The underlying mechanisms and corresponding neural circuits involved in how cognition and perception emerge in the brain have been actively studied for over fifty years. Fully understanding how these traits emerge is complex and challenging, leading to the development of novel tools, both experimental and computational. Over the last three decades, computational models have played important roles in investigating the complex action of neural circuits involved in perception and cognitive function, while developing a cellular and network-level description of how external information is transformed and perceived in the brain, in an orderly fashion. Despite the complex nature of such information, the brain can easily decode information through the coordinated activity of neural populations and corresponding processes. Here, computational models have proved useful for understanding such processes via building network models of neural dynamics, inspired by the mammalian cortex's hierarchical (deep) organisation, which can mimic the activity of neural populations involved in some perceptual or cognitive task.Fifteen years have passed since the inception of Frontiers in Computational Neuroscience and this research topic is a celebration of the 15 th year anniversary of the journal and for the community to highlight new results focusing on computational perception and cognition. This research topic, as a part of a series aimed at showcasing recent advances in the field of computational perception and cognition, provides an outlet to discuss current challenges and exciting new developments in computational perception and cognition. The presented articles on this research topic provide a snapshot of recent research outcomes, that will hopefully inspire others to investigate the underlying neural correlates of cognition and perception.Investigations presented in this Topic, focus on several important aspects for perception and cognition, such as information coding and memory capacity. Wei and Li (2023) proposed that using directed graphs as abstractions of biological neural networks along with node-adaptive learning can encode, store, and retrieve information and further illustrated consistent memory performance, that outperformed Hopfield network in both memory retreival accuracy and storage capacity.
Frontiers in Computational Neuroscience is a multidisciplinary journal that focuses on the theoretical modeling of brain function and encourages multidisciplinary interactions between theoretical and experimental neuroscience. Our mission aligns closely with advancing global health and wellness goals, particularly the United Nations’ Sustainable Development Goal 3: good health and well-being by promoting a deeper understanding of brain function and fostering research and collaboration in the field. This contributes to the development of new knowledge and technologies that can potentially improve mental health, neurological disorders, and overall well being, aligning with the broader goal of ensuring healthy lives and promoting well-being for all at all ages. Here we are pleased to introduce this Theme book entitled ‘Research Highlights from Frontiers in Computational Neuroscience: 2024’ curated by our esteemed Chief Editors of Frontiers in Computational Neuroscience. This collection honors the remarkable contributions of authors who have furthered our understanding of computational neuroscience through innovative and impactful research. The work presented here spotlights the broad diversity of exciting research performed across the journal. We hope you enjoy our selection of key articles. We also thank all authors, editors, and reviewers of Frontiers in Computational Neuroscience for their contributions to our journal and look forward to another exciting year in 2025.
Thermal infrared imaging has been proposed, and is now used, as a tool for the non-contact and non-invasive computational assessment of human autonomic nervous activity and psychophysiological states. Thanks to a new generation of high sensitivity infrared thermal detectors and the development of computational models of the autonomic control of the facial cutaneous temperature, several autonomic variables can be computed through thermal infrared imaging, including localized blood perfusion rate, cardiac pulse rate, breath rate, sudomotor and stress responses. In fact, all of these parameters impact on the control of the cutaneous temperature. The physiological information obtained through this approach, could then be used to infer about a variety of psychophysiological or emotional states, as proved by the increasing number of psychophysiology or neurosciences studies that use thermal infrared imaging. This paper presents a review of the principal achievements of thermal infrared imaging in computational psychophysiology, focusing on the capability of the technique for providing ubiquitous and unwired monitoring of psychophysiological activity and affective states. It also presents a summary on the modern, up-to-date infrared sensors technology.
