P. Latham

发表

Neural correlations, population coding and computation

A. Pouget, B. Averbeck, P. Latham, 2006, Nature Reviews Neuroscience.

Probabilistic brains: knowns and unknowns

A. Pouget, P. Latham, J. Beck, 2013, Nature Neuroscience.

Randomly Connected Networks Have Short Temporal Memory

Peter E. Latham, Hamid Reza Maei, Edward Wallace, 2013, Neural Computation.

Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

M. London, M. Häusser, P. Latham, 2010, Nature.

Synergy, Redundancy, and Independence in Population Codes, Revisited

P. Latham, S. Nirenberg, 2005, The Journal of Neuroscience.

The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect.

C. Gallistel, P. Latham, T. A. Mark, 2001, Journal of experimental psychology. Animal behavior processes.

A Test of Gibbon's Feedforward Model of Matching

C. Gallistel, P. Latham, T. A. Mark, 2002 .

Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability

A. Pouget, P. Latham, J. Beck, 2012, Neuron.

Reading population codes: a neural implementation of ideal observers

A. Pouget, P. Latham, S. Denéve, 1999, Nature Neuroscience.

Intrinsic dynamics in neuronal networks. I. Theory.

B. Richmond, P. Latham, S. Nirenberg, 2000, Journal of neurophysiology.

Bayesian inference with probabilistic population codes

Wei Ji Ma, Alexandre Pouget, Peter E Latham, 2006, Nature Neuroscience.

Efficient computation and cue integration with noisy population codes

A. Pouget, P. Latham, S. Denéve, 2001, Nature Neuroscience.

Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations

A. Pouget, P. Latham, P. Seriès, 2004, Nature Neuroscience.

Retinal ganglion cells act largely as independent encoders

P. Latham, S. Nirenberg, A. Jacobs, 2001, Nature.

Coding strategies in monkey V1 and inferior temporal cortices.

B. Richmond, P. Latham, M. Wiener, 1998, Journal of neurophysiology.

Marginalization in Neural Circuits with Divisive Normalization

A. Pouget, P. Latham, J. Beck, 2011, The Journal of Neuroscience.

Information-limiting correlations

A. Pouget, P. Latham, J. Beck, 2014, Nature Neuroscience.

Mutual Information

Peter E. Latham, Yasser Roudi, P. Latham, 2006 .

Probabilistic Population Codes for Bayesian Decision Making

Timothy D. Hanks, A. Pouget, M. Shadlen, 2008, Neuron.

References and Notes Supporting Online Material Materials and Methods Figs. S1 to S11 References Movie S1 Optimally Interacting Minds R�ports

P. Latham, C. Frith, H. Blau, 2022 .

Probabilistic population codes and the exponential family of distributions.

A. Pouget, P. Latham, J. Beck, 2007, Progress in brain research.

Population coding in the retina

P. Latham, S. Nirenberg, 1998, Current Opinion in Neurobiology.

Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron

Nicolas Brunel, Peter E. Latham, P. Latham, 2003, Neural Computation.

Ruling out and ruling in neural codes

P. Latham, R. Douglas, S. Nirenberg, 2009, Proceedings of the National Academy of Sciences.

Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior

P. Latham, C. Harvey, S. Panzeri, 2017, Neuron.

Developmental and evolutionary constraints on olfactory circuit selection

P. Latham, Naoki Hiratani, 2020, bioRxiv.

The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect.

C. Gallistel, P. Latham, T. A. Mark, 2001, Journal of experimental psychology. Animal behavior processes.

Nonverbal arithmetic in humans: Light from noise

C. Gallistel, P. Latham, R. Gelman, 2007, Perception & psychophysics.

Confidence matching in group decision-making

P. Latham, C. Summerfield, B. Bahrami, 2017, Nature Human Behaviour.

The Perception of Probability

C. Gallistel, P. Latham, M. Krishan, 2022 .

The idiosyncratic nature of confidence

P. Latham, Mehdi Keramati, B. Bahrami, 2017, Nature Human Behaviour.

The idiosyncratic nature of confidence

P. Latham, Mehdi Keramati, B. Bahrami, 2017, bioRxiv.

Intrinsic dynamics in neuronal networks. II. experiment.

B. Richmond, P. Latham, S. Nirenberg, 2000, Journal of neurophysiology.

Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning

Peter E. Latham, John P. Cunningham, Eftychios Pnevmatikakis, 2018, Neuron.

Analysis of spontaneous bursting activity in random neural networks

Joël Tabak, Peter E Latham, P. Latham, 2003, Neuroreport.

Post-decisional accounts of biases in confidence

Bahador Bahrami, Peter E Latham, Joaquin Navajas, 2016, Current Opinion in Behavioral Sciences.

