Peter Latham: Publications

Publications


Neuroscience publications
arXiv and bioRxiv
Book chapters
Scholarpedia article: Mutual Information
Physics publications

back to my home page.
Neuroscience publications
  1. Understanding unimodal bias in multimodal deep linear networks.
    Yedi Zhang, Peter E. Latham and Andrew Saxe
    Proceedings of the 41st International Conference on Machine Learning. 2024. PDF

  2. Evolution of neural activity in circuits bridging sensory and abstract knowledge
    Francesca Mastrogiuseppe, Naoki Hiratani and Peter E. Latham
    eLife 12:e79908 (2023). PDF Online version

  3. Actionable neural Representations: grid cells from minimal constraints
    William Dorrell, Peter E Latham, Timothy E.J. Behrens and James C.R. Whittington
    The Eleventh International Conference on Learning Representations. 2023. PDF

  4. Meta-Learning the Inductive Bias of Simple Neural Circuits.
    William Dorrell, Maria Yuffa and Peter E Latham.
    Proceedings of the 40th International Conference on Machine Learning. 2023. PDF

  5. Developmental and evolutionary constraints on olfactory circuit selection
    Naoki Hiratani and Peter E. Latham
    PNAS 119:e2100600119 (2022). PDF
    Supplementary Information

  6. Sparse connectivity for MAP inference in linear models using sister mitral cells.
    Sina Tootoonian, Andreas T. Schaefer and Peter E. Latham
    PLoS Computational Biology 18:e1009808 (2022). PDF

  7. On the Stability and Scalability of Node Perturbation Learning.
    Naoki Hiratani, Yash Mehta, Timothy Lillicrap and Peter E. Latham.
    Advances in Neural Information Processing Systems. 35. MIT Press, Cambridge MA. (2022). PDF

  8. Synaptic plasticity as Bayesian inference.
    Laurence Aitchison, Jannes Jegminat, Jorge Aurelio Menendez, Jean-Pascal Pfister, Alexandre Pouget and Peter E. Latham
    Nature Neurosci. 24:565-571 (2021). PDF
    Supplementary Information

  9. Powerpropagation: A sparsity inducing weight reparameterisation.
    Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham and Yee Whye Teh.
    Advances in Neural Information Processing Systems. 34. MIT Press, Cambridge MA. 2021. PDF

  10. Towards Biologically Plausible Convolutional Networks.
    Roman Pogodin, Yash Mehta, Timothy Lillicrap and Peter E. Latham.
    Advances in Neural Information Processing Systems. 34. MIT Press, Cambridge MA. 2021. PDF

  11. Rapid Bayesian learning in the mammalian olfactory system.
    Naoki Hiratani and Peter E. Latham
    Nature Communications 11:3845 (2020). PDF
    Supplementary Figures

  12. Noisy Synaptic Conductance: Bug or a Feature?
    Dmitri A. Rusakov, Leonid P. Savtchenko and Peter E. Latham
    Trends in Neurosciences. 43:363-372 (2020). PDF

  13. Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.
    Farzaneh Najafi, Gamaleldin F. Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P. Cunningham and Anne K. Churchland
    Neuron. 105:165-179 (2020). PDF

  14. Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks.
    Roman Pogodin and Peter E. Latham
    Advances in Neural Information Processing Systems. 33. MIT Press, Cambridge MA. 2020. PDF

  15. A deep learning framework for neuroscience.
    Blake A. Richards et al.
    Nature Neurosci. 22:1761-1770 (2019). PDF

  16. Think: Theory for Africa.
    Christopher B. Currin, Phumlani N. Khoza, Alexander D. Antrobus, Peter E. Latham, Tim P. Vogels and Joseph V. Raimondo
    PLoS Computational Biology 14:e1007049 (2019). PDF

  17. The idiosyncratic nature of confidence.
    Joaquin Navajas, Chandni Hindocha, Hebah Foda, Mehdi Keramati, Peter E. Latham and Bahador Bahrami
    Nature Human Behaviour 1:810-818 (2017). PDF
    Supplementary Information

  18. Confidence matching in group decision-making.
    Dan Bang, Laurence Aitchison, Rani Moran, Santiago Herce Castanon, Banafsheh Rafiee, Ali Mahmoodi, Jennifer Y.F. Lau, Peter E. Latham, Bahador Bahrami and Christopher Summerfield
    Nature Human Behaviour 1:1-7 (2017). PDF

  19. Robust information propagation through noisy neural circuits.
    Joel Zylberberg, Alexandre Pouget, Peter E. Latham and Eric Shea-Brown
    PLoS Computational Biology 13:e1005497 (2017). PDF

  20. Cracking the neural code for sensory perception by combining statistics, intervention, and behavior.
    Stefano Panzeri, Christopher D. Harvey, Eugenio Piasini, Peter E. Latham and Tommaso Fellin
    Neuron 93:491-507 (2017). PDF

  21. A probabilistic approach to demixing odors.
    Agnieszka Grabska-Barwinska, Simon Barthelme, Jeff Beck, Zachary F Mainen, Alexandre Pouget and Peter E. Latham
    Nature Neurosci. 20:98-106 (2017). PDF
    matlab code (zip file)

