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Research interests
The brain has a remarkable capacity to learn continuously about the environment and to use this knowledge flexibly to make predictions and guide its future decisions. I study learning and memory from computational, algorithmic/representational and neurobiological viewpoints. I also maintain an active interest in the possible computational functions of neural oscillations, particularly those present in the hippocampus and neocortex. Computationally and algorithmically, I use ideas from Bayesian approaches to statistical inference and reinforcement learning to characterize the goals and mechanisms of learning in terms of normative principles and behavioral results. I also perform dynamical systems analyses of reduced biophysical models to understand the mapping of these mechanisms into cellular and network models. I collaborate very closely with experimental neuroscience groups, doing in vitro intracellular recordings, multi-unit recordings in behaving animals, and human psychophysical experiments.Publications
complete list is downloadable from hereLatham PE, Lengyel M.
Phase coding: spikes get a boost from local fields.
Current Biology 18:R349-351, 2008.
(commentary on Montemurro et al. Current Biology 18:375-380, 2008.)
paper : target article : bibtex
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Orbán G, Fiser J, Aslin
RN, Lengyel M. Bayesian learning of visual chunks by human observers. Proceedings of the National Academy of Sciences USA 105:2745-2750, 2008. paper : supplementary material : bibtex |
Lengyel M, Dayan P.
Hippocampal contributions to control: the third way.
Advances in Neural Information Processing Systems 20, 889-896, 2008.
paper : supplementary material : bibtex
Lengyel M, Dayan P.
Uncertainty, phase, and oscillatory hippocampal recall.
Advances in Neural Information Processing Systems 19, 833–840, 2007.
paper : supplementary material : bibtex
Orbán G, Fiser J, Aslin RN, Lengyel M.
Learning objects by learning models: finding independent causes and preferring simplicity.
Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society, 645-650, 2006.
paper : bibtex
Orbán G, Fiser J, Aslin RN, Lengyel M.
Bayesian model learning in human visual perception.
Advances in Neural Information Processing Systems 18, 1043-1050, 2006.
paper : bibtex
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Lengyel
M, Kwag J, Paulsen O, Dayan P. Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves. Nature Neuroscience 8:1677-1683, 2005. paper : supplementary material : bibtex |
Lengyel M, Dayan P.
Rate- and phase-coded autoassociative memory.
Advances in Neural Information Processing Systems 17, 769-776, 2005.
paper : bibtex
Lengyel M, Huhn Zs, Érdi, P.
Computational theories on the function of theta oscillations.
Biological Cybernetics 92:393–408, 2005.
paper : bibtex
Huhn Zs, Orbán G, Érdi P, Lengyel M.
Theta oscillation-coupled dendritic spiking integrates inputs on a long time scale.
Hippocampus 15:950-962, 2005.
paper : bibtex
Huhn Zs, Lengyel M, Orbán G, Érdi P.
Dendritic spiking accounts for rate and phase coding in a biophysical model of a hippocampal place cell.
Neurocomputing 65-66: 331-341, 2005.
paper : bibtex
Lengyel M, Érdi P.
Theta modulated feed-forward network generates rate and phase coded firing in the entorhino-hippocampal system.
IEEE Transactions on Neural Networks 15: 1092-1099, 2004.
paper : bibtex
Papp G, Huhn Zs, Lengyel M, Érdi P.
Effects of dendritic location and different components of LTP expression on the firing activity of hippocampal CA1 pyramidal cells.
Neurocomputing 58-60: 692-697, 2004.
paper : bibtex
Érdi P, Lengyel M.
Matematikai modellek az idegrendszer-kutatásban (Mathematical models in neuroscience, in Hungarian).
In: Kognitív idegtudomány (Cognitive Neuroscience, in Hungarian, eds. Pléh Cs, Kovács Gy, Gulyás B), Osiris: Budapest, pp. 126-148, 2003.
bibtex
Lengyel M.
The theta switch: rate and phase coding in the entorhino-hippocampal system
PhD Thesis, 2003.
thesis : bibtex
Zalányi L, Csárdi G, Kiss T, Lengyel M, Warner R, Tobochnik J, Érdi P.
Properties of a random attachment growing network.
Physical Review E 68: 066104, 2003.
paper : bibtex
Lengyel M, Szatmáry Z, Érdi P.
Dynamically detuned oscillations account for the coupled rate and temporal code of place cell firing.
Hippocampus 13: 700-714, 2003.
paper : bibtex
Orbán G, Kiss T, Lengyel M, Érdi P.
Hippocampal rhythm generation: gamma-related theta-frequency resonance in CA3 interneurons.
Biological Cybernetics 84: 123-132, 2001.
paper : bibtex
Kiss T, Orbán G, Lengyel M, Érdi P.
