Revealing nonlinear neural decoding by analyzing choices
暂无分享,去创建一个
Xaq Pitkow | Andreas S. Tolias | Qianli Yang | Edgar Walker | R. James Cotton | Edgar Y. Walker | A. Tolias | Xaq Pitkow | R. J. Cotton | Qianli Yang | R. Cotton
[1] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[2] Stefan Treue,et al. Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.
[3] Haim Sompolinsky,et al. Implications of Neuronal Diversity on Population Coding , 2006, Neural Computation.
[4] John H R Maunsell,et al. Potential confounds in estimating trial-to-trial correlations between neuronal response and behavior using choice probabilities. , 2012, Journal of neurophysiology.
[5] Alexandre Pouget,et al. Measuring Fisher Information Accurately in Correlated Neural Populations , 2015, PLoS Comput. Biol..
[6] A. Pouget,et al. Information-limiting correlations , 2014, Nature Neuroscience.
[7] W. Newsome,et al. Estimates of the Contribution of Single Neurons to Perception Depend on Timescale and Noise Correlation , 2009, The Journal of Neuroscience.
[8] Matthias Bethge,et al. Evaluating neuronal codes for inference using Fisher information , 2010, NIPS.
[9] Bruce G. Cumming,et al. Feedback Dynamics Determine the Structure of Spike-Count Correlation in Visual Cortex , 2016 .
[10] J. Maunsell,et al. Attention improves performance primarily by reducing interneuronal correlations , 2009, Nature Neuroscience.
[11] Ehud Zohary,et al. Correlated neuronal discharge rate and its implications for psychophysical performance , 1994, Nature.
[12] T. Poggio,et al. Synapses that compute motion. , 1987, Scientific American.
[13] Ifije E. Ohiorhenuan,et al. Sparse coding and high-order correlations in fine-scale cortical networks , 2010, Nature.
[14] G. DeAngelis,et al. Neural correlates of multisensory cue integration in macaque MSTd , 2008, Nature Neuroscience.
[15] A. Parker,et al. Perceptually Bistable Three-Dimensional Figures Evoke High Choice Probabilities in Cortical Area MT , 2001, The Journal of Neuroscience.
[16] Daeyeol Lee,et al. Effects of noise correlations on information encoding and decoding. , 2006, Journal of neurophysiology.
[17] Anthony J. Movshon,et al. Optimal representation of sensory information by neural populations , 2006, Nature Neuroscience.
[18] Alexander S. Ecker,et al. Decorrelated Neuronal Firing in Cortical Microcircuits , 2010, Science.
[19] C. Salzman,et al. Abstract Context Representations in Primate Amygdala and Prefrontal Cortex , 2015, Neuron.
[20] Peter Dayan,et al. The Effect of Correlated Variability on the Accuracy of a Population Code , 1999, Neural Computation.
[21] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[22] Doris Y. Tsao,et al. Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System , 2015, The Journal of Neuroscience.
[23] Alexander S. Ecker,et al. State Dependence of Noise Correlations in Macaque Primary Visual Cortex , 2014, Neuron.
[24] M. Shadlen,et al. Response of Neurons in the Lateral Intraparietal Area during a Combined Visual Discrimination Reaction Time Task , 2002, The Journal of Neuroscience.
[25] Alexander S. Ecker,et al. Reassessing optimal neural population codes with neurometric functions , 2011, Proceedings of the National Academy of Sciences.
[26] Gustavo Deco,et al. Stimulus-dependent variability and noise correlations in cortical MT neurons , 2013, Proceedings of the National Academy of Sciences.
[27] Alexander S. Ecker,et al. Attentional fluctuations induce shared variability in macaque primary visual cortex , 2017, Nature Communications.
[28] Xaq Pitkow,et al. Inference in the Brain: Statistics Flowing in Redundant Population Codes , 2017, Neuron.
[29] A. Tolias,et al. Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization , 2013, Proceedings of the National Academy of Sciences.
[30] Matthias Bethge,et al. Optimal Short-Term Population Coding: When Fisher Information Fails , 2002, Neural Computation.
[31] Arnulf B. A. Graf,et al. Decoding the activity of neuronal populations in macaque primary visual cortex , 2011, Nature Neuroscience.
[32] Richard D. Lange,et al. Inferring the brain’s internal model from sensory responses in a probabilistic inference framework , 2016, bioRxiv.
[33] A. Pouget,et al. Correlations and Neuronal Population Information. , 2016, Annual review of neuroscience.
[34] Valentin Dragoi,et al. Adaptive coding of visual information in neural populations , 2008, Nature.
[35] W. Newsome,et al. Context-Dependent Changes in Functional Circuitry in Visual Area MT , 2008, Neuron.
