An introduction to reinforcement learning for neuroscience
Kristopher T. Jensen
arXiv (2023)
Publications
Strong and weak principles of Bayesian machine learning for systems neuroscience
Kristopher T. Jensen
University of Cambridge (2023)
A recurrent network model of planning explains hippocampal replay and human behavior
Kristopher T. Jensen, Guillaume Hennequin*, Marcelo Mattar*
bioRxiv (2023)
Long-term stability of single neuron activity in the motor system
Kristopher T. Jensen, Naama Kadmon Harpaz, Ashesh K. Dhawale, Steffen B. E. Wolff, Bence P. Ölveczky
Nature neuroscience (2022)
Natural continual learning: success is a journey, not (just) a destination
Ta-Chu Kao*, Kristopher T. Jensen*, Alberto Bernacchia, Guillaume Hennequin
Advances in Neural Information Processing Systems (2021)
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Kristopher T. Jensen*, Ta-Chu Kao*, Jasmine T. Stone, Guillaume Hennequin
Advances in Neural Information Processing Systems (2021)
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin
Advances in Neural Information Processing Systems (2020)
The neuroanatomical ultrastructure and function of a biological ring attractor
Daniel B. Turner-Evans, Kristopher T. Jensen*, Saba Ali*, Tyler Paterson*, Arlo Sheridan*, Robert P. Ray, Tanya Wolff, Scott Lauritzen, Gerald M. Rubin, Davi Bock, Vivek Jayaraman
Neuron (2020)
Modelling electron transfers using quasidiabatic Hartree-Fock states
Kristopher T. Jensen, Raz L. Benson, Salvatore Cardamone, Alex J. W. Thom
Journal of Chemical Theory and Computation (2018)
Chromatin accessibility and guide sequence secondary structure affect CRISPR ‐Cas9 gene editing efficiency
Kristopher T. Jensen, Lasse Fløe, Trine S. Petersen, Jinrong Huang, Fengping Xu, Lars Bolund, Yonglun Luo, Lin Lin
FEBS Letters (2017)