Previous article Next article Non-Parametric Estimation of a Multivariate Probability DensityV. A. EpanechnikovV. A. Epanechnikovhttps://doi.org/10.1137/1114019PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] Emanuel Parzen, On estimation of a probability density function and mode, Ann. Math. Statist., 33 (1962), 1065–1076 MR0143282 0116.11302 CrossrefGoogle Scholar[2] Murray Rosenblatt, Remarks on some nonparametric estimates of a density function, Ann. Math. Statist., 27 (1956), 832–837 MR0079873 0073.14602 CrossrefGoogle Scholar[3] G. M. Manija, Remarks on non-parametric estimates of a two-dimensional density function, Soobšč. Akad. Nauk Gruzin. SSR, 27 (1961), 385–390 MR0143303 Google Scholar[4] E. A. Nadaraya, Estimation of a bivariate probability density, Soobshch. Akad. Nauk Gruz. SSR, 36 (1964), 267–268 Google Scholar[5] R. E. Bellman, , I. Glicksberg and , O. A. Gross, Some aspects of the mathematical theory of control processes, Rand Corporation, Santa Monica, Calif., Rep. No. R-313, 1958rm xix+244 MR0094281 0086.11703 Google Scholar Previous article Next article FiguresRelatedReferencesCited byDetails Kernel-based learning of birth process from evolving spatiotemporal RFS data stream in SMC CPHD filter for multi-target trackingSignal Processing, Vol. 203 Cross Ref Assessing spatial connectivity effects on daily streamflow forecasting using Bayesian-based graph neural networkScience of The Total Environment, Vol. 855 Cross Ref Joint Source-Channel Decoding of Polar Codes for HEVC-Based Video StreamingACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 18, No. 4 Cross Ref A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecastingApplied Energy, Vol. 325 Cross Ref A novel structure adaptive fractional discrete grey forecasting model and its application in China’s crude oil production predictionExpert Systems with Applications, Vol. 207 Cross Ref Age-varying effects of repeated emergency department presentations for children in Canada6 May 2022 | Journal of Health Services Research & Policy, Vol. 27, No. 4 Cross Ref Probabilistic adaptive power pinch analysis for islanded hybrid energy storage systemsJournal of Energy Storage, Vol. 54 Cross Ref Should I stay or should I fly? Migration phenology, individual-based migration decision and seasonal changes in foraging behaviour of Common Woodpigeons17 August 2022 | The Science of Nature, Vol. 109, No. 5 Cross Ref Machine learning and optimization based decision-support tool for seed variety selection24 September 2022 | Annals of Operations Research, Vol. 65 Cross Ref Estimating regional income indicators under transformations and access to limited population auxiliary information16 September 2022 | Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 37 Cross Ref Automated calibration of model-driven reconstructions in atom probe tomography1 July 2022 | Journal of Physics D: Applied Physics, Vol. 55, No. 37 Cross Ref Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning modelEnergy, Vol. 254 Cross Ref Deep attention ConvLSTM-based adaptive fusion of clear-sky physical prior knowledge and multivariable historical information for probabilistic prediction of photovoltaic powerExpert Systems with Applications, Vol. 202 Cross Ref The Taxonomy of Mineral Occurrence Rarity and Endemicity21 October 2022 | The Canadian Mineralogist, Vol. 60, No. 5 Cross Ref Estimation of wind speed distribution with time window and new kernel functionJournal of Renewable and Sustainable Energy, Vol. 14, No. 5 Cross Ref Future crop risk estimation due to drought, extreme temperature, hail, lightning, and tornado at the census tract level in louisiana23 August 2022 | Frontiers in Environmental Science, Vol. 10 Cross Ref Translocation detection from Hi‐C data via scan statistics10 August 2022 | Biometrics, Vol. 8 Cross Ref Does restricting therapeutic antibiotics use influence efficiency of pig farms? Evidence from Denmark’s Yellow Card Initiative18 May 2022 | European Review of Agricultural Economics, Vol. 49, No. 4 Cross Ref Computational approach to modeling microbiome landscapes associated with chronic human disease progression4 August 2022 | PLOS Computational Biology, Vol. 18, No. 8 Cross Ref Spillovers between exchange rate pressure and CDS bid-ask spreads, reserve assets and oil prices using the quantile ARDL modelInternational Economics, Vol. 170 Cross Ref Liability Structure and Risk Taking: Evidence from the Money Market Fund Industry18 June 2021 | Journal of Financial and Quantitative Analysis, Vol. 57, No. 5 Cross Ref Security risk assessment of wind integrated power system using Parzen window density estimation6 January 2022 | Electrical Engineering, Vol. 104, No. 4 Cross Ref Predator or prey? Effects of farm growth on neighbouring farms25 July 2022 | Journal of Agricultural Economics, Vol. 4 Cross Ref Multimodel Errors and Emergence Times in Climate Attribution StudiesJournal of Climate, Vol. 35, No. 14 Cross Ref The hierarchical structure of galactic haloes: generalized N -dimensional clustering with C lu STAR-ND20 June 2022 | Monthly Notices of the Royal Astronomical Society, Vol. 514, No. 4 Cross Ref Effective End-to-End Learning Framework for Economic DispatchIEEE Transactions on Network Science and Engineering, Vol. 9, No. 4 Cross Ref 4-D Gesture Sensing Using Reconfigurable Virtual Array Based on a 60-GHz FMCW MIMO Radar SensorIEEE Transactions on Microwave Theory and Techniques, Vol. 70, No. 7 Cross Ref Estimation of drift and diffusion functions from unevenly sampled time-series data27 July 2022 | Physical Review E, Vol. 106, No. 1 Cross Ref Detecting space–time patterns of disease risk under dynamic background population20 April 2022 | Journal of Geographical Systems, Vol. 24, No. 3 Cross Ref Wind power prediction based on PSO-KalmanEnergy Reports, Vol. 8 Cross Ref Visual cluster separation using high-dimensional sharpened dimensionality reduction21 April 2022 | Information Visualization, Vol. 21, No. 3 Cross Ref Interval Wind-Speed Forecasting Model Based on Quantile Regression Bidirectional Minimal Gated Memory Network and Kernel Density Estimation17 June 2022 | Arabian Journal for Science and Engineering, Vol. 4 Cross Ref Spatio-temporal process monitoring using exponentially weighted spatial LASSO2 June 2022 | Journal of Quality Technology, Vol. 58 Cross Ref Bridge deformation prediction based on SHM data using improved VMD and conditional KDEEngineering Structures, Vol. 261 Cross Ref Nonparametric extrapolation of extreme quantiles: a comparison study7 October 2021 | Stochastic Environmental Research and Risk Assessment, Vol. 36, No. 6 Cross Ref Schedule Performance as a