Laurence Aitchison
Citeret af
Citeret af
With or without you: predictive coding and Bayesian inference in the brain
L Aitchison, M Lengyel
Current opinion in neurobiology 46, 219-227, 2017
Deep convolutional networks as shallow gaussian processes
A Garriga-Alonso, CE Rasmussen, L Aitchison
arXiv preprint arXiv:1808.05587, 2018
Doubly Bayesian analysis of confidence in perceptual decision-making
L Aitchison, D Bang, B Bahrami, PE Latham
PLoS Comput Biol 11 (10), e1004519, 2015
Zipf’s law arises naturally when there are underlying, unobserved variables
L Aitchison, N Corradi, PE Latham
PLoS computational biology 12 (12), e1005110, 2016
Confidence matching in group decision-making
D Bang, L Aitchison, R Moran, SH Castanon, B Rafiee, A Mahmoodi, ...
Nature Human Behaviour 1 (6), 1-7, 2017
Active dendritic integration as a mechanism for robust and precise grid cell firing
C Schmidt-Hieber, G Toleikyte, L Aitchison, A Roth, BA Clark, T Branco, ...
Nature neuroscience 20 (8), 1114, 2017
The Hamiltonian brain: Efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics
L Aitchison, M Lengyel
PLoS computational biology 12 (12), e1005186, 2016
Fast Sampling-Based Inference in Balanced Neuronal Networks.
G Hennequin, L Aitchison, M Lengyel
NIPS 27, 2240-2248, 2014
Synaptic plasticity as Bayesian inference
L Aitchison, J Jegminat, JA Menendez, JP Pfister, A Pouget, PE Latham
Nature Neuroscience, 1-7, 2021
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
R Echeveste, L Aitchison, G Hennequin, M Lengyel
Nature Neuroscience 23 (9), 1138-1149, 2020
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit
L Aitchison, L Russell, A Packer, J Yan, P Castonguay, M Häusser, ...
Advances in Neural Information Processing Systems 2017, 3487-3496, 2017
Zipf's law arises naturally in structured, high-dimensional data
L Aitchison, N Corradi, PE Latham
arXiv preprint arXiv:1407.7135, 2014
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
L Aitchison
arXiv preprint arXiv:1807.07540, 2018
Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes
SW Ober, L Aitchison
arXiv preprint arXiv:2005.08140, 2020
Why bigger is not always better: on finite and infinite neural networks
L Aitchison
International Conference on Machine Learning, 156-164, 2020
A statistical theory of cold posteriors in deep neural networks
L Aitchison
arXiv preprint arXiv:2008.05912, 2020
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
L Aitchison, G Hennequin, M Lengyel
arXiv preprint arXiv:1807.08952, 2018
A statistical theory of semi-supervised learning
L Aitchison
arXiv preprint arXiv:2008.05913, 2020
Bayesian neural network priors revisited
V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ...
arXiv preprint arXiv:2102.06571, 2021
Discrete flow posteriors for variational inference in discrete dynamical systems
L Aitchison, V Adam, SC Turaga
arXiv preprint arXiv:1805.10958, 2018
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Artikler 1–20