Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Princeton University（普林斯顿大学）；Columbia University（哥伦比亚大学）；University of Pittsburgh（匹兹堡大学）；Massachusetts Institute of Technology（麻省理工学院）；Neuralink Corp.（Neuralink公司）；Stanford University（斯坦福大学）；University of Chicago（芝加哥大学）；Northwestern University（西北大学）；Stony Brook University（石溪大学）；Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）
Felix Pei, Joel Ye, David Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O'Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark Churchland, Mehr… 展开作者
Felix Pei, Joel Ye, David Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O'Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark Churchland, Mehrdad Jazayeri, Lee E. Miller, Jonathan Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this rich activity across diverse neural systems and behaviors, as LVMs do not depend on known relationships between the activity and external experimental variables. However, progress with LVMs for neuronal population activity is currently impeded by a lack of standardization, resulting in methods being developed and compared in an ad hoc manner. To coordinate these modeling efforts, we introduce a benchmark suite for latent variable modeling of neural population activity. We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas. We identify unsupervised evaluation as a common framework for evaluating models across datasets, and apply several baselines that demonstrate benchmark diversity. We release this benchmark through EvalAI. http://neurallatents.github.io
Evaluating Latent Space Structure in Timbre VAEs: A Comparative Study of Unsupervised, Descriptor-Conditioned, and Perceptual Feature-Conditioned Models
Department of Computer Science \& Technology, University of Cambridge\ , United Kingdom
Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes
Center for Machine Learning, Georgia Tech（佐治亚理工学院机器学习中心）；Department of Biomedical Engineering, Georgia Tech and Emory University（佐治亚理工学院与埃默里大学生物医学工程系）；AE Studio（AE工作室）；Department of Neurosurgery, Emory University School of Medicine（埃默里大学医学院神经外科）
Columbia University（哥伦比亚大学）；UT Austin（德克萨斯大学奥斯汀分校）
The ability to record activities from hundreds of neurons simultaneously in the brain has placed an increasing demand for developing appropriate statistical techniques to analyze such data. Recently, deep generative models have been proposed to fit neural population responses. While these methods are flexible and expressive, the downside is that they can be difficult to interpret and identify. To address this problem, we propose a method that integrates key ingredients from latent models and traditional neural encoding models. Our method, pi-VAE, is inspired by recent progress on identifiable …
University of Koblenz-Landau（科布伦茨-兰道大学）；University of Stuttgart（斯图加特大学）
Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time. Human Activity Recognition (HAR) is one of the fields which are actively benefiting from this availability. Unlike most of the approaches addressing HAR by considering predefined activity classes, this paper proposes a novel approach to discover the latent HAR patterns in sequential data. To this end, we employed Latent Dirichlet Allocation (LDA), which is initially a topic modelling approach used in text analysis. To ma…
The Hong Kong Polytechnic University（香港理工大学）；MIT（麻省理工学院）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）
Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitatio…
Probabilistic generative models provide a flexible and systematic framework for learning the underlying geometry of data. However, model selection in this setting is challenging, particularly when selecting for ill-defined qualities such as disentanglement or interpretability. In this work, we address this gap by introducing a method for ranking generative models based on the training dynamics exhibited during learning. Inspired by recent theoretical characterizations of disentanglement, our method does not require supervision of the underlying latent factors. We evaluate our approach by demon…
University of Edinburgh（爱丁堡大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays. Therefore, predicting behaviour from neural population recordings has been shown to be most effective when using latent variable models. Over time however, the activity of single neurons can drift, and different neurons will be recorded due to movement of implanted neural probes. This means that a decoder trained to predict behaviour on one day performs worse when tested on a different day. On the other hand, eviden…
Microsoft Research（微软研究院）；Massachusetts Institute of Technology（麻省理工学院）；University of California Irvine（加州大学尔湾分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Morgridge Institute for Research（莫格里奇研究所）
Gene expression is a readily-observed quantification of transcriptional activity and cellular state that enables the recovery of the relationships between regulators and their target genes. Reconstructing transcriptional regulatory networks from gene expression data is a problem that has attracted much attention, but previous work often makes the simplifying (but unrealistic) assumption that regulator activity is represented by mRNA levels. We use a latent tree graphical model to analyze gene expression without relying on transcription factor expression as a proxy for regulator activity. The l…
Norwegian University of Science and Technology（挪威科技大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）；University of Cambridge（剑桥大学）
Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simple tuning curve functions. This has recently been demonstrated using Gaussian processes, with applications to realistic and topologically relevant latent manifolds. Those and previous models, however, missed crucial shared coding properties of neural populations. We propose featu…
Emory University（埃默里大学）；Georgia Tech（佐治亚理工学院）；The University of Chicago（芝加哥大学）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；NC State University（北卡罗来纳州立大学）
Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of lat…
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specifically, we randomly assign a pseudo parent-class label to each observation which is then modified by applying the domain specific transformation associated with the assigned label. Generated pseudo observation-label pairs are subsequently used to train a neural network with Auto-clustering Output Layer (ACOL) that introduces multiple softmax nodes for each pseudo parent-class. Due to the unsupervised objective based …
We address the problem of learning reusable state representations from streaming high-dimensional observations. This is important for areas like Reinforcement Learning (RL), which yields non-stationary data distributions during training. We make two key contributions. First, we propose an evaluation suite that measures alignment between latent and true low-dimensional states. We benchmark several widely used unsupervised learning approaches. This uncovers the strengths and limitations of existing approaches that impose additional constraints/objectives on the latent space. Our second contribut…