Baseline for the Experimental Analysis of Coordinated Behavior: Same or Different Units of Analysis?24 February 2022 | The Psychological Record, Vol. 72, No. 2 Cross Ref Rainfall intensity and catchment size control storm runoff in a gullied blanket peatlandJournal of Hydrology, Vol. 609 Cross Ref Creating a Healthy Environment for Children: GIS Tools for Improving the Quality of the Social Welfare Management System10 June 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 12 Cross Ref Conditional catheter‐related thrombosis free probability and risk‐adapted choices of catheter for lung cancer13 May 2022 | Thoracic Cancer, Vol. 13, No. 12 Cross Ref Search for an anomalous excess of charged-current quasielastic νe interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction13 June 2022 | Physical Review D, Vol. 105, No. 11 Cross Ref Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusionRenewable Energy, Vol. 192 Cross Ref Dynamic disease screening by joint modelling of survival and longitudinal data27 May 2022 | Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 23 Cross Ref Uncertainties in the Assessment of COVID-19 Risk: A Study of People’s Exposure to High-Risk Environments Using Individual-Level Activity Data20 September 2021 | Annals of the American Association of Geographers, Vol. 112, No. 4 Cross Ref Incremental software product line verification - A performance analysis with dead variable code17 March 2022 | Empirical Software Engineering, Vol. 27, No. 3 Cross Ref Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary modelsJournal of Econometrics, Vol. 117 Cross Ref An Unconventional Technique for Choosing the Kernel Function Blur Coefficients in Nonparametric Regression18 August 2022 | Measurement Techniques, Vol. 65, No. 2 Cross Ref Specificities of ERD lateralization during motion execution Cross Ref Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection21 April 2022 | Journal of Nonparametric Statistics, Vol. 34, No. 2 Cross Ref Concurrent Effects between Geomagnetic Storms and Magnetospheric Substorms6 April 2022 | Universe, Vol. 8, No. 4 Cross Ref Deep non-crossing probabilistic wind speed forecasting with multi-scale featuresEnergy Conversion and Management, Vol. 257 Cross Ref Efficient and robust propensity‐score‐based methods for population inference using epidemiologic cohorts6 September 2021 | International Statistical Review, Vol. 90, No. 1 Cross Ref Estimation of a Nonlinear Functional of the Probability Density of a Three-Dimensional Random Variable to Improve the Computational Efficiency of Nonparametric Decision Rules28 August 2022 | Optoelectronics, Instrumentation and Data Processing, Vol. 58, No. 2 Cross Ref The relation between belief in a just world and early processing of deserved and undeserved outcomes: An ERP study13 February 2022 | Social Neuroscience, Vol. 17, No. 2 Cross Ref Mutual information scaling for tensor network machine learning20 January 2022 | Machine Learning: Science and Technology, Vol. 3, No. 1 Cross Ref Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point cloudsAutomation in Construction, Vol. 135 Cross Ref OnlineSTLProceedings of the VLDB Endowment, Vol. 15, No. 7 Cross Ref On the application of generative adversarial networks for nonlinear modal analysisMechanical Systems and Signal Processing, Vol. 166 Cross Ref Interval Prediction Method for Solar Radiation Based on Kernel Density Estimation and Machine LearningComplexity, Vol. 2022 Cross Ref A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal Data20 March 2021 | Annals of the Institute of Statistical Mathematics, Vol. 74, No. 1 Cross Ref Phase I monitoring of serially correlated nonparametric profiles by mixed‐effects modeling28 July 2021 | Quality and Reliability Engineering International, Vol. 38, No. 1 Cross Ref Data-driven prosumer-centric energy scheduling using convolutional neural networksApplied Energy, Vol. 308 Cross Ref The (un)predictable magnetosphere: the role of the internal dynamics3 March 2022 | Journal of Plasma Physics, Vol. 88, No. 1 Cross Ref Quick Selecting Kernel Blur Coefficients to Estimate Probability Density for Independent Random Variables8 July 2022 | Optoelectronics, Instrumentation and Data Processing, Vol. 58, No. 1 Cross Ref Robust analogs to the coefficient of variation20 August 2020 | Journal of Applied Statistics, Vol. 49, No. 2 Cross Ref Nonparametric Mass Imputation for Data Integration17 November 2020 | Journal of Survey Statistics and Methodology, Vol. 10, No. 1 Cross Ref Regional wind power probabilistic forecasting based on an improved kernel density estimation, regular vine copulas, and ensemble learningEnergy, Vol. 238 Cross Ref Probabilistic Revenue Analysis of Microgrid Considering Source-Load and Forecast UncertaintiesIEEE Access, Vol. 10 Cross Ref Data-Enhancement Strategies in Weather-Related Health Studies14 January 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 2 Cross Ref Model-based techniques for traffic congestion detection Cross Ref Object-based cluster validation with densitiesPattern Recognition, Vol. 121 Cross Ref Towards Robust Waveform-Based Acoustic ModelsIEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 30 Cross Ref Nonparametric Survival Analysis20 July 2022 Cross Ref A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation DataIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15 Cross Ref Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library1 January 2022 | Faraday Discussions, Vol. 8 Cross Ref Joint modeling of multivariate nonparametric longitudinal data and survival data: A local smoothing approach25 September 2021 | Statistics in Medicine, Vol. 40, No. 29 Cross Ref Shock Reduction Technique on Thin Plate Structure by Wave Refraction Using an Elastic PatchShock and Vibration, Vol. 2021 Cross Ref Persistent meanders and eddies lead to quasi-steady