Big data and the industrialization of neuroscience: A safe roadmap for understanding the brain?
暂无分享,去创建一个
[1] Moritz Helmstaedter,et al. The Mutual Inspirations of Machine Learning and Neuroscience , 2015, Neuron.
[2] Y. Frégnac,et al. Visual input evokes transient and strong shunting inhibition in visual cortical neurons , 1998, Nature.
[3] Stefan Theil. Trouble in Mind , 2015 .
[4] C. von der Malsburg,et al. Am I Thinking Assemblies , 1986 .
[5] Gilles Laurent,et al. Transient Dynamics for Neural Processing , 2008, Science.
[6] F. Collins,et al. The Human Genome Project: Lessons from Large-Scale Biology , 2003, Science.
[7] Adam R Ferguson,et al. Big data from small data: data-sharing in the 'long tail' of neuroscience , 2014, Nature Neuroscience.
[8] E. Kandel,et al. Neuroscience thinks big (and collaboratively) , 2013, Nature Reviews Neuroscience.
[9] H. Markram. The Blue Brain Project , 2006, Nature Reviews Neuroscience.
[10] Lorenz Pammer,et al. Comparative approaches to cortical microcircuits , 2016, Current Opinion in Neurobiology.
[11] E. Marder,et al. From the connectome to brain function , 2013, Nature Methods.
[12] J. Touryan,et al. Spatial Structure of Complex Cell Receptive Fields Measured with Natural Images , 2005, Neuron.
[13] D. McCormick,et al. Neural control of brain state , 2014, Current Opinion in Neurobiology.
[14] P M MILNER,et al. The cell assembly: Mark II. , 1957, Psychological review.
[15] Joseph J. Paton,et al. Big behavioral data: psychology, ethology and the foundations of neuroscience , 2014, Nature Neuroscience.
[16] Bruno A. Olshausen,et al. Book Review , 2003, Journal of Cognitive Neuroscience.
[17] Konrad Paul Kording,et al. Could a Neuroscientist Understand a Microprocessor? , 2016, bioRxiv.
[18] N. Logothetis,et al. Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.
[19] L. Luo,et al. It takes the world to understand the brain , 2015, Science.
[20] L. Cooper,et al. A theory for the development of feature detecting cells in visual cortex , 1975, Biological Cybernetics.
[21] D. Knill,et al. The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.
[22] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[23] David Cyranoski,et al. Marmosets are stars of Japan’s ambitious brain project , 2014, Nature.
[24] David J. Anderson,et al. Toward a Science of Computational Ethology , 2014, Neuron.
[25] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[26] Florian Engert,et al. Large-scale imaging in small brains , 2015, Current Opinion in Neurobiology.
[27] R. Yuste,et al. The Brain Activity Map Project and the Challenge of Functional Connectomics , 2012, Neuron.
[28] R Linsker,et al. From basic network principles to neural architecture: emergence of spatial-opponent cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.
[29] J L Gallant,et al. Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.
[30] Trevor Bekolay,et al. A Large-Scale Model of the Functioning Brain , 2012, Science.
[31] Emery N. Brown,et al. The BRAIN Initiative: developing technology to catalyse neuroscience discovery , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.
[32] P. Drew,et al. Neurovascular Coupling and Decoupling in the Cortex during Voluntary Locomotion , 2014, The Journal of Neuroscience.
[33] Esther Landhuis,et al. Neuroscience: Big brain, big data , 2017, Nature.
[34] P. Anderson. More is different. , 1972, Science.
[35] J. Haynes. A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.
[36] Israel Nelken,et al. Local versus global scales of organization in auditory cortex , 2014, Trends in Neurosciences.
[37] Anders Lansner,et al. Biophysically detailed modelling of microcircuits and beyond , 2005, Trends in Neurosciences.
[38] R Linsker,et al. From basic network principles to neural architecture: emergence of orientation-selective cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.
[39] Karl J. Friston,et al. The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..
[40] Michelle L. McGowan,et al. Big data, open science and the brain: lessons learned from genomics , 2014, Front. Hum. Neurosci..
[41] Michael Häusser,et al. Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo , 2014, Nature Methods.
[42] Barbara Lom,et al. Looking Inside the Brain: The Power of Neuroimaging. , 2015 .
[43] Ulrike Felt,et al. Taking European Knowledge Society Seriously , 2009 .
[44] Yves Frégnac,et al. Neuroscience: Where is the brain in the Human Brain Project? , 2014, Nature.
[45] Denis Noble,et al. A theory of biological relativity: no privileged level of causation , 2012, Interface Focus.
[46] R. Reid,et al. Specificity and randomness in the visual cortex , 2007, Current Opinion in Neurobiology.
[47] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[48] Chaim Zins,et al. Conceptual approaches for defining data, information, and knowledge , 2007, J. Assoc. Inf. Sci. Technol..