  22. An international laboratory for systems and computational neuroscience.
    Larry Abbott et al.
    Neuron 96:1213-1218 (2017). PDF

  23. Correlations demystified.
    Peter E. Latham
    Nature Neurosci. 20:6-8 (2017). PDF

  24. Zipf's law arises naturally when there are underlying, unobserved variables.
    Laurence Aitchison, Nicola Corradi and Peter E. Latham
    PLoS Computational Biology 12:e1005110 (2016). PDF

  25. Post-decisional accounts of biases in confidence.
    Joaquin Navajas, Bahador Bahrami and Peter E. Latham
    Current Opinion in Behavioral Sciences 11:55-60 (2016). PDF

  26. Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.
    Laurence Aitchison, Dan Bang, Bahador Bahrami and Peter E. Latham
    PLoS Computational Biology 11:e1004519 (2015). PDF
    Supplementary information (PDF)

  27. Information-limiting correlations.
    Ruben Moreno-Bote, Jeffrey Beck, Ingmar Kanitscheider, Xaq Pitkow, Peter E. Latham and Alexandre Pouget
    Nature Neurosci. 17:1410-1417 (2014). PDF
    Supplementary information (PDF)

  28. How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
    Agnieszka Grabska-Barwinska and Peter E. Latham
    Journal of Computational Neuroscience. 36:469-481 (2014). PDF

  29. Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making.
    Dan Bang, Riccardo Fusaroli, Kristian Tylen, Karsten Olsen, Peter E. Latham, Jennifer Lau, Andreas Roepstorff, Geraint Rees, Chris Frith and Bahador Bahrami
    Consciousness and Cognition. 16:13-23 (2014). PDF

  30. The Perception of Probability.
    C.R. Gallistel, Monika Krishan, Ye Liu, Reilly Miller, and P.E. Latham
    Psych. Review 121:96-123 (2014). PDF

  31. Probabilistic brains: knowns and unknowns.
    Alexandre Pouget, Jeffrey M. Beck, Wei Ji Ma, and Peter E. Latham
    Nature Neurosci. 16:1170-1178 (2013). PDF

  32. Estimation bias in maximum entropy models.
    Jakob H. Macke, Iain Murray, and Peter E. Latham
    Entropy 15:3109-3219 (2013). PDF

  33. Randomly connected networks have short temporal memory.
    Edward Wallace, Hamid Reza Maei, and Peter E. Latham
    Neural Comput. 25:1408-1439 (2013). PDF

  34. Demixing odors -- fast inference in olfaction.
    Agnieszka Grabska-Barwinska, Jeff Beck, Alexandre Pouget and Peter E. Latham
    Advances in Neural Information Processing Systems. 26. MIT Press, Cambridge MA. 2013. PDF Appendix

  35. Not noisy, just wrong: the role of suboptimal inference in behavioral variability.
    Jeffrey M. Beck, Wei Ji Ma, Xaq Pitkow, Peter E. Latham, and Alexandre Pouget
    Neuron 74:30-39 (2012). PDF

  36. How biased are maximum entropy models?
    Jakob H. Macke, Iain Murray and Peter E. Latham
    Advances in Neural Information Processing Systems. 24. MIT Press, Cambridge MA. 2012. PDF Appendix

  37. Marginalization in neural circuits with divisive normalization.
    Jeffrey M. Beck, Peter E. Latham, and Alexandre Pouget
    J. Neurosci. 31:15310-15319 (2011). PDF
    Supplementary information (PDF)

  38. Optimally interacting minds.
    Bahador Bahrami, Karsten Olsen, Peter E. Latham, Andreas Roepstorff, Geraint Rees, and Chris D. Frith
    Science 329:1081-1085 (2010). PDF
    Supplementary information (PDF)
    Perspective (PDF)

  39. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex.
    Michael London, Arnd Roth, Lisa Beeren, Michael Hausser and Peter E. Latham
    Nature 466:123-127 (2010). PDF
    Supplementary information (PDF)

  40. Pairwise maximum entropy models for studying large biological systems: when they can and when they can't work.
    Y. Roudi, S. Nirenberg and Peter E. Latham
    PLoS Computational Biology 5:e1000380 (2009). PDF

  41. Ruling out and ruling in neural codes.
    A.L. Jacobs, G. Fridman, R.M. Douglas, N.M. Alam, Peter E. Latham, G.T. Prusky and S. Nirenberg
    PNAS 106:5936-5941 (2009). PDF
    Supplementary Information PDF

  42. Feedforward to the past: the relation between neuronal connectivity, amplification, and short-term memory.
    S. Ganguli and Peter E. Latham
    Neuron 61:499 - 501 (2009). PDF

  43. Probabilistic population codes for Bayesian decision making.
    J.M. Beck, W-J. Ma, R. Kiani, T. Hanks, A.K. Churchland, J. Roitman, M.N. Shadlen, Peter E. Latham and A. Pouget
    Neuron 60:1142-1152 (2008). PDF
    Supplementary Information PDF
    Preview by Emilio Salinas PDF

  44. Phase coding: spikes get a boost from local fields.
    Peter E. Latham and M. Lengyel
    Current Biology 18:R349-R351 (2008). PDF

