Divisive Normalization, Line Attractor Networks and Ideal Observers. - Sophie Denève; Alexandre Pouget; Peter E. Latham (NIPS, 1998)

Statistically Efficient Estimation Using Population Coding. - Alexandre Pouget; Kechen Zhang; Sophie Denève; Peter E. Latham (Neural Comput., 1998)

Narrow vs Wide Tuning Curves: What's Best for a Population Code? - Alexandre Pouget; Sophie Denève; Jean-Christophe Ducom; Peter E. Latham (Neural Comput., 1999)

Associative memory in realistic neuronal networks. - Peter E. Latham (NIPS, 2001)

Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron. - Nicolas Brunel; Peter E. Latham (Neural Comput., 2003)

Computing and Stability in Cortical Networks. - Peter E. Latham; Sheila Nirenberg (Neural Comput., 2004)

Neural characterization in partially observed populations of spiking neurons. - Jonathan W. Pillow; Peter E. Latham (NIPS, 2007)

A Balanced Memory Network. - Yasser Roudi; Peter E. Latham (PLoS Comput. Biol., 2007)

Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't. - Yasser Roudi; Sheila Nirenberg; Peter E. Latham (PLoS Comput. Biol., 2009)

Mutual information. - Peter E. Latham; Yasser Roudi (Scholarpedia, 2009)

How biased are maximum entropy models? - Jakob H. Macke; Iain Murray 001; Peter E. Latham (NIPS, 2011)

Does interaction matter? Testing whether fast and frugal heuristics can replace interaction in collective decision-making. - Dan Bang; Riccardo Fusaroli; Kristian Tylén; Karsten Olsen; Peter E. Latham; Jennifer Lau; Andreas Roepstorff; Geraint Rees 001; Chris D. Frith; Bahador Bahrami (CogSci, 2013)

Demixing odors - fast inference in olfaction. - Agnieszka Grabska-Barwinska; Jeffrey M. Beck; Alexandre Pouget; Peter E. Latham (NIPS, 2013)

Estimation Bias in Maximum Entropy Models. - Jakob H. Macke; Iain Murray 001; Peter E. Latham (Entropy, 2013)

Randomly Connected Networks Have Short Temporal Memory. - Edward W. J. Wallace; Hamid Reza Maei; Peter E. Latham (Neural Comput., 2013)

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes? - Agnieszka Grabska-Barwinska; Peter E. Latham (J. Comput. Neurosci., 2014)

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making. - Laurence Aitchison; Dan Bang; Bahador Bahrami; Peter E. Latham (PLoS Comput. Biol., 2015)

Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables. - Laurence Aitchison; Nicola Corradi; Peter E. Latham (PLoS Comput. Biol., 2016)

Robust information propagation through noisy neural circuits. - Joel Zylberberg; Alexandre Pouget; Peter E. Latham; Eric Shea-Brown (PLoS Comput. Biol., 2017)

Think: Theory for Africa. - Christopher Brian Currin; Phumlani N. Khoza; Alexander D. Antrobus; Peter E. Latham; Tim P. Vogels; Joseph Valentino Raimondo (PLoS Comput. Biol., 2019)

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. - Roman Pogodin; Peter E. Latham (NeurIPS, 2020)

Towards Biologically Plausible Convolutional Networks. - Roman Pogodin; Yash Mehta; Timothy P. Lillicrap; Peter E. Latham (NeurIPS, 2021)

Powerpropagation: A sparsity inducing weight reparameterisation. - Jonathan Schwarz; Siddhant M. Jayakumar; Razvan Pascanu; Peter E. Latham; Yee Whye Teh (NeurIPS, 2021)

On the Stability and Scalability of Node Perturbation Learning. - Naoki Hiratani; Yash Mehta; Timothy P. Lillicrap; Peter E. Latham (NeurIPS, 2022)

Sparse connectivity for MAP inference in linear models using sister mitral cells. - Sina Tootoonian; Andreas T. Schaefer; Peter E. Latham (PLoS Comput. Biol., 2022)

Actionable Neural Representations: Grid Cells from Minimal Constraints. - Will Dorrell; Peter E. Latham; Tim E. J. Behrens; James C. R. Whittington (ICLR, 2023)

Meta-Learning the Inductive Bias of Simple Neural Circuits. - Will Dorrell; Maria Yuffa; Peter E. Latham (ICML, 2023)

A Theory of Unimodal Bias in Multimodal Learning. - Yedi Zhang; Peter E. Latham; Andrew M. Saxe (CoRR, 2023)

Understanding Unimodal Bias in Multimodal Deep Linear Networks. - Yedi Zhang; Peter E. Latham; Andrew M. Saxe (ICML, 2024)

When Are Bias-Free ReLU Networks Like Linear Networks? - Yedi Zhang; Andrew M. Saxe; Peter E. Latham (CoRR, 2024)

Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations. - Will Dorrell; Kyle Hsu; Luke Hollingsworth; Jin Hwa Lee; Jiajun Wu 001; Chelsea Finn; Peter E. Latham; Timothy Edward John Behrens; James C. R. Whittington (CoRR, 2024)

Range, not Independence, Drives Modularity in Biologically Inspired Representations. - Will Dorrell; Kyle Hsu; Luke Hollingsworth; Jin Hwa Lee; Jiajun Wu 001; Chelsea Finn; Peter E. Latham; Timothy Edward John Behrens; James C. R. Whittington (ICLR, 2025)

When Are Bias-Free ReLU Networks Effectively Linear Networks? - Yedi Zhang; Andrew M. Saxe; Peter E. Latham (Trans. Mach. Learn. Res., 2025)

Training Dynamics of In-Context Learning in Linear Attention. - Yedi Zhang; Aaditya K. Singh; Peter E. Latham; Andrew M. Saxe (CoRR, 2025)