Peter E. Latham - researchr alias

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Viewing Publication 1 - 27 from 27

2023

Meta-Learning the Inductive Bias of Simple Neural CircuitsWill Dorrell, Maria Yuffa, Peter E. Latham. icml 2023: 8389-8402 [doi]

Actionable Neural Representations: Grid Cells from Minimal ConstraintsWill Dorrell, Peter E. Latham, Tim E. J. Behrens, James C. R. Whittington. iclr 2023: [doi]

2022

On the Stability and Scalability of Node Perturbation LearningNaoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham. nips 2022: [doi]

Sparse connectivity for MAP inference in linear models using sister mitral cellsSina Tootoonian, Andreas T. Schaefer, Peter E. Latham. ploscb, 18(1), 2022. [doi]

2021

Towards Biologically Plausible Convolutional NetworksRoman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham. nips 2021: 13924-13936 [doi]

Powerpropagation: A sparsity inducing weight reparameterisationJonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Teh. nips 2021: 28889-28903 [doi]

2020

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networksRoman Pogodin, Peter E. Latham. nips 2020: [doi]

2019

Think: Theory for AfricaChristopher B. Currin, Phumlani N. Khoza, Alexander D. Antrobus, Peter E. Latham, Tim P. Vogels, Joseph V. Raimondo. ploscb, 15(7), 2019. [doi]

2017

Robust information propagation through noisy neural circuitsJoel Zylberberg, Alexandre Pouget, Peter E. Latham, Eric Shea-Brown. ploscb, 13(4), 2017. [doi]

2016

Zipf's Law Arises Naturally When There Are Underlying, Unobserved VariablesLaurence Aitchison, Nicola Corradi, Peter E. Latham. ploscb, 12(12), 2016. [doi]

2015

Doubly Bayesian Analysis of Confidence in Perceptual Decision-MakingLaurence Aitchison, Dan Bang, Bahador Bahrami, Peter E. Latham. ploscb, 11(10), 2015. [doi]

2014

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?Agnieszka Grabska-Barwinska, Peter E. Latham. jcns, 36(3):469-481, 2014. [doi]

2013

2011

How biased are maximum entropy models?Jakob H. Macke, Iain Murray, Peter E. Latham. nips 2011: 2034-2042 [doi]

2009

Mutual informationPeter E. Latham, Yasser Roudi. scholarpedia, 4(1):1658, 2009. [doi]

Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can'tYasser Roudi, Sheila Nirenberg, Peter E. Latham. ploscb, 5(5), 2009. [doi]

2007

Neural characterization in partially observed populations of spiking neuronsJonathan Pillow, Peter E. Latham. nips 2008: 1161-1168 [doi]

A Balanced Memory NetworkYasser Roudi, Peter E. Latham. ploscb, 3(9), 2007. [doi]

2004

Computing and Stability in Cortical NetworksPeter E. Latham, Sheila Nirenberg. neco, 16(7):1385-1412, 2004. [doi]

2003

Firing Rate of the Noisy Quadratic Integrate-and-Fire NeuronNicolas Brunel, Peter E. Latham. neco, 15(10):2281-2306, 2003. [doi]

2001

Associative memory in realistic neuronal networksPeter E. Latham. nips 2001: 237-244 [doi]

1999

Narrow vs Wide Tuning Curves: What s Best for a Population Code?Alexandre Pouget, Sophie Deneve, Jean-Christophe Ducom, Peter E. Latham. neco, 11(1):85-90, 1999.

1998

Divisive Normalization, Line Attractor Networks and Ideal ObserversSophie Deneve, Alexandre Pouget, Peter E. Latham. nips 1999: 104-110 [doi]

Statistically Efficient Estimation Using Population CodingAlexandre Pouget, Kechen Zhang, Sophie Deneve, Peter E. Latham. neco, 10(2):373-401, 1998.

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