Confidence matching in group decision-making AUTHORS

P. Latham, B. Bahrami, Dan Bang, 2017 .

Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning

Gamaleldin F. Elsayed, J. Cunningham, P. Latham, 2018, Neuron.

A unifying framework for understanding state-dependent network dynamics in cortex

Peter E. Latham, Alexander Lerchner, Alexander Lerchner, 2015, 1511.00411.

Zipf's law arises naturally in structured, high-dimensional data

Peter E. Latham, Laurence Aitchison, P. Latham, 2014 .

Title: a Balanced Memory Network

P. Latham, 2013 .

Evolution of neural activity in circuits bridging sensory and abstract knowledge

P. Latham, F. Mastrogiuseppe, Naoki Hiratani, 2022, bioRxiv.

The relevance of Fisher Information for theories of cortical computation and attention

Peter E. Latham, Alexandre Pouget, Sophie Denève, 2001 .

A rapid and efficient learning rule for biological neural circuits

D. Budden, J. Veness, Marcus Hutter, 2021, bioRxiv.

Actionable Neural Representations: Grid Cells from Minimal Constraints

James C. R. Whittington, P. Latham, W. Dorrell, 2022, 2209.15563.

Evolution of neural activity in circuits bridging sensory and abstract knowledge

P. Latham, F. Mastrogiuseppe, Naoki Hiratani, 2022, bioRxiv.

Meta-Learning the Inductive Biases of Simple Neural Circuits

P. Latham, W. Dorrell, Maria Yuffa, 2022, 2211.13544.

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?

P. Latham, Agnieszka Grabska-Barwinska, A. Grabska-Barwinska, 2013, Journal of Computational Neuroscience.

Ruling out and ruling in neural codes

Peter E. Latham, Sheila Nirenberg, Glen T. Prusky, 2009, Proceedings of the National Academy of Sciences.

Bringing Bayes and Shannon to the Study of Behavioural and Neurobiological Timing and Associative Learning

C. Gallistel, P. Latham, 2022, Timing & Time Perception.

Sparse connectivity for MAP inference in linear models using sister mitral cells

Andreas T. Schaefer, P. Latham, Sina Tootoonian, 2017, bioRxiv.

Rapid Bayesian learning in the mammalian olfactory system

Peter E. Latham, Naoki Hiratani, P. Latham, 2019, Nature Communications.

Robust information propagation through noisy neural circuits

Peter E. Latham, Alexandre Pouget, Eric Shea-Brown, 2016, PLoS Comput. Biol..

Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations

Peter E. Latham, Laurence Aitchison, P. Latham, 2015, 1505.04544.

Bayesian synaptic plasticity makes predictions about plasticity experiments in vivo

Peter E. Latham, Laurence Aitchison, P. Latham, 2014 .

Digitized neural networks: long-term stability from forgetful neurons

Alexandre Pouget, Peter Latham, A. Pouget, 2002, Nature Neuroscience.

Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't

Peter E. Latham, Sheila Nirenberg, Yasser Roudi, 2008, PLoS Comput. Biol..

Decoding neuronal spike trains: How important are correlations?

Sheila Nirenberg, Peter E Latham, P. Latham, 2003, Proceedings of the National Academy of Sciences of the United States of America.

Humans, rats and mice show species-specific adaptations to sensory statistics in categorisation behaviour

P. Latham, Peter Vincent, A. Akrami, 2023, bioRxiv.

Computing and Stability in Cortical Networks

Peter E. Latham, Sheila Nirenberg, P. Latham, 2004, Neural Computation.

Touché: the feeling of choice

Peter Dayan, Peter E Latham, P. Dayan, 2005, Nature Neuroscience.

Neural characterization in partially observed populations of spiking neurons

Peter E. Latham, Jonathan W. Pillow, P. Latham, 2007, NIPS.

A deep learning framework for neuroscience

Surya Ganguli, Yoshua Bengio, Claudia Clopath, 2019, Nature Neuroscience.

Demixing odors - fast inference in olfaction

Peter E. Latham, Alexandre Pouget, Jeffrey M. Beck, 2013, NIPS.

Associative memory in realistic neuronal networks

Peter E. Latham, P. Latham, 2001, NIPS.

Actionable Neural Representations: Grid Cells from Minimal Constraints

James C. R. Whittington, P. Latham, W. Dorrell, 2022, ICLR.

Noisy Synaptic Conductance: Bug or a Feature?

Peter E. Latham, Dmitri A. Rusakov, Leonid P. Savtchenko, 2020, Trends in Neurosciences.

