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Heald JB, Lengyel M*, Wolpert DM*. (*equal contributions) Contextual inference underlies the learning of sensorimotor repertoires. Nature 600: 489-493, 2021. paper : bibtex : data : code : commentary

image captionimage caption A model of motor learning explains how we manage a repetroire of distinct memories appropriate for handling objects such as a cup, die or counter. The model determines when to create a new memory and how existing memories are expressed and updated using a full probabilistic treatment of the problem of motor control in the face of stochasticity in our environment (such as the roll of a dice) as well as in our senses and muscles. Image credit and design: Daniel Wolpert.

Liu D, Lengyel M.
A universal probabilistic spike count model reveals ongoing modulations of neural variability.
Advances in Neural Information Processing Systems 34, in press, 2021.

Koblinger Á, Fiser J, Lengyel M.
Representations of uncertainty: where art thou?
Current Opinion in Behavioral Sciences, 38:150-162, 2021.
paper : bibtex

Echeveste R, Aitchison L, Hennequin G*, Lengyel M*. (*equal contributions) Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nature Neuroscience 23: 1138-1149, 2020. paper : code for experiments : code for optimization : bibtex

image captionimage caption This image shows the ornate ceiling of Kings College Chapel: an interaction between regular columnar structure and the play of light coming through the stained glass windows creates a unique, ever-changing percept. This symbolizes how dynamical interactions between the regular structure of a visual cortical hypercolumn and ongoing neural variability underlie a probabilistic model of perception developed by Echeveste and colleagues. Image credit and design: Rodrigo Echeveste.

Doiron B, Lengyel M.
Editorial overview: Computational neuroscience.
Current Opinion in Neurobiology 58:iii-vii, 2019.
paper : bibtex

Gáspár ME, Polack P-O, Golshani P, Lengyel M, Orbán G.
Representational untangling by the firing rate nonlinearity in V1 simple cells.
eLife 8:e43625, 2019.
paper : bibtex : code

Lengyel G, Žalalytė G, Pantelides A, Ingram JN, Fiser J*, Lengyel M*, Wolpert DM*. (*equal contributions)
Unimodal statistical learning produces multimodal object-like representations.
eLife 8:e43942, 2019.
paper : bibtex : code :

Echeveste R, Lengyel M.
The redemption of noise: inference with neural populations.
Trends in Neurosciences 41:767-770, 2018.
(invited commentary on Ma et al. Nature Neuroscience 9:1432-1438, 2006.)
paper : bibtex

Bernacchia A, Lengyel M, Hennequin G.
Exact natural gradient in deep linear networks and its application to the nonlinear case.
Advances in Neural Information Processing Systems 31, 5941-5950, 2018.
paper : bibtex

Ujfalussy BB, Makara JK, Lengyel M*, Branco T*. (*equal contributions) Global and multiplexed dendritic computations under in vivo-like conditions. Neuron 100:579-592, 2018. paper : bibtex : code

cover captioncover caption The apparent complexity of dendrites masks the fundamentally simple principles they obey. In this issue of Neuron, Ujfalussy et al. (pages 579–592) use a systematic computational modeling-based approach to reveal the contributions of dendritic processing to the overall input-output transformation of a cell and find that the net effect of multiple localized dendritic nonlinearities is a simple linear transformation with only a single global nonlinearity. The cover image shows so-called "manganese dendrites": mineral crystal forms that are often mistaken for fossils due to their phenomenological complexity but are revealed to simply obey the basic principles of crystal growth when studied using systematic approaches. Photograph by Balázs Ujfalussy from the collection of Dorottya Örsi.

Hennequin G, Ahmadian Y#, Rubin DB#, Lengyel M*, Miller KD*. (#*equal contributions) The dynamical regime of sensory cortex: stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability. Neuron 98:846-860, 2018. paper : bibtex

cover captioncover caption The variability of cortical responses reveals fundamental properties of the dynamics of the underlying circuit. In this issue of Neuron, Hennequin et al. (pages 846–860) use patterns of stimulus-dependent variability in visual cortex to identify specific circuit mechanisms that generate and control response variability. The cover image is a photograph of a so-called "Chladni pattern": as a sound makes a metal plate vibrate, high- and low-variability regions of the plate are revealed as regions repelling and attracting small grains of rice. Much like in the cortex, patterns of variability arise as an interplay between the "stimulus" (the sound) and the dynamics of the medium (the plate). Photograph by Gergő Orbán, with help from Miklós Vass, Máté and Dávid Lengyel. Artwork by Guillaume Hennequin.

Aitchison M, Lengyel M.
With or without you: predictive coding and Bayesian inference in the brain.
Current Opinion in Neurobiology 46:219-227, 2017.
paper : bibtex

Echeveste R, Hennequin G, Lengyel M.
Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model.
arXiV:1706.00925, 2017.
paper : bibtex

Hennequin G, Lengyel M.
Characterizing variability in nonlinear recurrent neuronal networks.
arXiV:1610.03110, 2016.
paper : bibtex

Aitchison M, Lengyel M.
The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics.
PLoS Computational Biology 12:e1005186, 2016.
paper : supplementary material : code : bibtex

Orbán G, Berkes P, Fiser J, Lengyel M. Neural variability and sampling-based probabilistic representations in the visual cortex. Neuron 92:530-543, 2016. paper : supplemental information : code : bibtex : commentary : mediamedia press release by the Hungarian Academy of Sciences Inforadio (Hungarian news radio) Origo (Hungarian news site)

McNamee D, Wolpert DM, Lengyel M.
Efficient state-space modularization for planning: theory, behavioral and neural signatures.
Advances in Neural Information Processing Systems 29, 4511-4519, 2016.
paper : supplementary material : bibtex