Intrahippocampal gamma and theta rhythm generation in a network model of inhibitory interneurons.
Neurocomputing 38-40: 713-719, 2001.
paper : bibtex
Misják F, Lengyel M, Érdi P.
Episodic memory and cognitive map in a rate model network of the rat hippocampus.
Lecture Notes in Computer Science 2130: 1135-1140, 2001.
manuscript : bibtex
Lengyel M.
Szomatikus és dendritikus gátlás közötti pozíciófüggő különbségek hippokampális piramissejteken (Differences between somatic and dendritic inhibition on hippocampal pyramidal cells, in Hungarian).
MSc Thesis, 2000.
thesis : bibtex
Lengyel M, Kepecs Á, Érdi P.
Location-dependent differences between somatic and dendritic IPSPs.
Neurocomputing 26-27: 193-197, 1999.
paper : bibtex
Bazsó F, Kepecs Á, Lengyel M, Payrits Sz, Szalisznyó K, Zalányi L, Érdi P.
Single cell and population activities in cortical-like systems.
Reviews in the Neurosciences 10: 201-212, 1999.
manuscript : bibtex
Teaching
| 3G2 : Mathematical Physiology (4 lectures on mathematical electrophysiology) : see EfLS website |
| 3G3 : Introduction to Neuroscience (4 lectures on learning and memory) : see EfLS website |
| 4G3 : Computational Neuroscience (module leader + 10 lectures): see EfLS website |
| 4th year projects : see COMET |
Events organized
| 2008 |
Programme
Gulbenkian Champalimaud Neuroscience course on Hippocampus
and Navigation with: Miguel Remondes |
| 2006 |
Workshop on Computing
with
Spikes (Cosyne 2006, Salt Lake City, UT, USA) with: Sophie Deneve, Boris Gutkin |
Grants
| 2008-2010 |
Wellcome Trust Project Grant:
'Spike timing-based memory in the hippocampus' with Peter Dayan (UCL, UK) and Ole Paulsen (U Oxford, UK) |
| 2006-2007 |
NWO - British Council
Partnership Programme in Science with Francesco Battaglia (U Amsterdam, The Netherlands) |
| 2006–2009 | British Council Franco-British
Alliance Programme with Peter Dayan (UCL, UK) and Boris Gutkin (CNRS, France) |
Collaborators
Richard
Aslin (Dept Brain and Cognitive Sciences, Center for Visual
Science, U Rochester)Francesco Battaglia (Swammerdam Inst for Life Sciences, U Amsterdam)
Peter Dayan (Gatsby Computational Neuroscience Unit, UCL)
József Fiser (Dept Psychology, Volen Center for Complex Systems, Brandeis U)
Boris Gutkin (Group for Neural Theory, ENS)
Uta Noppeney (Cognitive Neuroimaging Group, MPI for Biological Cybernetics)
Ole Paulsen (Dept Physiology, Anatomy and Genetics, U Oxford)
Students, postdocs
Current
Jean-Pascal Pfister, postdoc (since 2008)Ferenc Huszár, undergrad (since 2007)
Alumni
Gergő Orbán, undergrad, PhD (1998-2006)Zsófia Huhn, undergrad (2001-2005)
Tamás Kiss, undergrad (1998-2000)
Fanni Misják, undergrad (1999-2001)
Gergely Papp, undergrad (2001-2003)
Vacancies
My lab has no vacancies at the moment, but I am always keen to
consider qualified applicants willing to secure independent funding -
with which I am glad to help.Postdoc applicants
Candidates must have a strong analytical background and demonstrable interest in theoretical neuroscience. They should have completed (or be near to completing) a PhD or equivalent in neuroscience, cognitive science, computational neuroscience, computational cognitive science, physics, mathematics, computer science, machine learning or a related field. Preference will be given to candidates with sufficient programming skills to run numerical simulations (eg. in C or MatLab). Expertise with neural network models, analysis of dynamical systems, Bayesian techniques, or reinforcement learning, and familiarity with relevant neurobiology is an advantage.Applicants should apply to my e-mail address by forwarding, in PDF or plain text format where possible,
- their CV,
- a statement of research interests,
- names
and full contact details (including e-mail addresses) of three referees.
PhD applicants
Applicants should have a strong analytical background, a keen interest in neuroscience or machine learning and a relevant first degree, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply.Applications proceed in two stages.
- In the first instance, applicants are encouraged to send their applications to Diane Unwin by forwarding, in PDF or plain
text format where possible:
- their CV,
- a statement of research interests,
- transcript(s) for previous degrees,
- Once evaluated favourably in the first stage, a formal application for admission as a graduate student must be made on a University application form. Further information is available at the Graduate Admissions website of the University.
Katalin Gárdos, psychotherapy in Cambridge