Lagrangian transport patterns in a weak western boundary current12 January 2021 | Scientific Reports, Vol. 11, No. 1 Cross Ref Caenorhabditis elegans exhibits positive gravitaxis14 September 2021 | BMC Biology, Vol. 19, No. 1 Cross Ref Niche partitioning among social clusters of a resident estuarine apex predator15 November 2021 | Behavioral Ecology and Sociobiology, Vol. 75, No. 12 Cross Ref Study of the Method for Verification of the Hypothesis on Independence of Two-Dimensional Random Quantities Using a Nonparametric Classifier13 May 2022 | Optoelectronics, Instrumentation and Data Processing, Vol. 57, No. 6 Cross Ref Epanechnikov kernel for PDF estimation applied to equalization and blind source separationSignal Processing, Vol. 189 Cross Ref Age-coherent extensions of the Lee–Carter model29 April 2021 | Scandinavian Actuarial Journal, Vol. 2021, No. 10 Cross Ref compaso : A new halo finder for competitive assignment to spherical overdensities19 October 2021 | Monthly Notices of the Royal Astronomical Society, Vol. 509, No. 1 Cross Ref Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model26 October 2021 | Mathematics, Vol. 9, No. 21 Cross Ref A method of sequentially generating a set of components of a multidimensional random variable using a nonparametric pattern recognition algorithm1 November 2021 | Computer Optics, Vol. 45, No. 6 Cross Ref Year-round spatial distribution and migration phenology of a rapidly declining trans-Saharan migrant—evidence of winter movements and breeding site fidelity in European turtle doves13 October 2021 | Behavioral Ecology and Sociobiology, Vol. 75, No. 11 Cross Ref Efficiency of European universities: A comparison of peersResearch Policy, Vol. 50, No. 9 Cross Ref Heads Or Tails: A Framework To Model Supply Chain Heterogeneous Messages Cross Ref Research of Wind Power Generation Characteristics in Southern China Based on Improved Non-Parametric Kernel Density Estimation Cross Ref Fast multivariate empirical cumulative distribution function with connection to kernel density estimationComputational Statistics & Data Analysis, Vol. 162 Cross Ref Some confidence intervals and insights for the proportion below the relative poverty line13 October 2021 | SN Business & Economics, Vol. 1, No. 10 Cross Ref The HARPS search for southern extra-solar planets18 October 2021 | Astronomy & Astrophysics, Vol. 654 Cross Ref The sectorally heterogeneous and time-varying price elasticities of energy demand in ChinaEnergy Economics, Vol. 102 Cross Ref Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling29 September 2021 | Statistical Modelling, Vol. 124 Cross Ref SAFE-STOP System: Tactical Intention Awareness Based Emergency Collision Avoidance for Malicious Cut-in of Surrounding Vehicle Cross Ref Violin graphs to supervise the energy performance of PV arrays Cross Ref Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecastingApplied Energy, Vol. 298 Cross Ref The continuous wavelet derived by smoothing function and its application in cosmology5 August 2021 | Communications in Theoretical Physics, Vol. 73, No. 9 Cross Ref Inertial Sensor Algorithms to Characterize Turning in Neurological Patients With Turn HesitationsIEEE Transactions on Biomedical Engineering, Vol. 68, No. 9 Cross Ref Consistent inference for predictive regressions in persistent economic systemsJournal of Econometrics, Vol. 224, No. 1 Cross Ref Identifying convergence in nitrogen oxides emissions from motor vehicles in China: A spatial panel data approachJournal of Cleaner Production, Vol. 316 Cross Ref Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation22 September 2021 | Journal of The Royal Society Interface, Vol. 18, No. 182 Cross Ref Nonparametric pattern recognition algorithm for testing a hypothesis of the independence of random variables1 September 2021 | Computer Optics, Vol. 5, No. 45 Cross Ref Noise and error analysis and optimization in particle-based kinetic plasma simulationsJournal of Computational Physics, Vol. 440 Cross Ref Unsupervised Learning Methods for Molecular Simulation Data4 May 2021 | Chemical Reviews, Vol. 121, No. 16 Cross Ref A robust dynamic screening system by estimation of the longitudinal data distribution26 May 2020 | Journal of Quality Technology, Vol. 53, No. 4 Cross Ref Ellipsoidal one-class constraint acquisition for quadratically constrained programmingEuropean Journal of Operational Research, Vol. 293, No. 1 Cross Ref Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experimentsReliability Engineering & System Safety, Vol. 212 Cross Ref Effective disease surveillance by using covariate information30 July 2021 | Statistics in Medicine, Vol. 59 Cross Ref Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models6 January 2021 | Journal of Time Series Analysis, Vol. 42, No. 4 Cross Ref between and to the of level and in the of in Research, Vol. 68, No. 2 Cross Ref energy demand spatio-temporal data Vol. Cross Ref Learning for Statistical of June 2021 | Vol. 29 Cross Ref joint distribution analysis of the under semiparametric distribution for the in of Engineering and Science, Vol. No. 2 Cross Ref model of Econometrics, Vol. No. 2 Cross Ref of Based on and Network with the Quantile May 2021 | Applied Vol. 11, No. 11 Cross Ref An to multivariate probabilistic and Vol. 4 Cross Ref based on deep learning model with attention for hybrid system under Energy, Vol. 170 Cross Ref and March 2022 | Mathematics, Vol. No. 1 Cross Ref Uncertainty Analysis of Wind Power Based on Data Cross Ref kernel density estimation Systems with Applications, Vol. Cross Ref network modelling of the of a with the Physics Vol. Cross Ref March 2021 | Journal of Computational and Vol. No. 2 Cross Ref modeling of for July 2020 | Reviews, Vol. 40, No. 3 Cross Ref for Cross Ref on the size of February 2021 | of the of Vol. No. 9 Cross Ref Uncertainty analysis of wind power