Evaluating the Efficiency of Latent Spaces via the Coupling-Matrix
Faculty of Engineering（工程学院）；Natural Sciences Işık University, İstanbul, Türkiye（自然科学学院，伊斯坦布尔大学）；Natural Sciences Sabancı University, İstanbul, Türkiye（自然科学学院，萨班奇大学）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mayo Clinic（梅奥诊所）
The recent availability of large datasets in bio-medicine has inspired the development of representation learning methods for multiple healthcare applications. Despite advances in predictive performance, the clinical utility of such methods is limited when exposed to real-world data. This study develops model diagnostic measures to detect potential pitfalls before deployment without assuming access to external data. Specifically, we focus on modeling realistic data shifts in electrophysiological signals (EEGs) via data transforms and extend the conventional task-based evaluations with analyses…
This project demonstrated a methodology to estimating cooperate credibility with a Natural Language Processing approach. As cooperate transparency impacts both the credibility and possible future earnings of the firm, it is an important factor to be considered by banks and investors on risk assessments of listed firms. This approach of estimating cooperate credibility can bypass human bias and inconsistency in the risk assessment, the use of large quantitative data and neural network models provides more accurate estimation in a more efficient manner compare to manual assessment. At the beginn…
Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference
Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Southern California（南加州大学）；Department of Computer Science（计算机科学系）；Department of Biomedical Engineering（生物医学工程系）；Neuroscience Graduate Program（神经科学研究生项目）
Machine Learning Methods for Studying Latent Neural Activity Dynamics
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；Department of Computer Science, Cornell University（康奈尔大学计算机科学系）；Department of Neurobiology and Behavior, Cornell University（康奈尔大学神经生物学与行为学系）；Department of Ecology and Evolutionary Biology, Cornell University（康奈尔大学生态学与进化生物学系）；School of Computer Science and Artificial Intelligence, Foshan University（佛山大学计算机科学与人工智能学院）
University of California Berkeley（加州大学伯克利分校）；Duke University School of Medicine（杜克大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Intel Labs（英特尔实验室）
We introduce a novel, probabilistic binary latent variable model to detect noisy or approximate repeats of patterns in sparse binary data. The model is based on the "Noisy-OR model" (Heckerman, 1990), used previously for disease and topic modelling. The model's capability is demonstrated by extracting structure in recordings from retinal neurons, but it can be widely applied to discover and model latent structure in noisy binary data. In the context of spiking neural data, the task is to "explain" spikes of individual neurons in terms of groups of neurons, "Cell Assemblies" (CAs), that often f…
We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep on six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and causal model search to find the most probable graphical model accounting for self-reported sleep efficiency (SE), sleep quality (SQ), and the latent EDA factors. Structural equation modeling was used to confirm fit of the extracted graph. Based on the generated graph, logistic regression and naive Bayes models were used to test the efficacy of the EDA data i…
NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Hong Kong（香港大学）；Shenzhen Loop Area Institute（深圳河套学院）；Tsinghua University（清华大学）；Chinese University of Hong Kong（香港中文大学）
Online Continual Learning (OCL) studies learning over a continuous data stream without observing any single example more than once, a setting that is closer to the experience of humans and systems that must learn "on-the-wild". Yet, commonly available benchmarks are far from these real-world conditions, because they explicitly signal different tasks, lack latent similarity structure or assume temporal independence between different examples. Here, we propose a new benchmark for OCL based on language modelling in which input alternates between different languages and domains without any explici…
École polytechnique fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstrained problems that are hard to tackle with maximum likelihood estimation. In this work, we use coarse-graining and mean-field approximations to derive a bottom-up, neuronally-grounded latent variable model (neuLVM), where the activity of the unobserved neurons is reduced to a low-dimensional mesoscopic description. In contrast to previous la…
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Chemistry and Biochemistry（化学与生物化学系）；Skaggs School of Pharmacy and Pharmaceutical Sciences（斯卡格斯药学与药学科学学校）
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Mount Holyoke College（曼荷莲学院）
We develop nested automatic differentiation (AD) algorithms for exact inference and learning in integer latent variable models. Recently, Winner, Sujono, and Sheldon showed how to reduce marginalization in a class of integer latent variable models to evaluating a probability generating function which contains many levels of nested high-order derivatives. We contribute faster and more stable AD algorithms for this challenging problem and a novel algorithm to compute exact gradients for learning. These contributions lead to significantly faster and more accurate learning algorithms, and are the …
Inferring Latent Market Forces: Evaluating LLM Detection of Gamma Exposure Patterns via Obfuscation Testing
Department of Computer Science Kennesaw State University Marietta, GA, Cobb（计算机科学系 凯尼恩州立大学 马里埃塔, 乔治亚州, 理查兹）；Department of Information Technology Professor of Information Technology Kennesaw State University Marietta, GA（信息科技系 信息科技教授 凯尼恩州立大学 马里埃塔, 乔治亚州）
Large quantifies of online user activity data, such as weekly web search volumes, which co-evolve with the mutual influence of several queries and locations, serve as an important social sensor. It is an important task to accurately forecast the future activity by discovering latent interactions from such data, i.e., the ecosystems between each query and the flow of influences between each area. However, this is a difficult problem in terms of data quantity and complex patterns covering the dynamics. To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts…
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
Groupe de Recherche Interdisciplinaire en Cybersécurité (GRIC)（跨学科网络安全研究组（GRIC））；Department of Computer Science, Université de Sherbrooke（谢布鲁克大学计算机科学系）；Department of Management School, Université de Sherbrooke（谢布鲁克大学管理学院）
