Kimberly Lauren Stachenfeld
Kimberly Lauren Stachenfeld
Research Scientist, Google DeepMind
Verified email at - Homepage
Cited by
Cited by
The hippocampus as a predictive map
KL Stachenfeld, MM Botvinick, SJ Gershman
Nature neuroscience 20 (11), 1643-1653, 2017
What is a cognitive map? Organizing knowledge for flexible behavior
TEJ Behrens, TH Muller, JCR Whittington, S Mark, AB Baram, ...
Neuron 100 (2), 490-509, 2018
Design principles of the hippocampal cognitive map
KL Stachenfeld, M Botvinick, SJ Gershman
Advances in neural information processing systems 27, 2014
Structured agents for physical construction
V Bapst, A Sanchez-Gonzalez, C Doersch, K Stachenfeld, P Kohli, ...
International conference on machine learning, 464-474, 2019
Formalizing planning and information search in naturalistic decision-making
LT Hunt, ND Daw, P Kaanders, MA MacIver, U Mugan, E Procyk, ...
Nature neuroscience 24 (8), 1051-1064, 2021
A general model of hippocampal and dorsal striatal learning and decision making
JP Geerts, F Chersi, KL Stachenfeld, N Burgess
Proceedings of the National Academy of Sciences 117 (49), 31427-31437, 2020
Noradrenergic control of error perseveration in medial prefrontal cortex
MS Caetano, LE Jin, L Harenberg, KL Stachenfeld, AFT Arnsten, ...
Frontiers in integrative neuroscience 6, 125, 2013
Flexible modulation of sequence generation in the entorhinal–hippocampal system
DC McNamee, KL Stachenfeld, MM Botvinick, SJ Gershman
Nature neuroscience 24 (6), 851-862, 2021
Learned coarse models for efficient turbulence simulation
K Stachenfeld, DB Fielding, D Kochkov, M Cranmer, T Pfaff, J Godwin, ...
arXiv preprint arXiv:2112.15275, 2021
Jraph: A library for graph neural networks in jax., 2020
J Godwin, T Keck, P Battaglia, V Bapst, T Kipf, Y Li, K Stachenfeld, ...
URL http://github. com/deepmind/jraph 5, 0
Spectral inference networks: Unifying deep and spectral learning
D Pfau, S Petersen, A Agarwal, DGT Barrett, KL Stachenfeld
arXiv preprint arXiv:1806.02215, 2018
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns
JR Manning, X Zhu, TL Willke, R Ranganath, K Stachenfeld, U Hasson, ...
NeuroImage 180, 243-252, 2018
Physical design using differentiable learned simulators
KR Allen, T Lopez-Guevara, K Stachenfeld, A Sanchez-Gonzalez, ...
arXiv preprint arXiv:2202.00728, 2022
Learned simulators for turbulence
K Stachenfeld, DB Fielding, D Kochkov, M Cranmer, T Pfaff, J Godwin, ...
International conference on learning representations, 2021
Rapid learning of predictive maps with STDP and theta phase precession
TM George, W de Cothi, KL Stachenfeld, C Barry
Elife 12, e80663, 2023
Graph network simulators can learn discontinuous, rigid contact dynamics
KR Allen, TL Guevara, Y Rubanova, K Stachenfeld, A Sanchez-Gonzalez, ...
Conference on Robot Learning, 1157-1167, 2023
Graph networks with spectral message passing
K Stachenfeld, J Godwin, P Battaglia
arXiv preprint arXiv:2101.00079, 2020
Neuroscience needs network science
DL Barabási, G Bianconi, E Bullmore, M Burgess, SY Chung, ...
Journal of Neuroscience 43 (34), 5989-5995, 2023
Probabilistic successor representations with Kalman temporal differences
JP Geerts, KL Stachenfeld, N Burgess
arXiv preprint arXiv:1910.02532, 2019
Spectral inference networks: Unifying spectral methods with deep learning
D Pfau, S Petersen, A Agarwal, D Barrett, K Stachenfeld
arXiv preprint arXiv:1806.02215 2, 2018
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