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
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
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
Jraph: A library for graph neural networks in jax
J Godwin, T Keck, P Battaglia, V Bapst, T Kipf, Y Li, K Stachenfeld, ...
Version 0.0 1, 2020
Spectral inference networks: Unifying deep and spectral learning
D Pfau, S Petersen, A Agarwal, DGT Barrett, KL Stachenfeld
arXiv preprint arXiv:1806.02215, 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
Neuroscience needs network science
DL Barabási, G Bianconi, E Bullmore, M Burgess, SY Chung, ...
Journal of Neuroscience 43 (34), 5989-5995, 2023
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
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
Probabilistic successor representations with Kalman temporal differences
JP Geerts, KL Stachenfeld, N Burgess
arXiv preprint arXiv:1910.02532, 2019
RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments
TM George, M Rastogi, W de Cothi, C Clopath, K Stachenfeld, C Barry
Elife 13, e85274, 2024
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