probability density forecasting based on and quantile Vol. Cross Ref of Methods in Data of March 2021 | International Journal of Vol. 10, No. 3 Cross Ref Dynamic spatial analysis of China: and spatial convergence Research, Vol. No. 3 Cross Ref the Hypothesis of the Independence of Two-Dimensional Random Using a Nonparametric for August 2021 | Optoelectronics, Instrumentation and Data Processing, Vol. 57, No. 2 Cross Ref A Study of Nonparametric Kernel with of Vol. No. 1 Cross Ref Fast Modelling, Vol. Cross Ref for of Coefficients for Kernel of Probability March 2021 | Measurement Techniques, Vol. No. 11 Cross Ref of laser fusion Vol. Cross Ref A of density based clustering September 2020 | Frontiers of Computer Science, Vol. 15, No. 1 Cross Ref of Information and Regression for of Acoustic Data during on Structural and Construction, Vol. No. 1 Cross Ref of Heterogeneous in January 2021 | Research Vol. No. 2 Cross Ref for Time Series of Cross Ref Data-driven Kernel-based Probabilistic for Time Series Reduction Cross Ref The for Robust Energy Estimation Cross Ref from August 2020 Cross Ref during Chain for Detecting Using from 2020 | Vol. No. 1 Cross Ref Method for of in Using and 2020 | Remote Sensing, Vol. 13, No. 1 Cross Ref of a Framework for Environmental of 2020 | Journal of Science and Engineering, Vol. 9, No. 1 Cross Ref a for kernel density estimation, and of Vol. No. Cross Ref Analysis of the of the mean of the kernel probability density estimation in the of and random variables1 January 2021 | No. 3 Cross Ref Energy Management of April 2021 Cross Ref for Detecting of July 2021 Cross Ref of a Analysis Method to the of COVID-19 Kernel Density Estimation Using July 2021 Cross Ref On generative as the for August 2021 | Engineering, Vol. 2 Cross Ref and in the January 2021 | Journal of Vol. 27, No. 1 Cross Ref The in January 2021 | Vol. No. 5 Cross Ref Kernel Based for MIMO Radar in Access, Vol. 9 Cross Ref The diffusion of diffusion and Computational Analysis, Vol. Cross Ref of of components of a multidimensional random variable based on a nonparametric pattern recognition algorithm1 January 2021 | No. 9 Cross Ref in energy efficiency of and its for performance Journal of and Engineering, Vol. Cross Ref of Nonlinear Systems with Cross Ref Probability density forecasting of wind power based on quantile neural Systems, Vol. Cross Ref Hypothesis testing based on a of of Econometrics, Vol. No. 2 Cross Ref Analysis of distribution human in the of Lake in Vol. Cross Ref in and May 2020 | Scientific Reports, Vol. 10, No. 1 Cross Ref Assessment of regressions for in the insights from and historical June 2020 | Vol. 17, No. 12 Cross Ref hybrid and for September 2020 | Journal on Communications and Vol. No. 1 Cross Ref of March 2021 | Physics of Vol. No. 12 Cross Ref price and July 2020 | European Review of Agricultural Economics, Vol. No. 5 Cross Ref A of | Applied Vol. 27, No. Cross Ref Risk Estimation to and in November 2020 | Frontiers in Earth Science, Vol. 8 Cross Ref Learning to a A Transactions on Systems Technology, Vol. No. 6 Cross Ref for cell lung Analysis based on and Radiation Vol. Cross Ref Fast for the of the Kernel Density April 2021 | Optoelectronics, Instrumentation and Data Processing, Vol. No. 6 Cross Ref Using Deep Learning: With Systems and November 2020 | Journal of in Earth Systems, Vol. No. 11 Cross Ref search in the A Research Vol. Cross Ref A new algorithm based on and algorithm to the optimization Computing, Vol. Cross Ref of data methods and clustering model in the of of of Vol. No. 3 Cross Ref Energy Efficient in of Cross Ref Statistical of and for July 2020 | Journal International, Vol. No. 1 Cross Ref Unsupervised of Deep Bayesian Cross Ref for Visual of Data Cross Ref nonlinear of covariance Annals of Statistics, Vol. No. 5 Cross Ref modeling and prediction approach for using deep Journal of and Mass Vol. Cross Ref in model for May 2020 | Structural and Vol. No. 4 Cross Ref A Kernel Outlier August | Review, Vol. No. 5 Cross Ref emissions in China: spatial patterns and Research, Vol. 11, No. 9 Cross Ref A Analysis of the Effects of on the Performance of Transactions on Applications, Vol. No. 5 Cross Ref during A for the of based on September 2020 | Journal of and Management, Vol. No. 3 Cross Ref Dynamic risk assessment with network and clustering Engineering & System Safety, Vol. Cross Ref with exchange and Some from Modelling, Vol. Cross Ref A probabilistic verification application of random
PreviousNext No AccessSEG Technical Program Expanded Abstracts 1992Electronic documents give reproducible research a new meaningAuthors: Jon F. ClaerboutMartin KarrenbachJon F. ClaerboutStanford Univ. and Martin KarrenbachStanford Univ.https://doi.org/10.1190/1.1822162 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1822162FiguresReferencesRelatedDetailsCited ByPackaging research artefacts with RO-CrateData Science, Vol. 46Reproducibility in Evolutionary ComputationACM Transactions on Evolutionary Learning and Optimization, Vol. 1, No. 4Reproducible Research in R: A Tutorial on How to Do the Same Thing More Than Once9 December 2021 | Psych, Vol. 3, No. 4Preregistration in experimental linguistics: applications, challenges, and limitations24 March 2021 | Linguistics, Vol. 59, No. 5Practical Reproducibility in Geography and Geosciences13 October 2020 | Annals of the American Association of Geographers, Vol. 111, No. 5Tool-based Support for the FAIR Principles for Control Theoretic Results: The "Automatic Control Knowledge Repository"30 June 2021 | SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL, Vol. 1, No. 1Knowledge and Attitudes Among Life Scientists Toward Reproducibility Within Journal Articles: A Research Survey29 June 2021 | Frontiers in Research Metrics and Analytics, Vol. 6Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem21 May 