  45. Neural characterization in partially observed populations of spiking neurons.
    J.W. Pillow and Peter E. Latham
    Advances in Neural Information Processing Systems. 20. MIT Press, Cambridge MA. 2008. PDF

  46. Nonverbal arithmetic in humans: Light from noise.
    S. Cordes, C.R. Gallistel, R. Gelman and Peter E. Latham
    Perception & Psychophysics 69:1185-1203 (2007). PDF

  47. A balanced memory network.
    Y. Roudi and Peter E. Latham
    PLoS Computational Biology 3:1679-1700 (2007). PDF

  48. Probabilistic population codes and the exponential family of distributions.
    J. Beck, W.J. Ma, Peter E. Latham and A. Pouget
    Prog Brain Res. 165:509-519 (2007). PDF

  49. Bayesian inference with probabilistic population codes.
    W.J. Ma, J.M. Beck, Peter E. Latham and A. Pouget
    Nature Neurosci. 9:1432-1438 (2006). PDF
    Supplementary information PDF

  50. Neural correlations, population coding and computation.
    B. Averbeck, Peter E. Latham and A. Pouget
    Nature Reviews Neurosci. 7:358-366 (2006). PDF

  51. Synergy, redundancy, and independence in population codes, revisited.
    Peter E. Latham and S. Nirenberg
    J. Neurosci. 25:5195-5206 (2005). PDF

  52. Touche: the feeling of choice.
    Peter E. Latham and P. Dayan
    Nature Neurosci. 8:408-409 (2005). PDF

  53. Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations.
    P. Series, Peter E. Latham and A. Pouget
    Nature Neurosci. 7:1129-1135 (2004). PDF
    Supplementary information PDF

  54. Computing and stability in cortical networks.
    Peter E. Latham and S. Nirenberg
    Neural Comput. 16:1385-1412 (2004). PDF

  55. Optimal computation with attractor networks.
    Peter E. Latham, S. Deneve, and A. Pouget
    J. Physiol. (Paris) 97:683-694 (2003). PDF

  56. Firing rate of the noisy quadratic integrate-and-fire neuron.
    N. Brunel and Peter E. Latham
    Neural Comput. 15:2281-2306 (2003). PDF

  57. Decoding neuronal spike trains: how important are correlations?
    S. Nirenberg and Peter E. Latham
    PNAS 100:7348-7353 (2003). PDF
    Supporting Information: PDF

  58. Analysis of spontaneous bursting activity in random neural networks
    J. Tabak and Peter E. Latham
    Neuroreport 14:1445-1449 (2003). PDF

  59. A test of Gibbon's feedforward model of matching.
    C.R. Gallistel, T.A. Mark, A.P. King, and Peter E. Latham
    Learning and Motivation 33:46-62 (2002). PDF

  60. Digitized neural networks: long-term stability from forgetful neurons.
    A. Pouget and Peter E. Latham
    Nature Neurosci. 5:709-710 (2002). PDF

  61. Associative memory in realistic neuronal networks.
    Peter E. Latham
    Advances in Neural Information Processing Systems. 14. MIT Press, Cambridge MA. 2002. PDF

  62. Retinal ganglion cells act largely as independent encoders.
    S. Nirenberg, S.M. Carcieri, A.L. Jacobs, and Peter E. Latham
    Nature 411:698-701 (2001). PDF
    Supplementary information: PDF Postscript
    Further discussion

  63. Efficient computation and cue integration with noisy population codes.
    S. Deneve, Peter E. Latham, and A. Pouget
    Nature Neurosci. 4(8):826-831 (2001). PDF
    Supplementary information PDF

  64. The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect.
    C.R. Gallistel, T.A. Mark, A.P. King, and Peter E. Latham
    Journal of Experimental Psychology: Animal Behavior Processes 27(4):354-372 (2001). (PDF)

  65. Intrinsic dynamics in neuronal networks. I. Theory.
    Peter E. Latham, B.J. Richmond, P.G. Nelson, and S. Nirenberg
    J. Neurophysiol. 83(2):808-827 (2000). PDF

  66. Intrinsic dynamics in neuronal networks. II. Experiment.
    Peter E. Latham, B.J. Richmond, S. Nirenberg, and P.G. Nelson
    J. Neurophysiol. 83(2):828-835 (2000). PDF

  67. Reading population codes: a neural implementation of ideal observers.
    S. Deneve, Peter E. Latham, and A. Pouget
    Nature Neurosci. 2(8):740-745 (1999). PDF
    Supplementary information: HTML PDF Postscript

  68. Narrow versus wide tuning curves: what's best for a population code?
    A. Pouget, S. Deneve, J.C. Ducom, and Peter E. Latham
    Neural Comput. 11:85-90 (1999). PDF

  69. Heeger's normalization, line attractor networks, and ideal observers.
    S. Deneve, A. Pouget, and Peter E. Latham
    Advances in Neural Information Processing Systems. 11. MIT Press, Cambridge MA. 1999. PDF

  70. Population coding in the retina.
    S. Nirenberg and Peter E. Latham
    Curr. Opin. Neurobiol. 8(4):488-493 (1998). PDF

  71. Coding strategies in monkey V1 and inferior temporal cortices.
    E.D. Gershon, M.C. Weiner, Peter E. Latham, and B.J. Richmond
    J. Neurophysiol. 79(3):1135-1144 (1998). PDF

