Neural Network Poisson Models for Behavioural and Neural Spike Train Data
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Richard Nock | Peter Dayan | Ehsan Arabzadeh | Amir Dezfouli | P. Dayan | R. Nock | E. Arabzadeh | A. Dezfouli | M. Khajehnejad | Forough Habibollahi
[1] K. Naka,et al. White-Noise Analysis of a Neuron Chain: An Application of the Wiener Theory , 1972, Science.
[2] R. Cowan. An introduction to the theory of point processes , 1978 .
[3] William Bialek,et al. Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[4] Joseph Sill,et al. Monotonic Networks , 1997, NIPS.
[5] E J Chichilnisky,et al. A simple white noise analysis of neuronal light responses , 2001, Network.
[6] L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models , 2004, Network.
[7] Byron M. Yu,et al. Extracting Dynamical Structure Embedded in Neural Activity , 2005, NIPS.
[8] Uri T Eden,et al. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.
[9] Ali Esmaili,et al. Probability and Random Processes , 2005, Technometrics.
[10] Liam Paninski,et al. Statistical models for neural encoding, decoding, and optimal stimulus design. , 2007, Progress in brain research.
[11] Eero P. Simoncelli,et al. Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.
[12] Andrew M. Clark,et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon , 2010, Nature Neuroscience.
[13] B. Balleine,et al. Human and Rodent Homologies in Action Control: Corticostriatal Determinants of Goal-Directed and Habitual Action , 2010, Neuropsychopharmacology.
[14] H. Sompolinsky,et al. Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. , 2012, Annual review of neuroscience.
[15] M. Sahani,et al. Cortical control of arm movements: a dynamical systems perspective. , 2013, Annual review of neuroscience.
[16] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[17] Emery N. Brown,et al. Analysis of Neural Data , 2014 .
[18] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[19] Eero P. Simoncelli,et al. Partitioning neuronal variability , 2014, Nature Neuroscience.
[20] David Sussillo,et al. Neural circuits as computational dynamical systems , 2014, Current Opinion in Neurobiology.
[21] Byron M. Yu,et al. Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.
[22] Albert Compte,et al. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT , 2015, Nature Communications.
[23] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[24] A. Pouget,et al. Correlations and Neuronal Population Information. , 2016, Annual review of neuroscience.
[25] Lei Shi,et al. Understand scene categories by objects: A semantic regularized scene classifier using Convolutional Neural Networks , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[26] Naoshige Uchida,et al. Demixed principal component analysis of neural population data , 2016, eLife.
[27] Adrienne L. Fairhall,et al. Analysis of Neuronal Spike Trains, Deconstructed , 2016, Neuron.
[28] Maneesh Sahani,et al. Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation , 2017, Front. Syst. Neurosci..
[29] Iasonas Kokkinos,et al. UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Sergey L. Gratiy,et al. Fully integrated silicon probes for high-density recording of neural activity , 2017, Nature.
[31] Peter Dayan,et al. Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models , 2018, bioRxiv.
[32] Chethan Pandarinath,et al. Inferring single-trial neural population dynamics using sequential auto-encoders , 2017, Nature Methods.
[33] Nicholas A. Steinmetz,et al. Distributed coding of choice, action, and engagement across the mouse brain , 2019, Nature.
[34] Surya Ganguli,et al. A deep learning framework for neuroscience , 2019, Nature Neuroscience.
[35] Andrew B Schwartz,et al. Distributed processing of movement signaling , 2019, Proceedings of the National Academy of Sciences.
[36] Fully Neural Network based Model for General Temporal Point Processes , 2019, NeurIPS.
[37] Ricardo Silva,et al. Neural Likelihoods via Cumulative Distribution Functions , 2018, UAI.