Neurons, behavior, data analysis and theory最新文献_第2页

Mixed-horizon optimal feedback control as a model of human movement 混合视界最优反馈控制作为人体运动模型

Neurons, behavior, data analysis and theory Pub Date : 2021-04-13 DOI: 10.51628/001c.29674

Justinas Česonis, D. W. Franklin

{"title":"Mixed-horizon optimal feedback control as a model of human movement","authors":"Justinas Česonis, D. W. Franklin","doi":"10.51628/001c.29674","DOIUrl":"https://doi.org/10.51628/001c.29674","url":null,"abstract":"Funding information Computational optimal feedback control (OFC) models in the sensorimotor control literature span a vast range of different implementations. Among the popular algorithms, finitehorizon, receding-horizon or infinite-horizon linear-quadratic regulators (LQR) have been broadly used to model human reaching movements. While these different implementations have their unique merits, all three have limitations in simulating the temporal evolution of visuomotor feedback responses. Here we propose a novel approach – a mixed-horizonOFC – by combining the strengths of the traditional finite-horizon and the infinite-horizon controllers to address their individual limitations. Specifically, we use the infinite-horizonOFC to generate durations of themovements, which are then fed into the finite-horizon controller to generate control gains. We then demonstrate the stability of our model by performing extensive sensitivity analysis of both re-optimisation and different cost functions. Finally, we use our model to provide a fresh look to previously published studies by reinforcing the previous results [1], providing alternative explanations to previous studies [2], or generating new predictive results for prior experiments [3].","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84057651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale 深度循环编码器:一个端到端的网络来模拟大规模的脑磁图

Neurons, behavior, data analysis and theory Pub Date : 2021-03-03 DOI: 10.51628/001c.38668

O. Chehab, Alexandre Défossez, Jean-Christophe Loiseau, Alexandre Gramfort, J. King

{"title":"Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale","authors":"O. Chehab, Alexandre Défossez, Jean-Christophe Loiseau, Alexandre Gramfort, J. King","doi":"10.51628/001c.38668","DOIUrl":"https://doi.org/10.51628/001c.38668","url":null,"abstract":"Understanding how the brain responds to sensory inputs from non-invasive brain recordings like magnetoencephalography (MEG) can be particularly challenging: (i) the high-dimensional dynamics of mass neuronal activity are notoriously difficult to model, (ii) signals can greatly vary across subjects and trials and (iii) the relationship between these brain responses and the stimulus features is non-trivial. These challenges have led the community to develop a variety of preprocessing and analytical (almost exclusively linear) methods, each designed to tackle one of these issues. Instead, we propose to address these challenges through a specific end-to-end deep learning architecture, trained to predict the MEG responses of multiple subjects at once. We successfully test this approach on a large cohort of MEG recordings acquired during a one-hour reading task. Our Deep Recurrent Encoder (DRE) reliably predicts MEG responses to words with a three-fold improvement over classic linear methods. We further describe a simple variable importance analysis to investigate the MEG representations learnt by our model and recover the expected evoked responses to word length and word frequency. Last, we show that, contrary to linear encoders, our model captures modulations of the brain response in relation to baseline fluctuations in the alpha frequency band. The quantitative improvement of the present deep learning approach paves the way to a better characterization of the complex dynamics of brain activity from large MEG datasets.","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82532932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Statistical analysis of periodic data in neuroscience 神经科学周期性数据的统计分析

Neurons, behavior, data analysis and theory Pub Date : 2021-01-12 DOI: 10.51628/001c.27680