Synaptic plasticity as Bayesian inference

A. Pouget, P. Latham, J. Pfister, 2021, Nature Neuroscience.

Probabilistic Synapses

A. Pouget, P. Latham, L. Aitchison, 2014, 1410.1029.

Powerpropagation: A sparsity inducing weight reparameterisation pdf

Yee Whye Teh, Razvan Pascanu, Peter E. Latham, 2021, NeurIPS.

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making

Peter E. Latham, Bahador Bahrami, Laurence Aitchison, 2015, PLoS Comput. Biol..

A probabilistic approach to demixing odors

Alexandre Pouget, Simon Barthelmé, Zachary F Mainen, 2016, Nature Neuroscience.

Statistically Efficient Estimation Using Population Coding

Peter E. Latham, Alexandre Pouget, Kechen Zhang, 1998, Neural Computation.

2010) Optimally interacting minds

Peter E. Latham, Bahador Bahrami, P. Latham, 2016 .

Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making

Peter E. Latham, Geraint Rees, Bahador Bahrami, 2014, Consciousness and Cognition.

Brain-wide representations of prior information in mouse decision-making

Julia M. Huntenburg, Jonathan W. Pillow, Eric E. J. DeWitt, 2023, bioRxiv.

Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables

Peter E. Latham, Laurence Aitchison, Nicola Corradi, 2016, PLoS Comput. Biol..

Neuron NeuroView An International Laboratory for Systems and Computational Neuroscience

Kenneth D. Harris, Surya Ganguli, Peter E. Latham, 2018 .

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks

P. Latham, Roman Pogodin, 2020, NeurIPS.

A Balanced Memory Network

Peter E. Latham, Yasser Roudi, P. Latham, 2007, PLoS Comput. Biol..

Probabilistic brains: knowns and

Wei Ji Ma, Peter E. Latham, Jeffrey M. Beck, 2013 .

Think: Theory for Africa

Christopher B Currin, Phumlani N Khoza, Alexander D Antrobus, 2019, PLoS Comput. Biol..

An International Laboratory for Systems and Computational Neuroscience

Jonathan W. Pillow, Ilana B. Witten, Larry F. Dora E. Matteo Anne K. Yang Peter Sophie Ila Su Abbott Angelaki Carandini Churchland Dan Da, 2017, Neuron.

References and Notes Supporting Online Material Optimally Interacting Minds

Sta, P. Latham, H. Blau, 2022 .

On the Stability and Scalability of Node Perturbation Learning

T. Lillicrap, P. Latham, Naoki Hiratani, 2022, NeurIPS.

Brain-wide representations of prior information in mouse decision-making

Julia M. Huntenburg, Tatiana A. Engel, Jonathan W. Pillow, 2023, bioRxiv.

Optimal computation with attractor networks

Peter E. Latham, Alexandre Pouget, Sophie Deneve, 2003, Journal of Physiology-Paris.

Role of correlations in population coding

P. Latham, Y. Roudi, 2011, 1109.6524.

Towards Biologically Plausible Convolutional Networks pdf

Peter E. Latham, Timothy P. Lillicrap, Roman Pogodin, 2021, NeurIPS.

Ruling out and ruling in neural codes

P. Latham, R. Douglas, S. Nirenberg, 2009, Proceedings of the National Academy of Sciences of the United States of America.

Feedforward to the Past: The Relation between Neuronal Connectivity, Amplification, and Short-Term Memory

Surya Ganguli, Peter Latham, P. Latham, 2009, Neuron.

Neural characterization in partially observed populations of spiking neurons

J. Pillow, P. Latham, 2007, NIPS.

Narrow Versus Wide Tuning Curves: What's Best for a Population Code?

Peter E. Latham, Alexandre Pouget, Sophie Denève, 1999, Neural Computation.

Inhibitory control of correlated variability in cortical networks

M. Sahani, K. Harris, P. Barthó, 2016 .

Strong information-limiting correlations in early visual areas

Jorrit Steven Montijn, Rex G Liu, Amir Aschner, 2019, bioRxiv.

How does a population of ganglion cells encode a visual stimulus ?

P. Latham, S. Nirenberg, 1998 .

Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't

P. Latham, S. Nirenberg, Y. Roudi, 2008, PLoS Comput. Biol..

Divisive Normalization, Line Attractor Networks and Ideal Observers

Peter E. Latham, Alexandre Pouget, Sophie Denève, 1998, NIPS.

Developmental and evolutionary constraints on olfactory circuit selection

P. Latham, Naoki Hiratani, 2020, bioRxiv.

Phase Coding: Spikes Get a Boost from Local Fields

Peter E. Latham, Máté Lengyel, P. Latham, 2008, Current Biology.