[49] David S. Greenberg,et al. Rats maintain an overhead binocular field at the expense of constant fusion , 2013, Nature.
[50] Meredith Wadman,et al. Behind the scenes of a brain-mapping moon shot , 2013, Nature.
[51] Nikos K. Logothetis,et al. fMRI at High Spatial Resolution: Implications for BOLD-Models , 2016, Front. Comput. Neurosci..
[52] Kelly Rae Chi. Neural modelling: Abstractions of the mind , 2016, Nature.
[53] Silvia Arber. BRAIN Initiative and Human Brain Project: Hopes and Reservations , 2013, Cell.
[54] Ling Wang,et al. Mu-ming Poo: China Brain Project and the future of Chinese neuroscience , 2017 .
[55] E. Marder,et al. Similar network activity from disparate circuit parameters , 2004, Nature Neuroscience.
[56] Nikos K Logothetis,et al. Interpreting the BOLD signal. , 2004, Annual review of physiology.
[57] K. Deisseroth,et al. Engineering Approaches to Illuminating Brain Structure and Dynamics , 2013, Neuron.
[58] J Anthony Movshon,et al. Putting big data to good use in neuroscience , 2014, Nature Neuroscience.
[59] Gerald M Edelman,et al. Learning in and from Brain-Based Devices , 2007, Science.
[60] Yves Frégnac,et al. Hidden Complexity of Synaptic Receptive Fields in Cat V1 , 2014, The Journal of Neuroscience.
[61] G. Rees,et al. Neuroimaging: Decoding mental states from brain activity in humans , 2006, Nature Reviews Neuroscience.
[62] Christof Koch,et al. Neuroscience: Observatories of the mind , 2012, Nature.
[63] Cori Bargmann,et al. High-throughput imaging of neuronal activity in Caenorhabditis elegans , 2013, Proceedings of the National Academy of Sciences.
[64] M. Schölvinck,et al. Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.
[65] S. Dehaene,et al. Characterizing the dynamics of mental representations: the temporal generalization method , 2014, Trends in Cognitive Sciences.
[66] M. Moulins,et al. Construction of a pattern-generating circuit with neurons of different networks , 1991, Nature.
[67] K. Deisseroth,et al. Millisecond-timescale, genetically targeted optical control of neural activity , 2005, Nature Neuroscience.
[68] James G. King,et al. Reconstruction and Simulation of Neocortical Microcircuitry , 2015, Cell.
[69] Henry Markram,et al. Seven challenges for neuroscience. , 2013, Functional neurology.
[70] M. Stryker,et al. Modulation of Visual Responses by Behavioral State in Mouse Visual Cortex , 2010, Neuron.
[71] Yves Frégnac,et al. Cortical Correlates of Low-Level Perception: From Neural Circuits to Percepts , 2015, Neuron.
[72] Daniel L. K. Yamins,et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..
[73] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[74] Alexandre Pouget,et al. A better way to crack the brain , 2016, Nature.
[75] Hans Knutsson,et al. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.
[76] Cori Bargmann. Beyond the connectome: How neuromodulators shape neural circuits , 2012, BioEssays : news and reviews in molecular, cellular and developmental biology.
[77] G. Edelman,et al. Neural dynamics in a model of the thalamocortical system. II. The role of neural synchrony tested through perturbations of spike timing. , 1997, Cerebral cortex.
[78] H. Barlow. Summation and inhibition in the frog's retina , 1953, The Journal of physiology.
[79] Noah D. Brenowitz,et al. Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis , 2012, Proceedings of the National Academy of Sciences.
[80] Kelly Rae Chi,et al. The dark side of the human genome , 2016, Nature.
[81] Marie-Eve Laramée,et al. Visual cortical areas of the mouse: comparison of parcellation and network structure with primates , 2015, Front. Neural Circuits.
[82] Henry Markram,et al. The human brain project. , 2012, Scientific American.
[83] Leslie Roberts. Genome backlash going full force. , 1990, Science.
[84] Byron M. Yu,et al. Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.
[85] Rodney J. Douglas,et al. Behavioral architecture of the cortical sheet , 2012, Current Biology.
[86] P. Wolynes,et al. The middle way. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[87] W. Singer. Synchronization of cortical activity and its putative role in information processing and learning. , 1993, Annual review of physiology.
[88] E. P. Gardner,et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex , 2008, Nature Reviews Neuroscience.
[89] E. Marder,et al. Variability, compensation and homeostasis in neuron and network function , 2006, Nature Reviews Neuroscience.
[90] Nicholas Cain,et al. Inferring cortical function in the mouse visual system through large-scale systems neuroscience , 2016, Proceedings of the National Academy of Sciences.
[91] Byron M. Yu,et al. Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex , 2016, PLoS Comput. Biol..