D. Baker

{"title":"Statistical analysis of periodic data in neuroscience","authors":"D. Baker","doi":"10.51628/001c.27680","DOIUrl":"https://doi.org/10.51628/001c.27680","url":null,"abstract":"Many experimental paradigms in neuroscience involve driving the nervous system with periodic sensory stimuli. Neural signals recorded using a variety of techniques will then include phase-locked oscillations at the stimulation frequency. The analysis of such data often involves standard univariate statistics such as T-tests, conducted on the Fourier amplitude components (ignoring phase), either to test for the presence of a signal, or to compare signals across different conditions. However, the assumptions of these tests will sometimes be violated because amplitudes are not normally distributed, and furthermore weak signals might be missed if the phase information is discarded. An alternative approach is to conduct multivariate statistical tests using the real and imaginary Fourier components. Here the performance of two multivariate extensions of the T-test are compared: Hotelling's $T^2$ and a variant called $T^2_{circ}$. A novel test of the assumptions of $T^2_{circ}$ is developed, based on the condition index of the data (the square root of the ratio of eigenvalues of a bounding ellipse), and a heuristic for excluding outliers using the Mahalanobis distance is proposed. The $T^2_{circ}$ statistic is then extended to multi-level designs, resulting in a new statistical test termed $ANOVA^2_{circ}$. This has identical assumptions to $T^2_{circ}$, and is shown to be more sensitive than MANOVA when these assumptions are met. The use of these tests is demonstrated for two publicly available empirical data sets, and practical guidance is suggested for choosing which test to run. Implementations of these novel tools are provided as an R package and a Matlab toolbox, in the hope that their wider adoption will improve the sensitivity of statistical inferences involving periodic data.","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81747179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning. 利用反强化学习预测目标导向的注意力控制

Neurons, behavior, data analysis and theory Pub Date : 2021-01-01 Epub Date: 2021-04-20 DOI: 10.51628/001c.22322

Gregory J Zelinsky, Yupei Chen, Seoyoung Ahn, Hossein Adeli, Zhibo Yang, Lihan Huang, Dimitrios Samaras, Minh Hoai

{"title":"Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning.","authors":"Gregory J Zelinsky, Yupei Chen, Seoyoung Ahn, Hossein Adeli, Zhibo Yang, Lihan Huang, Dimitrios Samaras, Minh Hoai","doi":"10.51628/001c.22322","DOIUrl":"10.51628/001c.22322","url":null,"abstract":"Understanding how goals control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train models. To annotate a large-scale image dataset with observed search fixations, we collected 16,184 fixations from people searching for either microwaves or clocks in a dataset of 4,366 images (MS-COCO). We then used this behaviorally-annotated dataset and the machine learning method of inverse-reinforcement learning (IRL) to learn target-specific reward functions and policies for these two target goals. Finally, we used these learned policies to predict the fixations of 60 new behavioral searchers (clock = 30, microwave = 30) in a disjoint test dataset of kitchen scenes depicting both a microwave and a clock (thus controlling for differences in low-level image contrast). We found that the IRL model predicted behavioral search efficiency and fixation-density maps using multiple metrics. Moreover, reward maps from the IRL model revealed target-specific patterns that suggest, not just attention guidance by target features, but also guidance by scene context (e.g., fixations along walls in the search of clocks). Using machine learning and the psychologically meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218820/pdf/nihms-1715365.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39101639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strong and weak principles of neural dimension reduction 神经降维的强弱原则

Neurons, behavior, data analysis and theory Pub Date : 2020-11-16 DOI: 10.51628/001c.24619

M. Humphries

{"title":"Strong and weak principles of neural dimension reduction","authors":"M. Humphries","doi":"10.51628/001c.24619","DOIUrl":"https://doi.org/10.51628/001c.24619","url":null,"abstract":"If spikes are the medium, what is the message? Answering that question is driving the development of large-scale, single neuron resolution recordings from behaving animals, on the scale of thousands of neurons. But these data are inherently high-dimensional, with as many dimensions as neurons - so how do we make sense of them? For many the answer is to reduce the number of dimensions. Here I argue we can distinguish weak and strong principles of neural dimension reduction. The weak principle is that dimension reduction is a convenient tool for making sense of complex neural data. The strong principle is that dimension reduction shows us how neural circuits actually operate and compute. Elucidating these principles is crucial, for which we subscribe to provides radically different interpretations of the same neural activity data. I show how we could make either the weak or strong principles appear to be true based on innocuous looking decisions about how we use dimension reduction on our data. To counteract these confounds, I outline the experimental evidence for the strong principle that do not come from dimension reduction; but also show there are a number of neural phenomena that the strong principle fails to address. To reconcile these conflicting data, I suggest that the brain has both principles at play.","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73754293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Comparing representational geometries using whitened unbiased-distance-matrix similarity 使用白化无偏距离矩阵相似性比较代表性几何