2021 | Social Science Computer Review, Vol. 533The fundamental principles of reproducibility29 March 2021 | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 379, No. 2197Toward Long-Term and Archivable ReproducibilityComputing in Science & Engineering, Vol. 23, No. 3Principles for data analysis workflows18 March 2021 | PLOS Computational Biology, Vol. 17, No. 3Reproducible image handling and analysis22 January 2021 | The EMBO Journal, Vol. 40, No. 3A Reproducible Data Analysis Workflow11 May 2021 | Quantitative and Computational Methods in Behavioral Sciences, Vol. 1CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility30 March 2021 | F1000Research, Vol. 10CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility20 July 2021 | F1000Research, Vol. 10Reproducible Workflow8 January 2022Text as big data: Develop codes of practice for rigorous computational text analysis in energy social scienceEnergy Research & Social Science, Vol. 70"Automatic Control Knowledge Repository" – A Computational Approach for Simpler and More Robust Reproducibility of Results in Control TheoryA Survey on Collecting, Managing, and Analyzing Provenance from ScriptsACM Computing Surveys, Vol. 52, No. 3Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness20 March 2020 | BMJ, Vol. 1pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage24 January 2020 | PLOS ONE, Vol. 15, No. 1The State of Reproducible Research in Computer Science12 May 2020Reproducibility and the future of MRI research10 August 2019 | Magnetic Resonance in Medicine, Vol. 82, No. 6Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective14 August 2019 | Computational Brain & Behavior, Vol. 2, No. 3-4In-depth examination of spatiotemporal figures in open reproducible research19 October 2018 | Cartography and Geographic Information Science, Vol. 46, No. 5Application of BagIt-Serialized Research Object Bundles for Packaging and Re-Execution of Computational AnalysesReproducibility by Other Means: Transparent Research ObjectsOut-of-the-Box Reproducibility: A Survey of Machine Learning PlatformsRinse and Repeat: Understanding the Value of Replication across Different Ways of Knowing17 July 2019 | Publications, Vol. 7, No. 3trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R20 May 2019 | Journal of Computational and Graphical Statistics, Vol. 28, No. 3Reproducibility in Scientific ComputingACM Computing Surveys, Vol. 51, No. 3Computing environments for reproducibility: Capturing the "Whole Tale"Future Generation Computer Systems, Vol. 94Reproducible big data science: A case study in continuous FAIRness11 April 2019 | PLOS ONE, Vol. 14, No. 4Open access to research artifacts: Implementing the next generation data management plan18 October 2019 | Proceedings of the Association for Information Science and Technology, Vol. 56, No. 1TIRA Integrated Research Architecture14 August 2019Applicability Study of the PRIMAD Model to LIGO Gravitational Wave Search WorkflowsScientific Tests and Continuous Integration Strategies to Enhance Reproducibility in the Scientific Software ContextReproducibility Certification in Economics ResearchSSRN Electronic JournalPraxis of Reproducible Computational ScienceComputing in Science & Engineering, Vol. 21, No. 1Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge10 September 2019 | Socius: Sociological Research for a Dynamic World, Vol. 5Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail31 October 2018 | Journal of Computational Neuroscience, Vol. 45, No. 3Reproducible Research Using Biomodels6 September 2018 | Bulletin of Mathematical Biology, Vol. 80, No. 12Bridging the ChasmACM Computing Surveys, Vol. 50, No. 4Reproducibility vs. Replicability: A Brief History of a Confused Terminology18 January 2018 | Frontiers in Neuroinformatics, Vol. 11Implementation of a Next-Generation Course Architecture for Blended Learning14 September 2017Supporting Sustainable Process Documentation6 January 2018Fair Benchmarking Considered DifficultVerifiability in computer-aided research: the role of digital scientific notations at the human-computer interface23 July 2018 | PeerJ Computer Science, Vol. 4Provenance and Reproducibility7 December 2018A New Kind of Article for Reproducible Research in Intelligent Robotics [From the Field]IEEE Robotics & Automation Magazine, Vol. 24, No. 3Towards repeatability & verifiability in networking experiments: A stochastic frameworkJournal of Network and Computer Applications, Vol. 81Provenance and Reproducibility10 May 2017Reproducible Cartography31 May 2017Tools and techniques for computational reproducibility11 July 2016 | GigaScience, Vol. 5, No. 1Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance14 October 2016 | Earth and Space Science, Vol. 3, No. 10What does research reproducibility mean?Science Translational Medicine, Vol. 8, No. 341The Open Biodiversity Knowledge Management System in Scholarly Publishing11 January 2016 | Research Ideas and Outcomes, Vol. 2Scientific MisconductAnnual Review of Psychology, Vol. 67, No. 1Advantages and Limits in the Adoption of Reproducible Research and R-Tools for the Analysis of Omic Data31 July 2016Publishing the research process17 December 2015 | Research Ideas and Outcomes, Vol. 1Toward Replicable and Measurable Robotics Research [From the Guest Editors]IEEE Robotics & Automation Magazine, Vol. 22, No. 3MINRES-QLP Pack and Reliable Reproducible Research via Supportable Scientific SoftwareJournal of Open Research Software, Vol. 2, No. 1Embedded GeoComputation: Publishing Text, Data and Software in a Reproducible Form3 July 2014Spatial science – Looking outward25 March 2014 | Dialogues in Human Geography, Vol. 4, No. 1Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome27 November 2013 | PLoS ONE, Vol. 8, No. 11IPOL: Reviewed publication and public testing of research softwareMaking neurophysiological data