological and artificia…