Neurons, behavior, data analysis and theory Pub Date : 2020-07-06 DOI: 10.51628/001c.27664

J. Diedrichsen, Eva Berlot, Marieke Mur, Heiko H. Schütt, Mahdiyar Shahbazi, N. Kriegeskorte

{"title":"Comparing representational geometries using whitened unbiased-distance-matrix similarity","authors":"J. Diedrichsen, Eva Berlot, Marieke Mur, Heiko H. Schütt, Mahdiyar Shahbazi, N. Kriegeskorte","doi":"10.51628/001c.27664","DOIUrl":"https://doi.org/10.51628/001c.27664","url":null,"abstract":"Representational similarity analysis (RSA) tests models of brain computation by investigating how neural activity patterns reflect experimental conditions. Instead of predicting activity patterns directly, the models predict the geometry of the representation, as defined by the representational dissimilarity matrix (RDM), which captures to what extent experimental conditions are associated with similar or dissimilar activity patterns. RSA therefore first quantifies the representational geometry by calculating a dissimilarity measure for each pair of conditions, and then compares the estimated representational dissimilarities to those predicted by each model. Here we address two central challenges of RSA: First, dissimilarity measures such as the Euclidean, Mahalanobis, and correlation distance, are biased by measurement noise, which can lead to incorrect inferences. Unbiased dissimilarity estimates can be obtained by crossvalidation, at the price of increased variance. Second, the pairwise dissimilarity estimates are not statistically independent, and ignoring this dependency makes model comparison statistically suboptimal. We present an analytical expression for the mean and (co)variance of both biased and unbiased estimators of the squared Euclidean and Mahalanobis distance, allowing us to quantify the bias-variance trade-off. We also use the analytical expression of the covariance of the dissimilarity estimates to whiten the RDM estimation errors. This results in a new criterion for RDM similarity, the whitened unbiased RDM cosine similarity (WUC), which allows for near-optimal model selection combined with robustness to correlated measurement noise.","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78117809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Overcoming the Weight Transport Problem via Spike-Timing-Dependent Weight Inference 利用峰值时间相关权重推断克服权重传输问题

Neurons, behavior, data analysis and theory Pub Date : 2020-03-09 DOI: 10.51628/001c.27423

Nasir Ahmad, L. Ambrogioni, M. Gerven

引用次数: 1

Application of the hierarchical bootstrap to multi-level data in neuroscience. 层次自举法在神经科学多层次数据中的应用。

Neurons, behavior, data analysis and theory Pub Date : 2020-01-01 Epub Date: 2020-07-21

Varun Saravanan, Gordon J Berman, Samuel J Sober

{"title":"Application of the hierarchical bootstrap to multi-level data in neuroscience.","authors":"Varun Saravanan, Gordon J Berman, Samuel J Sober","doi":"","DOIUrl":"","url":null,"abstract":"A common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across multiple trials. Accordingly, the measurements constituting the dataset are not independent, even though the traditional statistical analyses often applied in such cases (e.g., Student's t-test) treat them as such. The hierarchical bootstrap has been shown to be an effective tool to accurately analyze such data and while it has been used extensively in the statistical literature, its use is not widespread in neuroscience - despite the ubiquity of hierarchical datasets. In this paper, we illustrate the intuitiveness and utility of this approach to analyze hierarchically nested datasets. We use simulated neural data to show that traditional statistical tests can result in a false positive rate of over 45%, even if the Type-I error rate is set at 5%. While summarizing data across non-independent points (or lower levels) can potentially fix this problem, this approach greatly reduces the statistical power of the analysis. The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods. We conclude by demonstrating the effectiveness of the method in two real-world examples, first analyzing singing data in male Bengalese finches (Lonchura striata var. domestica) and second quantifying changes in behavior under optogenetic control in flies (Drosophila melanogaster).","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906290/pdf/nihms-1630846.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25414976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals. 时变神经信号贝叶斯单试验分析的敏感性和特异性。