[92] M. Baker. Neuroscience: Through the eyes of a mouse , 2013, Nature.
[93] Tobias Bonhoeffer,et al. Two-photon calcium imaging in mice navigating a virtual reality environment. , 2014, Journal of visualized experiments : JoVE.
[94] Yevgeniy B. Sirotin,et al. Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. , 2009, Nature.
[95] John Bickle,et al. Marr and Reductionism , 2015, Top. Cogn. Sci..
[96] Y. Frégnac,et al. A cellular analogue of visual cortical plasticity , 1988, Nature.
[97] Olaf Sporns,et al. Mapping the Connectome: Multi-Level Analysis of Brain Connectivity , 2012, Front. Neuroinform..
[98] T. Sejnowski,et al. Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.
[99] R. Kerr,et al. Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning , 2014, Science.
[100] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[101] G. Edelman,et al. Large-scale model of mammalian thalamocortical systems , 2008, Proceedings of the National Academy of Sciences.
[102] J. Fournier,et al. Looking for the roots of cortical sensory computation in three-layered cortices , 2015, Current Opinion in Neurobiology.
[103] M. A. MacIver,et al. Neuroscience Needs Behavior: Correcting a Reductionist Bias , 2017, Neuron.
[104] Yves Frégnac,et al. Adaptation of the simple or complex nature of V1 receptive fields to visual statistics , 2011, Nature Neuroscience.
[105] Spencer L. Smith,et al. Parallel processing of visual space by neighboring neurons in mouse visual cortex , 2010, Nature Neuroscience.
[106] Arie Rip,et al. TAKING EUROPEAN KNOWLEDGE SOCIETY SERIOUSLY Report of the Expert Group on Science and Governance to the Science, Economy and Society Directorate, Directorate-General for Research, European Commission , 2007 .
[107] Y. Frégnac,et al. In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices , 2008, Journal of Neuroscience Methods.
[108] Victor Ya. Frenkel. Yakov Ilich Frenkel , 1996 .
[109] Thomas L. Griffiths,et al. Supplementary Information for Natural Speech Reveals the Semantic Maps That Tile Human Cerebral Cortex , 2022 .
[110] M. Carandini. From circuits to behavior: a bridge too far? , 2012, Nature Neuroscience.
[111] R. Turner,et al. Deficient approaches to human neuroimaging , 2014, Front. Hum. Neurosci..
[112] M. Häusser,et al. All-Optical Interrogation of Neural Circuits , 2015, The Journal of Neuroscience.
[113] M. Orger,et al. Whole-Brain Activity Maps Reveal Stereotyped, Distributed Networks for Visuomotor Behavior , 2014, Neuron.
[114] Eero P. Simoncelli,et al. Spatiotemporal Elements of Macaque V1 Receptive Fields , 2005, Neuron.
[115] R Linsker,et al. From basic network principles to neural architecture: emergence of orientation columns. , 1986, Proceedings of the National Academy of Sciences of the United States of America.
[116] Christof Koch,et al. Worldwide initiatives to advance brain research , 2016, Nature Neuroscience.
[117] Florian Engert. The Big Data Problem: Turning Maps into Knowledge , 2014, Neuron.
[118] E. Capaldi,et al. The organization of behavior. , 1992, Journal of applied behavior analysis.
[119] J. Bower. 20 Years of Computational Neuroscience , 2013, Springer Series in Computational Neuroscience.
[120] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[121] C. Eliasmith,et al. The use and abuse of large-scale brain models , 2014, Current Opinion in Neurobiology.
[122] David A. Leopold,et al. The marmoset monkey as a model for visual neuroscience , 2015, Neuroscience Research.
[123] Concha Bielza,et al. New insights into the classification and nomenclature of cortical GABAergic interneurons , 2013, Nature Reviews Neuroscience.
[124] J. Changeux,et al. Experimental and Theoretical Approaches to Conscious Processing , 2011, Neuron.
[125] Yves Frégnac,et al. Animation of natural scene by virtual eye-movements evokes high precision and low noise in V1 neurons , 2013, Front. Neural Circuits.
[126] Richard Jones. The economy of promises. , 2008, Nature nanotechnology.
[127] Stephen M. Smith,et al. Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data , 2010, Front. Syst. Neurosci..
[128] Richard P. Cooper,et al. Beyond Single-Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation , 2015, Top. Cogn. Sci..
[129] James M. Bower,et al. The Book of GENESIS , 1994, Springer New York.
[130] R. chef. Sciences et technologies émergentes : Pourquoi tant de promeses ? Dirigé par Marc Audetat - Société d'Anthropologie des Connaissances , 2015 .
[131] Michael M Yartsev,et al. The emperor’s new wardrobe: Rebalancing diversity of animal models in neuroscience research , 2017, Science.