analysis reproducible: Why and how?Journal of Physiology-Paris, Vol. 106, No. 3-4Supporting the internet-based evaluation of research software with cloud infrastructure30 May 2010 | Software & Systems Modeling, Vol. 11, No. 1A Universal Identifier for Computational ResultsProcedia Computer Science, Vol. 4Reproducible research in signal processingIEEE Signal Processing Magazine, Vol. 26, No. 3Notice of Violation of IEEE Publication Principles - Reproducible research in various facets of signal processingExperiences with Reproducible Research in Various Facets of Signal Processing ResearchReproducible Computational Experiments using SconsEmbedding formal knowledge models in active documentsCommunications of the ACM, Vol. 42, No. 1 SEG Technical Program Expanded Abstracts 1992ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 1992 Pages: 1410 publication data© 1992 Copyright © 1992 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 10 Feb 2005 CITATION INFORMATION Jon F. Claerbout and Martin Karrenbach, (1992), "Electronic documents give reproducible research a new meaning," SEG Technical Program Expanded Abstracts : 601-604. https://doi.org/10.1190/1.1822162 Plain-Language Summary PDF DownloadLoading ...
Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from non-invasive neuroimaging techniques such as functional magnetic resonance imaging (fMRI). An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior.
The main applications of the Brain-Computer Interface (BCI) have been in the domain of rehabilitation, control of prosthetics, and in neuro-feedback. Only a few clinical applications presently exist for the management of drug-resistant epilepsy. Epilepsy surgery can be a life-changing procedure in the subset of millions of patients who are medically intractable. Recording of seizures and localization of the Seizure Onset Zone (SOZ) in the subgroup of "surgical" patients, who require intracranial-EEG (icEEG) evaluations, remain to date the best available surrogate marker of the epileptogenic tissue. icEEG presents certain risks and challenges making it a frontier that will benefit from optimization. Despite the presentation of several novel biomarkers for the localization of epileptic brain regions (HFOs-spikes vs. Spikes for instance), integration of most in practices is not at the prime time as it requires a degree of knowledge about signal and computation. The clinical care remains inspired by the original practices of recording the seizures and expert visual analysis of rhythms at onset. It is becoming increasingly evident, however, that there is more to infer from the large amount of EEG data sampled at rates in the order of less than 1 ms and collected over several days of invasive EEG recordings than commonly done in practice. This opens the door for interesting areas at the intersection of neuroscience, computation, engineering and clinical care. Brain-Computer interface (BCI) has the potential of enabling the processing of a large amount of data in a short period of time and providing insights that are not possible otherwise by human expert readers. Our practices suggest that implementation of BCI and Real-Time processing of EEG data is possible and suitable for most standard clinical applications, in fact, often the performance is comparable to a highly qualified human readers with the advantage of producing the results in real-time reliably and tirelessly. This is of utmost importance in specific environments such as in the operating room (OR) among other applications. In this review, we will present the readers with potential targets for BCI in caring for patients with surgical epilepsy.
Frontiers in Human Neuroscience is a first-tier electronic journal devoted to understanding the brain mechanisms supporting cognitive and social behavior in humans, and how these mechanisms might be altered in disease states. The last 25 years have seen an explosive growth in both the methods and the theoretical constructs available to study the human brain. Advances in electrophysiological, neuroimaging, neuropsychological, psychophysical, neuropharmacological and computational approaches have provided key insights into the mechanisms of a broad range of human behaviors in both health and disease. Work in human neuroscience ranges from the cognitive domain, including areas such as memory, attention, language and perception to the social domain, with this last subject addressing topics, such as interpersonal interactions, social discourse and emotional regulation. How these processes unfold during development, mature in adulthood and often decline in aging, and how they are altered in a host of developmental, neurological and psychiatric disorders, has become increasingly amenable to human neuroscience research approaches. Work in human neuroscience has influenced many areas of inquiry ranging from social and cognitive psychology to economics, law and public policy. Accordingly, our journal will provide a forum for human research spanning all areas of human cognitive, social, developmental and translational neuroscience using any research approach.
Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistics and the empirical neural data of neuroscience. Serving as an interdisciplinary cornerstone, computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models through modeling, simulation, and data analysis. This enables a computational dialogue between linguistic hypotheses and neural mechanisms. Recent advances in deep learning, particularly large language models (LLMs), have powerfully advanced this pursuit. Their high-dimensional representational spaces provide a novel scale for exploring the neural basis of linguistic processing, while the "model-brain alignment" framework offers a methodology to evaluate the biological plausibility of language-related theories.