Neurons, behavior, data analysis and theory Pub Date : 2020-01-01

Jeff T Mohl, Valeria C Caruso, Surya T Tokdar, Jennifer M Groh

{"title":"Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals.","authors":"Jeff T Mohl, Valeria C Caruso, Surya T Tokdar, Jennifer M Groh","doi":"","DOIUrl":"","url":null,"abstract":"We recently reported the existence of fluctuations in neural signals that may permit neurons to code multiple simultaneous stimuli sequentially across time [1]. This required deploying a novel statistical approach to permit investigation of neural activity at the scale of individual trials. Here we present tests using synthetic data to assess the sensitivity and specificity of this analysis. We fabricated datasets to match each of several potential response patterns derived from single-stimulus response distributions. In particular, we simulated dual stimulus trial spike counts that reflected fluctuating mixtures of the single stimulus spike counts, stable intermediate averages, single stimulus winner-take-all, or response distributions that were outside the range defined by the single stimulus responses (such as summation or suppression). We then assessed how well the analysis recovered the correct response pattern as a function of the number of simulated trials and the difference between the simulated responses to each \"stimulus\" alone. We found excellent recovery of the mixture, intermediate, and outside categories (>97% correct), and good recovery of the single/winner-take-all category (>90% correct) when the number of trials was >20 and the single-stimulus response rates were 50Hz and 20Hz respectively. Both larger numbers of trials and greater separation between the single stimulus firing rates improved categorization accuracy. These results provide a benchmark, and guidelines for data collection, for use of this method to investigate coding of multiple items at the individual-trial time scale.","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425354/pdf/nihms-1702888.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10506788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide 使用TensorFlow的生物神经网络并行可扩展模拟:初学者指南

Neurons, behavior, data analysis and theory Pub Date : 2019-06-10 DOI: 10.51628/001c.37893

Rishika Mohanta, Collins G. Assisi

{"title":"Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide","authors":"Rishika Mohanta, Collins G. Assisi","doi":"10.51628/001c.37893","DOIUrl":"https://doi.org/10.51628/001c.37893","url":null,"abstract":"Biological neural networks are often modeled as systems of coupled, nonlinear, ordinary or partial differential equations. The number of differential equations used to model a network increases with the size of the network and the level of detail used to model individual neurons and synapses. As one scales up the size of the simulation, it becomes essential to utilize powerful computing platforms. While many tools exist that solve these equations numerically, they are often platform-specific. Further, there is a high barrier of entry to developing flexible platform-independent general-purpose code that supports hardware acceleration on modern computing architectures such as GPUs/TPUs and Distributed Platforms. TensorFlow is a Python-based open-source package designed for machine learning algorithms. However, it is also a scalable environment for a variety of computations, including solving differential equations using iterative algorithms such as Runge-Kutta methods. In this article and the accompanying tutorials, we present a simple exposition of numerical methods to solve ordinary differential equations using Python and TensorFlow. The tutorials consist of a series of Python notebooks that, over the course of five sessions, will lead novice programmers from writing programs to integrate simple one-dimensional ordinary differential equations using Python to solving a large system (1000’s of differential equations) of coupled conductance-based neurons using a highly parallelized and scalable framework. Embedded with the tutorial is a physiologically realistic implementation of a network in the insect olfactory system. This system, consisting of multiple neuron and synapse types, can serve as a template to simulate other networks.","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88060526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3