  72. Statistically efficient estimation using population coding.
    A. Pouget, K. Zhang, S. Deneve, and Peter E. Latham
    Neural Comput. 10:373-401 (1998). PDF Postscript
arXiv and bioRxiv
  1. Understanding unimodal bias in multimodal deep linear networks.
    Yedi Zhang, Peter E. Latham and Andrew Saxe
    arXiv:2312.00935 (2024)

  2. A theory of brain-computer interface learning via low-dimensional control.
    Jorge Aurelio Menendez, Jay A Hennig, Matthew D Golub, Emily R Oby, Patrick T Sadtler, Aaron P Batista, Steven M Chase, Byron M Yu and Peter E Latham
    bioRxiv:2024.04.18.589952 (2024)

  3. Brain-wide representations of prior information in mouse decision-making.
    Charles Findling, Felix Hubert, International Brain Laboratory, Luigi Acerbi, Brandon Benson, Julius Benson, Daniel Birman, Niccolò Bonacchi, Matteo Carandini, Joana A Catarino, Gaelle A Chapuis, Anne K Churchland, Yang Dan, Eric EJ DeWitt, Tatiana A Engel, Michele Fabbri, Mayo Faulkner, Ila Rani Fiete, Laura Freitas-Silva, Berk Gerçek, Kenneth D Harris, Michael Häusser, Sonja B Hofer, Fei Hu, Julia M Huntenburg, Anup Khanal, Chris Krasniak, Christopher Langdon, Peter E Latham, Petrina YP Lau, Zach Mainen, Guido T Meijer, Nathaniel J Miska, Thomas D Mrsic-Flogel, Jean-Paul Noel, Kai Nylund, Alejandro Pan-Vazquez, Liam Paninski, Jonathan Pillow, Cyrille Rossant, Noam Roth, Rylan Schaeffer, Michael Schartner, Yanliang Shi, Karolina Z Socha, Nicholas A Steinmetz, Karel Svoboda, Charline Tessereau, Anne E Urai, Miles J Wells, Steven Jon West, Matthew R Whiteway, Olivier Winter, Ilana B Witten, Anthony Zador, Peter Dayan and Alexandre Pouget
    bioRxiv 2023.07.04.547684 (2023)

  4. A Theory of Unimodal Bias in Multimodal Learning
    Yedi Zhang, Peter E. Latham and Andrew Saxe
    arXiv:2312.00935 (2023)

  5. Humans, rats and mice show species-specific adaptations to sensory statistics in categorisation behaviour
    Victor Pedrosa, Elena Menichini, Quentin Pajot-Moric, Peter Vincent, Liang Zhou, Lillianne Teachen, Peter Latham, Athena Akrami
    bioRxiv 2023.01.30.526119 (2023)

  6. Meta-Learning the Inductive Biases of Simple Neural Circuits
    William Dorrell, Maria Yuffa, Peter Latham
    arXiv:2211.13544 (2022)

  7. Actionable Neural Representations: Grid Cells From Minimal Constraints
    William Dorrell, Peter E. Latham, Timothy E.J. Behrens, James C.R. Whittington
    arXiv:2209.15563 (2022)

  8. Evolution of neural activity in circuits bridging sensory and abstract knowledge
    Francesca Mastrogiuseppe, Naoki Hiratani, Peter E. Latham
    bioRxiv 2022.01.29.478317 (2022)

  9. Sparse connectivity for MAP inference in linear models using sister mitral cells
    Sina Tootoonian, Andreas T. Schaefer and Peter E. Latham
    bioRxiv 2021.06.28.450144 (2021)

  10. Towards Biologically Plausible Convolutional Networks.
    Roman Pogodin, Yash Mehta, Timothy P. Lillicrap and Peter E. Latham
    arXiv 2106.13031 (2021)
    See also NeurIPS 35 (2021)

  11. Powerpropagation: A sparsity inducing weight reparameterisation.
    Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
    arXiv 2110.00296v2 (2021)
    See also NeurIPS 35 (2021)

  12. A rapid and efficient learning rule for biological neural circuits.
    Eren Sezener, Agnieszka Grabska-Barwinska, Dimitar Kostadinov, Maxime Beau, Sanjukta Krishnagopal, David Budden, Marcus Hutter, Joel Veness, Matthew Botvinick, Claudia Clopath, Michael Hausser and Peter E. Latham
    bioRxiv 2021.03.10.434756 (2021)

  13. Synaptic plasticity as Bayesian inference.
    Laurence Aitchison, Jannes Jegminat, Jorge Aurelio Menendez, Jean-Pascal Pfister, Alex Pouget and Peter E. Latham
    arXiv:1410.1029 (2021)
    See also Nature Neurosci. (2021)

  14. Developmental and evolutionary constraints on olfactory circuit selection.
    Naoki Hiratani and Peter E. Latham
    bioRxiv 2020.12.22.423799 (2020)

  15. Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks.
    Roman Pogodin and Peter E. Latham
    arXiv:2006.07123 (2020)
    See also NeurIPS 34 (2020)

  16. Rapid Bayesian learning in the mammalian olfactory system.
    Naoki Hiratani and Peter E. Latham
    bioRxiv 706200 (2019)
    See also Nature Communications 11:3845 (2020)