To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models of human cognition, decomposing task performance into computational components. However, its algorithms still fall short of human intelligence and are not grounded in neurobiology. Computational neuroscience has investigated how interacting neurons can implement component functions of brain computation. However, it has yet to explain how those components interact to explain human cognition and behavior. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. It is time to assemble the pieces of the puzzle of brain computation. Here we review recent work in the intersection of cognitive science, computational neuroscience, and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive, and control tasks are beginning to be deve
This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity, and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and
Little is known about the integration of neural mechanisms for balance and locomotion. Muscle synergies have been studied independently in standing balance and walking, but not compared. Here, we hypothesized that reactive balance and walking are mediated by a common set of lower-limb muscle synergies. In humans, we examined muscle activity during multidirectional support-surface perturbations during standing and walking, as well as unperturbed walking at two speeds. We show that most muscle synergies used in perturbations responses during standing were also used in perturbation responses during walking, suggesting common neural mechanisms for reactive balance across different contexts. We also show that most muscle synergies using in reactive balance were also used during unperturbed walking, suggesting that neural circuits mediating locomotion and reactive balance recruit a common set of muscle synergies to achieve task-level goals. Differences in muscle synergies across conditions reflected differences in the biomechanical demands of the tasks. For example, muscle synergies specific to walking perturbations may reflect biomechanical challenges associated with single limb stance, and muscle synergies used during sagittal balance recovery in standing but not walking were consistent with maintaining the different desired center of mass motions in standing vs. walking. Thus, muscle synergies specifying spatial organization of muscle activation patterns may define a repertoire of biomechanical subtasks available to different neural circuits governing walking and reactive balance and may be recruited based on task-level goals. Muscle synergy analysis may aid in dissociating deficits in spatial vs. temporal organization of muscle activity in motor deficits. Muscle synergy analysis may also provide a more generalizable assessment of motor function by identifying whether common modular mechanisms are impaired across the performance of multiple motor tasks.
HIGHLIGHTS: ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. OBJECTIVE: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents. METHODS: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. RESULTS: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R (2)) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation efficiency compared with MDFA. CONCLUSION: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.
Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT.
Two transformative waves of computing have redefined the way we approach science. The first wave came with the birth of the digital computer, which enabled scientists to numerically simulate their models and analyze massive datasets. This technological breakthrough led to the emergence of many sub-disciplines bearing the prefix "computational" in their names. Currently, we are in the midst of the second wave, marked by the remarkable advancements in artificial intelligence. From predicting protein structures to classifying galaxies, the scope of its applications is vast, and there can only be more awaiting us on the horizon. While these two waves influence scientific methodology at the instrumental level, in this dissertation, I will present the computational lens in science, aiming at the conceptual level. Specifically, the central thesis posits that computation serves as a convenient and mechanistic language for understanding and analyzing information processing systems, offering the advantages of composability and modularity. This dissertation begins with an illustration of the blueprint of the computational lens, supported by a review of relevant previous work. Subsequently, I
A FUNDAMENTAL CHALLENGE FOR SYSTEMS NEUROSCIENCE IS TO QUANTITATIVELY RELATE ITS THREE MAJOR BRANCHES OF RESEARCH: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience.
This textbook is an introduction to Systems and Theoretical/Computational Neuroscience, with a particular emphasis on cognition. It consists of three parts: Part I covers fundamental concepts and mathematical models in computational neuroscience, along with cutting-edge topics. Part II explores the building blocks of cognition, including working memory (how the brain maintains and manipulates information "online" without external input), decision making (how choices are made among multiple options under conditions of uncertainty and risk) and behavioral flexibility (how we direct attention and control actions). Part III is dedicated to frontier research, covering models of large-scale multi-regional brain systems, Computational Psychiatry and the interface with Artificial Intelligence. The author highlights the perspective of neural circuits as dynamical systems, and emphasizes a cross-level mechanistic understanding of the brain and mind, from genes and cell types to collective neural populations and behavior. Overall, this textbook provides an opportunity for readers to become well versed in this highly interdisciplinary field of the twenty-first century. Key Features Rooted in the most recent advances in experimental studies of basic cognitive functions Introduces neurobiological and mathematical concepts so that the book is self-contained Heavily illustrated with high-quality figures that help to illuminate neurobiological concepts, present experimental findings and explain mathematical models Concludes with a list of core cognitive behavior tasks, ten take-home messages and three open questions for future research Computer model codes are available via GitHub for hands-on practice
Within computational neuroscience, informal interactions with modelers often reveal wildly divergent goals. In this opinion piece, we explicitly address the diversity of goals that motivate and ultimately influence modeling efforts. We argue that a wide range of goals can be meaningfully taken to be of highest importance. A simple informal survey conducted on the Internet confirmed the diversity of goals in the community. However, different priorities or preferences of individual researchers can lead to divergent model evaluation criteria. We propose that many disagreements in evaluating the merit of computational research stem from differences in goals and not from the mechanics of constructing, describing, and validating models. We suggest that authors state explicitly their goals when proposing models so that others can judge the quality of the research with respect to its stated goals.
Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.
The proliferation and refinement of affordable virtual reality (VR) technologies and wearable sensors have opened new frontiers in cognitive and behavioral neuroscience. This chapter offers a broad overview of VR for anyone interested in leveraging it as a research tool. In the first section, it examines the fundamental functionalities of VR and outlines important considerations that inform the development of immersive content that stimulates the senses. In the second section, the focus of the discussion shifts to the implementation of VR in the context of the neuroscience lab. Practical advice is offered on adapting commercial, off-theshelf devices to specific research purposes. Further, methods are explored for recording, synchronizing, and fusing heterogeneous forms of data obtained through the VR system or add-on sensors, as well as for labeling events and capturing game play.