Yang S-CH, Wolpert DM*, Lengyel M*. (*equal contributions)
Theoretical perspectives on active sensing.
Current Opinion in Behavioral Sciences 11:100-108, 2016.
paper : bibtex

Yang S-CH, Lengyel M*, Wolpert DM*. (*equal contributions) Active sensing in the categorization of visual patterns. eLife 5:e12215, 2016. paper : code : bibtex

Csibra G, Hernik M, Mascaro O, Tatone D, Lengyel M.
Statistical treatment of looking-time data.
Developmental Psychology 52:521-536, 2016.
paper : code : bibtex

Friedrich J, Lengyel M.
Goal-directed decision making with spiking neurons.
Journal of Neuroscience 36:1529-46, 2016.
paper : supporting information and code : bibtex : commentary :

Ujfalussy BB, Makara JK, Branco T, Lengyel M. Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits. eLife 4:e10056, 2015. paper : code : bibtex

Lengyel M, Koblinger Á, Popović M, Fiser J.
On the role of time in perceptual decision making.
arXiV:1502.03135, 2015.
paper : bibtex

Festa D, Hennequin G, Lengyel M.
Analog memories in a balanced rate-based network of E-I neurons.
Advances in Neural Information Processing Systems 27, 2231-2239, 2014.
paper : supplementary material : bibtex

Hennequin G, Aitchison L, Lengyel M.
Fast sampling-based inference in balanced neuronal networks.
Advances in Neural Information Processing Systems 27, 2240-2248, 2014.
paper : supplementary material : bibtex

Tootoonian S, Lengyel M.
A dual algorithm for olfactory computation in the locust brain.
Advances in Neural Information Processing Systems 27, 2276-2284, 2014.
paper : supplementary material : bibtex

Aitchison L, Lengyel M.
The Hamiltonian brain.
arXiV:1407.0973, 2014.
paper : bibtex

Hennequin G, Aitchison L, Lengyel M.
Fast sampling for Bayesian inference in neural circuits.
arXiV:1404.3521, 2014.
paper : bibtex

Savin C, Dayan P, Lengyel M.
Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.
PLoS Computational Biology 10: e1003489, 2014.
paper : supporting information : bibtex

Savin C, Dayan P, Lengyel M.
Correlations strike back (again): the case of associative memory retrieval.
Advances in Neural Information Processing Systems 26, 288-296, 2013.
paper : supplementary material : bibtex : talk video

Houlsby NMT#, Huszár F#, Ghassemi MM, Orbán G, Wolpert DM*, Lengyel M*. (#*equal contributions) Cognitive tomography reveals complex, task-independent mental representations. Current Biology 23: 2169-2175, 2013. paper : supporting information : bibtex

Fiser J, Lengyel M, Savin C, Orbán G, Berkes P.
How (not) to assess the importance of correlations for the matching of spontaneous and evoked activity.
arXiV:1301.6554, 2013.
paper : bibtex

Remme MWH, Lengyel M, Gutkin BS.
A theoretical framework for the dynamics of multiple intrinsic oscillators in single neurons.
In: Phase response curves in neuroscience: theory, experiment, and analysis (eds. Schultheiss NW, Prinz AA, Butera RJ), Springer, pp. 53-72, 2012.
chapter : bibtex

Houlsby N, Huszár F, Ghahramani Z, Lengyel.
Bayesian active learning for classification and preference learning.
arXiV:1112.5745, 2011.
paper : bibtex

Ujfalussy B, Lengyel M.
Active dendrites: adaptation to spike-based communication.
Advances in Neural Information Processing Systems 24, 1188-1196, 2011.
paper : supplementary material : bibtex

Savin C, Dayan P, Lengyel M.
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories.
Advances in Neural Information Processing Systems 24, 1305-1313, 2011.
paper : supplementary material : bibtex

Berkes P, Orbán G, Lengyel M*, Fiser J* (*equal contributions) Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331:83-87, 2011. paper : pdf : bibtex : mediamedia press release Origo (Hungarian news site)

Pfister J-P, Dayan P, Lengyel M. Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nature Neuroscience 13:1271-1275, 2010. paper : supplementary material : bibtex : Faculty of 1000

Huszár F, Noppeney U, Lengyel M.
Mind reading by machine learning: A doubly Bayesian method for inferring mental representations.
Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 2810-2815, 2010.
paper : supplementary material : bibtex

Remme MWH, Lengyel M, Gutkin BS. Democracy-independence trade-off in oscillating dendrites and its implications for grid cells. Neuron 66:429-437, 2010. paper : supplementary material : bibtex : commentary : Faculty of 1000

Fiser J, Berkes B, Orbán G, Lengyel M.
Statistically optimal perception and learning: from behavior to neural representations.
Trends in Cognitive Sciences 14:119-130, 2010.
paper : bibtex

Pfister JP, Dayan P, Lengyel M.
Know thy neighbour: a normative theory of synaptic depression.
Advances in Neural Information Processing Systems 22, 1464-1472, 2009.
paper : supplementary material : bibtex

Remme MWH, Lengyel M, Gutkin BS.
The role of ongoing dendritic oscillations in single-neuron dynamics.
PLoS Computational Biology 5:e1000493, 2009
paper : supplementary material : bibtex

Latham PE, Lengyel M.
Phase coding: spikes get a boost from local fields.
Current Biology 18:R349-351, 2008.
(invited commentary on Montemurro et al. Current Biology 18:375-380, 2008.)
paper : target article : bibtex

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.
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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.
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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.
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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