  17. Strong information-limiting correlations in early visual areas.
    Jorrit S Montijn, Rex G Liu, Amir Aschner, Adam Kohn, Peter E Latham and Alexandre Pouget
    bioRxiv 842724 (2019)

  18. Sparse connectivity for MAP inference in linear models using sister mitral cells.
    Sina Tootoonian and Peter E. Latham
    arXiv:1709.01437 (2017)

  19. Robust information propagation through noisy neural circuits.
    Joel Zylberberg, Alexandre Pouget, Peter E. Latham and Eric Shea-Brown
    arXiv:1608.05706 (2017)
    See also PLoS CB 13:e1005497 (2017)

  20. Zipf's law arises naturally in structured, high-dimensional data.
    Laurence Aitchison, Nicola Corradi and Peter E. Latham
    arXiv:1407.7135 (2016)
    See also PLoS CB 12:e1005110 (2016)

  21. A unifying framework for understanding state-dependent network dynamics in cortex.
    Alexander Lerchner and Peter E. Latham
    arXiv:1511.00411 (2015)

  22. Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations.
    Laurence Aitchison and Peter E. Latham
    arXiv:1505.04544 (2015)

  23. Role of correlations in population coding.
    Peter E. Latham and Yasser Roudi
    arXiv:1109.6524 (2011)

  24. Pairwise maximum entropy models for studying large biological systems: when they can and when they can't work.
    Yasser Roudi, Sheila Nirenberg and Peter Latham
    arXiv:0811.0903 (2008)
    See also PLoS CB 5:e1000380 (2009)

  25. A balanced memory network.
    Yasser Roudi and Peter E. Latham
    arXiv:0704.3005 (2007)
    See also PLoS CB 3:1679-1700 (2007)

  26. Coding Strategies in Monkey V1 and Inferior Temporal Cortices.
    Ethan D. Gershon, Matthew C. Wiener, Peter E. Latham and Barry J. Richmond
    arXiv:q-bio/0309026 (1998)
    See also J. Neurophysiol. 79:1135-1144 (1998)
Book chapters
  1. Bringing Bayes and Shannon to the Study of Behavioural and Neurobiological Timing and Associative Learning.
    C. Randy Gallistel and Peter E. Latham
    In: Timing & Time Perception.
    Edited by Argiro Vatakis, Hedderik van Rijn and Fuat Balcı.
    Brill Press, Leiden, The Netherlands. Pgs. 1-61 (2022)
    PDF

  2. Role of correlations in population coding.
    Peter E. Latham and Yasser Roudi
    In: Principles of Neural Coding.
    Edited by Stefano Panzeri and Rodrigo Quian Quiroga.
    CRC Press, Boca Raton, Florida (2013)
    preprint (PDF) arXive

  3. Computing with population codes.
    Peter E. Latham and A. Pouget
    In: Bayesian Brain.
    Edited by Kenji Doya, Shin Ishii, Alexandre Pouget and Rajesh P.N. Rao.
    MIT press, Cambridge, MA. Pgs. 131-144. (2006).
    preprint (PDF)

  4. Decoding population codes.
    A. Pouget and Peter E. Latham
    In: Handbook of brain theory and neural networks.
    Edited by Michael A. Arbib.
    MIT press, Cambridge, MA. (2003).
    preprint (PDF)

  5. The relevance of Fisher Information for theories of cortical computation and attention.
    A. Pouget, S. Deneve and Peter E. Latham
    In: Visual attention and cortical circuits.
    Edited by Jochen Braun, Christof Koch, and Joel L. Davis.
    MIT press, Cambridge, MA. Pgs. 265-283 (2001).
Society for Neuroscience posters
  1. Multisensory Interactions: Principles, Connections, Response Properties
    D.G.T. Barrett and Peter E. Latham
    Soc. Neurosc. Abstr. 36:370.13 (2010).

  2. Decision making: perception, cognition, neural corrrelates
    M. Ahmadi and Peter E. Latham
    Soc. Neurosc. Abstr. 36:503.12 (2010).

  3. Marginalization using linear probabilistic population codes: An auditory localization example.
    J.M. Beck, P. Latham, A. Pouget
    Soc. Neurosc. Abstr. 35:351.4 (2009).

  4. The interaction between a single cortical neuron and its local network in vivo.
    L. Beeren, M. London, P. Latham, M. Hausser
    Soc. Neurosc. Abstr. 35:656.6 (2009).

  5. Inferences about how the brain works based on in-vivo data must take into account the rich dynamical repertoire of balanced networks.
    A. Lerchner and Peter E. Latham
    Soc. Neurosc. Abstr. 35:823.1 (2009).

  6. Pairwise maximum entropy models: can they help us understand large neuronal populations?
    Y. Roudi, S. Nirenberg and Peter E. Latham
    Soc. Neurosc. Abstr. 34:498.1 (2008).

  7. The difficulty of interpreting cross-correlations in cortex - Apparent ly precise timing in random networks.
    A. Lerchner and Peter E. Latham
    Soc. Neurosc. Abstr. 34:437.2 (2008).