[36] R. Romo,et al. Neuronal correlates of subjective sensory experience , 2005, Nature Neuroscience.
[37] D. M. Green,et al. Signal detection theory and psychophysics , 1966 .
[38] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[39] Haim Sompolinsky,et al. Nonlinear Population Codes , 2004, Neural Computation.
[40] A. Pouget,et al. Behavior and neural basis of near-optimal visual search , 2011, Nature Neuroscience.
[41] Nicole C Rust,et al. Dynamic Target Match Signals in Perirhinal Cortex Can Be Explained by Instantaneous Computations That Act on Dynamic Input from Inferotemporal Cortex , 2014, The Journal of Neuroscience.
[42] David D. Cox,et al. Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .
[43] D. Bradley,et al. Neural population code for fine perceptual decisions in area MT , 2005, Nature Neuroscience.
[44] Alexandre Pouget,et al. Origin of information-limiting noise correlations , 2015, Proceedings of the National Academy of Sciences.
[45] Alexandre Pouget,et al. Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons , 2011, Neural Computation.
[46] B. Cumming,et al. Psychophysically measured task strategy for disparity discrimination is reflected in V2 neurons , 2007, Nature Neuroscience.
[47] Nikolaus Kriegeskorte,et al. Recurrence is required to capture the representational dynamics of the human visual system , 2019, Proceedings of the National Academy of Sciences.
[48] A. Pouget,et al. Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability , 2012, Neuron.
[49] Nicole C. Rust,et al. Signals in inferotemporal and perirhinal cortex suggest an “untangling” of visual target information , 2013, Nature Neuroscience.
[50] H. Sompolinsky,et al. Sparseness and Expansion in Sensory Representations , 2014, Neuron.
[51] G. DeAngelis,et al. How Can Single Sensory Neurons Predict Behavior? , 2015, Neuron.
[52] K. A. Davis,et al. Auditory Processing of Spectral Cues for Sound Localization in the Inferior Colliculus , 2003, Journal of the Association for Research in Otolaryngology.
[53] Wei Ji Ma,et al. Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.
[54] Eero P. Simoncelli,et al. Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation , 2016, Neural Computation.
[55] M. Bethge,et al. Inferring decoding strategies from choice probabilities in the presence of correlated variability , 2013, Nature Neuroscience.
[56] József Fiser,et al. Perceptual Decision-Making as Probabilistic Inference by Neural Sampling , 2014, Neuron.
[57] M. A. Smith,et al. Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque , 2005, The Journal of Neuroscience.
[58] Ian Krajbich,et al. Visual fixations and the computation and comparison of value in simple choice , 2010, Nature Neuroscience.
[59] Nicole C. Rust,et al. Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT , 2010, The Journal of Neuroscience.
[60] Alexander S. Ecker,et al. On the Structure of Neuronal Population Activity under Fluctuations in Attentional State , 2015, The Journal of Neuroscience.
[61] Alexandre Pouget,et al. Inferring decoding strategies for multiple correlated neural populations , 2017, bioRxiv.
[62] H. Sompolinsky,et al. Population coding in neuronal systems with correlated noise. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[63] E H Adelson,et al. Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.
[64] J. Donoghue,et al. Neuronal Interactions Improve Cortical Population Coding of Movement Direction , 1999, The Journal of Neuroscience.
[65] A. Pouget,et al. Neural correlations, population coding and computation , 2006, Nature Reviews Neuroscience.
[66] TJ Gawne,et al. How independent are the messages carried by adjacent inferior temporal cortical neurons? , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[67] K. H. Britten,et al. A relationship between behavioral choice and the visual responses of neurons in macaque MT , 1996, Visual Neuroscience.
[68] Wei Ji Ma,et al. A neural basis of probabilistic computation in visual cortex , 2018, Nature Neuroscience.
[69] Alexander S. Ecker,et al. Recording chronically from the same neurons in awake, behaving primates. , 2007, Journal of neurophysiology.
[70] M. Cohen,et al. Measuring and interpreting neuronal correlations , 2011, Nature Neuroscience.
[71] Bruce G. Cumming,et al. Feedback Determines the Structure of Correlated Variability in Visual Cortex , 2016 .
[72] Johannes Burge,et al. Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise , 2017, PLoS Comput. Biol..
[73] J. Movshon,et al. A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[74] Alexander S. Ecker,et al. The Effect of Noise Correlations in Populations of Diversely Tuned Neurons , 2011, The Journal of Neuroscience.
[75] Xiao-Jing Wang,et al. The importance of mixed selectivity in complex cognitive tasks , 2013, Nature.
[76] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[77] M. Paradiso,et al. A theory for the use of visual orientation information which exploits the columnar structure of striate cortex , 2004, Biological Cybernetics.