  8. The interaction between a single cortical neuron and its local network in vivo.
    M. London, L. Berren, A. Roth, Peter E. Latham and M. Hausser
    Soc. Neurosc. Abstr. 34:437.4 (2008).

  9. Strategies for finding neural codes.
    A. Jacobs, N. Alam, Peter E. Latham, G. Prusky and S.A. Nirenberg
    Soc. Neurosc. Abstr. 34:568.2 (2008).

  10. Deciding when to decide.
    Peter E. Latham, Y. Roudi, M. Ahmadi and A. Pouget
    Soc. Neurosc. Abstr. 33:740.10 (2007).

  11. Requiem for the spike?
    Peter E. Latham, A. Roth, M. Hausser, M. London
    Soc. Neurosc. Abstr. 32:432.12 (2006).

  12. Ruling out and ruling in neural codes.
    S.A .Nirenberg, A.L. Jacobs, G. Fridman, Peter E. Latham, N. Alam, R.M. Douglas and G.T. Prusky
    Soc. Neurosc. Abstr. 31:855.4 (2005).

  13. Population codes: decoding the "noise".
    Peter E. Latham, A. Pouget and P. Series
    Soc. Neurosc. Abstr. 31:856.7 (2005).

  14. How can realistic networks process time-varying signals? PDF Powerpoint
    H.R. Maei and Peter E. Latham
    Soc. Neurosc. Abstr. 30:81.9 (2004).

  15. Synergy, redundancy and independence in population codes, revisited. PDF Powerpoint
    Peter E. Latham and S. Nirenberg
    Soc. Neurosc. Abstr. 30:648.4 (2004).

  16. Cortical variability and statistical inferences.
    A. Pouget and Peter E. Latham
    Soc. Neurosc. Abstr. 30:984.14 (2004).

  17. Orientation encoding in V1 hypercolumn models and the efficiency of the thalamo-cortical transformation.
    P. Series, Peter E. Latham and A. Pouget
    Soc. Neurosc. Abstr. 30:984.15 (2004).

  18. Statistical efficiency of orientation selectivity models.
    P. Series, Peter E. Latham and A. Pouget
    Soc. Neurosc. Abstr. 29:484.14 (2003).

  19. Computation and memory in recurrent networks. PDF Postscript
    Peter E. Latham
    Soc. Neurosc. Abstr. 28:152.17 (2002).

  20. Yet another method for calculating information from neural data. PDF Postscript
    Peter E. Latham, S.M. Carcieri, A.L. Jacobs and S. Nirenberg
    Soc. Neurosc. Abstr. 26:52.7 (2000).

  21. To what extent do ganglion cells in the mouse retina fall into discrete classes?
    S.M. Carcieri, A.L. Jacobs, Peter E. Latham and S. Nirenberg
    Soc. Neurosc. Abstr. 26:52.6 (2000).

  22. The distribution of ganglion cells in mouse retina.
    A.L. Jacobs, S.M. Carcieri, Peter E. Latham and S. Nirenberg
    Soc. Neurosc. Abstr. 26:52.5 (2000).

  23. Attractor networks in systems with underlying random connectivity. PDF Postscript
    Peter E. Latham and S. Nirenberg
    Soc. Neurosc. Abstr. 25:898.14 (1999).

  24. Direction- and speed-sensitive responses in the mouse retina.
    S.M. Carcieri, J.R. Sinclair, Peter E. Latham, and S. Nirenberg
    Soc. Neurosc. Abstr. 24:57.5 (1998).

  25. Heeger's normalization and ideal observers.
    S. Deneve, Peter E. Latham, and A. Pouget
    Soc. Neurosc. Abstr. 24:59.12 (1998).

  26. Intrinsic dynamics in cultured neuronal networks.
    Peter E. Latham, M.J. O'Donovan, B.J. Richmond, V. Dunlap, and P.G. Nelson
    Soc. Neurosc. Abstr. 23:86.3 (1997).

  27. Evidence for deterministic dynamics in lamprey spinal cord.
    S. Lesher, Peter E. Latham, and A.H. Cohen
    Soc. Neurosc. Abstr. 23:86.4 (1997).

  28. Experiment and theory of the role of spontaneous transmitter release in cultured neural networks.
    Peter E. Latham, B.J. Richmond, and P.G. Nelson
    Soc. Neurosc. Abstr. 22:59.18 (1996).

  29. Stimulus-elicited neuronal responses in striate and inferior temporal cortices are well-described by a gaussian distribution.
    E.D. Gershon, Peter E. Latham, G.X. Gin, and B.J. Richmond
    Soc. Neurosc. Abstr. 22:633.6 (1996).

  30. How much information is carried by correlated neurons?
    Peter E. Latham, G.X. Gin, T.J. Gawne, and B.J. Richmond
    Soc. Neurosc. Abstr. 21:649.3 (1995).

Physics publications
  1. Experimental demonstration of a W-band gyroklystron amplifier.
    M. Blank, B.G. Danly, B. Levush, Peter E. Latham, and D. Pershing
    Phys. Rev. Lett. 79(22):4485-4488 (1997).

  2. Theory of relativistic gyro-traveling wave devices.
    Peter E. Latham and G.S. Nusinovich
    Physics of Plasmas 2(9):3494-3510 (1995).

  3. Stability analysis of relativistic gyro-traveling wave devices.
    Peter E. Latham and G.S. Nusinovich
    Physics of Plasmas 2(9):3511-3523 (1995).

  4. Theory of relativistic cyclotron masers.
    G.S. Nusinovich, Peter E. Latham, and O. Dumbrajs
    Phys. Rev. E. 52(1):998-1012 (1995).

  5. High power operation of first and second harmonic gyrotwystrons.
    W. Lawson, Peter E. Latham, J.P. Calame, J. Cheng, B. Hogan, G.S. Nusinovich, V. Irwin, V.L. Granatstein, and M. Reiser
    J. Appl. Phys. 78(1):550-559 (1995).

  6. Phase-locking of a 2nd-harmonic gyrotron oscillator using a quasi-optical circulator to separate injection and output signals.
    H.Z. Guo, D.J. Hoppe, J. Rodgers, R.M. Perez, J.P. Tate, B.L Conroy, V.L. Granatstein, A.M. Bhanji, Peter E. Latham, G.S. Nusinovich, M.L. Naiman, and S.H. Chen
    IEEE Tran. Plasma Sci. 23(5):822-832 (1995).

  7. High power operation of an X-band gyrotwistron.
    Peter E. Latham, W. Lawson, V. Irwin, B. Hogan, G. S. Nusinovich, H.W. Matthews, and M.K.E. Flaherty
    Phys. Rev. Lett. 72(23):3730-3733 (1994).

  8. Measurements of velocity ratio in a 90 MW gyroklystron electron beam.
    J.P. Calame, J. Cheng, B. Hogan, W. Lawson, C.D. Striffler, Peter E. Latham, and V. Irwin
    IEEE Trans. Plasma Sci. 22(4):476-485 (1994).

  9. Efficiency of frequency up-shifted gyrodevices: cyclotron harmonics versus CARM's.
    G.S. Nusinovich, Peter E. Latham, and H. Li
    IEEE Trans. Plasma Sci. 22(5):796-803 (1994).

  10. The design of a 100 MW, Ku band second harmonic gyroklystron experiment.
    Peter E. Latham, W. Lawson, and V. Irwin
    IEEE Trans. Plasma Sci. 22(5):804-817 (1994).

  11. Theory of phase-locked gyrotrons operating at cyclotron harmonics.
    Peter E. Latham, B. Levush, G.S. Nusinovich, and S. Parikh
    IEEE Trans. Plasma Sci. 22(5):818-824 (1994).

  12. Experimental studies of stability and amplification in a two-cavity second harmonic gyroklystron.
    H.W. Matthews, W. Lawson, J.P. Calame, M.K.E. Flaherty, B. Hogan, J. Cheng, and Peter E. Latham
    IEEE Trans. Plasma Sci. 22(5):825-833 (1994).

  13. Amplification studies of a two-cavity second harmonic gyroklystron with a mixed-mode output cavity.
    J.P. Calame, J. Cheng, Peter E. Latham, W. Lawson, B. Hogan, H.W. Matthews, M.K.E. Flaherty, and C.D. Striffler
    J. Appl. Phys. 75(9):4721-4730 (1994).

  14. High-power operation of a K-band second harmonic gyroklystron.
    W. Lawson, H.W. Matthews, M.K.E. Lee, J.P. Calame, B. Hogan, J. Cheng, Peter E. Latham, V.L. Granatstein, and M. Reiser
    Phys. Rev. Lett. 71(3):456-459 (1993).

  15. Phase locking and bandwidth in a gyrotron oscillator.
    Peter E. Latham, V.L. Granatstein, and Y. Carmel
    Int. J. Infrared Millimeter Waves 14(6):1217-1227 (1993).

  16. Use of Lie transforms to generalize Madey's theorem for computing the gain in microwave devices.
    Peter E. Latham, S.M. Miller, and C.D. Striffler
    Phys. Rev. A 45(2):1197-1206 (1992).

  17. Transverse mode interference in systems with discrete energy levels: applications to waveguide filters.
    Peter E. Latham, J.M. Finn, and J.H. Booske
    Int. J. Electronics 72(2):273-304 (1992).

  18. The scattering matrix formulation for overmoded coaxial cavities.
    W. Lawson and Peter E. Latham
    IEEE Trans. Microwave Theory Tech. 40(10):1973-1977 (1992).

  19. High-power X-band amplification from an overmoded three-cavity gyroklyston with a tunable penultimate cavity.
    S.G. Tantawi, W.T. Main, Peter E. Latham, G.S. Nusinovich, W. G. Lawson, C.D. Striffler, and V.L. Granatstein
    IEEE Trans Plasma Sci. 20(3):205-215 (1992).

  20. Efficient operation of a high-power X-band gyroklystron.
    W. Lawson, J.P. Calame, B. Hogan, Peter E. Latham, M.E. Read, V.L. Granatstein, M. Reiser, and C.D. Striffler
    Phys. Rev. Lett. 67(4):520-523 (1991).

  21. Harmonic operation of a free-electron laser.
    Peter E. Latham, B. Levush, T.M. Antonsen, Jr., and N. Metzler
    Phys. Rev. Lett. 66(11):1442-1445 (1991).

  22. Phase stability of gyroklystron amplifier.
    G.S. Park, V.L. Granatstein, Peter E. Latham, C.M. Armstrong, A.K. Ganguly, and S.Y. Park
    IEEE Trans. Plasma Sci. 19(4):632-640 (1991).

  23. Experimental studies of stability and amplification in four overmoded, two-cavity gyroklystrons operating at 9.87 GHz.
    J.P. Calame, W. Lawson, V.L. Granatstein, Peter E. Latham, B. Hogan, C.D. Striffler, M.E. Read, M. Reiser, and W. Main
    J. Appl. Phys. 70(4):2423-2434 (1991).

  24. A high-average-power tapered FEL amplifier at submillimeter frequencies using sheet electron beams and short-period wigglers.
    S.W. Bidwell, D.J. Radack, T.M. Antonsen, Jr., J.H. Booske, Y. Carmel, W.W. Destler, V.L. Granatstein, B. Levush, Peter E. Latham, I.D. Mayergoyz, and Z.X. Zhang
    Nuclear Instruments and Methods in Physics Research A304:187-191 (1991).

  25. AC space-charge effects in gyroklystron amplifiers.
    Peter E. Latham
    IEEE Trans. Plasma Sci. 18(3):273-285 (1990).

  26. Design of high-average-power, near-millimeter free electron laser oscillators using short-period wigglers and sheet electron beam.
    J.H. Booske, D.J. Radack, T.M. Antonsen, Jr., S.W. Bidwell, Y. Carmel, W.W. Destler, H.P. Freund, V.L. Granatstein, Peter E. Latham, B. Levush, I.D. Mayergoyz, and A. Serbeto
    IEEE Trans. Plasma Sci. 18(3):399-415 (1990).

  27. The interaction of high- and low-frequency waves in a free-electron laser.
    Peter E. Latham and B. Levush
    IEEE Trans. Plasma Sci. 18(3):472-481 (1990).

  28. High-average-power CW FELs for application to plasma heating: designs and experiments.
    J.H. Booske, V.L. Granatstein, D.J. Radack, T.M. Antonsen, Jr., S.W. Bidwell, Y. Carmel, W.W. Destler, Peter E. Latham, B. Levush, I.D. Mayergoyz, Z.X. Zhang, and H.P. Freund
    Nuclear Instruments and Methods in Physics Research A296:791-796 (1990).

  29. Linear-analysis of a free-electron laser coupled to betatron oscillations.
    Peter E. Latham
    Phys. Fluids B 1(10):2085-2098 (1989).

  30. The use of a single source to drive a binary peak power multiplier.
    Peter E. Latham
    IEEE Trans. Microwave Theory Tech. 37(5):929-931 (1989).

  31. Determination of the resonant frequencies in a complex cavity using the scattering matrix formulation.
    J.M. Neilson, Peter E. Latham, M. Caplan, and W.G. Lawson
    IEEE Trans. Microwave Theory Tech. 37(8):1165-1170 (1989).

  32. Free-electron laser with small period wiggler and sheet electron beam: a study of the feasibility of operation at 300 GHz with 1 MW CW output power.
    J.H. Booske, V.L. Granatstein, T.M. Antonsen, Jr., W.W. Destler, J. Finn, Peter E. Latham, B. Levush, I.D. Mayergoyz, D. Radack, and J. Rodgers
    Nuclear Instruments and Methods in Physics Research A285:92-96 (1989).

  33. Linear theory of a sheet beam free electron laser.
    T.M. Antonsen, Jr., and Peter E. Latham
    Phys. Fluids 31(11):3379-3386 (1988).

  34. Penultimate cavity tuning of the gyroklystron amplifier.
    K.R. Chu, Peter E. Latham, and V.L. Granatstein
    Int. J. Electronics 65(3):419-428 (1988).

  35. An FEL driven by transverse gradients in the wiggler field.
    Peter E. Latham
    Nuclear Instruments and Methods in Physics Research A272:442-447 (1988).

  36. Near-millimeter free electron lasers with small period wigglers and sheet electron beams.
    V.L. Granatstein, T.M. Antonsen, Jr., J.H. Booske, W.W. Destler, Peter E. Latham, B. Levush, I.D. Mayergoyz, D.J. Radack, Z. Segalov, and A. Serbet
    Nuclear Instruments and Methods in Physics Research A272:110-116 (1988).

  37. The design of a small-orbit/large-orbit gyroklystron experiment.
    W. Lawson and Peter E. Latham
    J. Appl. Phys. 61(2):519-528 (1987).

  38. Single-particle motion in a large-orbit gyrotron.
    H. Bluem, Peter E. Latham, W. Lawson, and C.D. Striffler
    IEEE Trans. Microwave Theory Tech. 35(11):946-955 (1987).

  39. A 30-MW gyroklystron-amplifier design for high-energy linear accelerators.
    K.R. Chu, V.L. Granatstein, Peter E. Latham, W. Lawson, and C.D. Striffler
    IEEE Trans. Plasma Sci. 13(6):424-434 (1985).

  40. Finite resolution approximation to the asymptotic distribution for dynamical systems.
    H.D.I. Abarbanel and Peter E. Latham
    Phys. Lett. 89A(2):55-58 (1982).
back to my home page.