Statistical Physics of Inference and Network Organization
STATISTICAL PHYSICS & INFERENCE
What does a set of observations about a complex system tell us about the structure and function of that system? What observations from the states of the system are most informative? Why are many features of data collected from complex systems universal? We use methods mainly from statistical physics and statistical inference to answer these questions. Along the way, we also study the dynamics of complex and disordered dynamical systems.
NEURAL NETWORK MODELLING
How do large networks of neurons in the brain work to generate cognitive functions such as memory and representation of external stimuli? What leads to the different dynamical states of such networks such as synchronous or asynchronous firing, and how do these states interact with the function of the network? Focusing mainly on attractor neural network models as a general framework for modelling memory, we study these questions, and also try to connect them to experimental observations at the Kavli Institute.
NEURAL DATA ANALYSIS
The ability to record the activity of many neurons in the brain has lead to great advances in our understanding of the neuronal codes involved in the brain. Analyzing the data collected with these new methods, however, requires new theoretical and computational tools. We work both on developing such tools and on applying them to various datasets to gain insight into how information carried by neurons in the brain about tasks and behaviors
CURRENT AND PAST GROUP MEMBERS
to join our group contact us at yasser dot roudi at ntnu do no
Yasser is interested in understanding learning and inference in living organisms and machines and for doing so, mainly uses ideas from statistical mechanics and information theory. Yasser was born in Tehran in 1981, went to Alborz High School, and read Physics at Sharif University. After obtaining his PhD from SISSA, he went to the Gatsby Unit, UCL, as a senior research fellow and then to Nordita, the Nordic Institute for Theoretical Physics. He has also been a member at the School of Natural Sciences, Institute for Advanced Study. In addition to being a professor at the Kavli Institute/CNC, Yasser is also a corresponding fellow at Nordita and a research staff associate at the Int. Cent. for Theor. Physics. For his research Yasser has been awarded the Norwegian Academy of Science and Letters young researcher award, the Royal Norwegian Society of Sciences and Letters award for young researchers and the Eric Kandel young neuroscientist prize. He has also been named one of the 10 bright young minds in 2015 by Science News.
Marsili M., Roudi Y. (2021) Relevance in model-free inference and learning, under review
Bulso N., Roudi Y. (2021) Restricted Boltzmann Machines as models of interacting spin variables, in press, Neural Computation
Tombaz T., Dunn B., Hovde K., Cubero R., Mimica B., Mamidanna P., Roudi Y., Whitlock J. (2020) Action representation in the mouse parieto-frontal cortex, Scientific Reports, 10 (1), 1-14
Monsalve-Mercado M. M., Roudi Y. (2020) Hippocampal spike-time correlations and place field overlaps during open field foraging, Hippocampus, 1-13
Cubero R., Jo J., Marsili M., Roudi Y., Song J. (2019) Statistical Criticality arises in Maximally Informative Samples, J. Stat. Mech., 063402
Bulso N., Marsili M., Roudi Y. (2019) On the complexity of logistic regression models, Neural Computation, 38 (9)
Salahshour M., Roudhani S., Roudi Y. (2019) Phase transitions and asymmetry between signal comprehension and production in biological communication, Sci. Rep. (9) 3428
Cubero R., Marsili M., Roudi Y. (2018) Minimum description length codes are cirtical, Entropy 20 (10), 755
Bretta A., Battistin C., Du Mulatier C., Mastromatteo I, Marsili M. (2018) The stochastic complexity of spin models: are pairwise models really simple, Entropy 20 (10) 739
Dunn B., Battistin C. (2017) The appropriateness of ignorance in the Kinetic Ising model, J Phys A: Math. Theor. (50) 124002
Dunn B., Wennberg D., Huang Z-W., Roudi Y. (2017) Grid cells show field-to-field variability and this can explain aperiodic firing of inhibitory interneurons, biorxiv:101899v1
Kanter B. R., Lykken, CM., Avesar D., Weible A., Dickinson J., Dunn B., Borgesius N. Z., Roudi Y. , Kentros C.G., (2017) Transgenic depolarization of medial entorhinal cortex layer II neurons reveals a potential novel mechanism of the grid-to-place cell transformation, Neuron , 93 (6): 1480-1492
Hertz J., Roudi Y. , Sollich P. (2017) Path integral methods for the dynamics of stochastic and disordered systems, J. Phys. A , 50:033001
Battistin C., Dunn B., Roudi Y. (2017) Learning with unknowns: analyzing biological data in the presence of hidden variables, Curr. Op. Sys. Biol. , 1:122-128
Bachschmid-Romano L., Battistin C., Opper M., Roudi Y. (2016) Variational perturbation and extended Plefka approaches to the dynamics on random networks: the case of the kinetic Ising model, J. Phys. A, 49:434003
Bulso N., Marsili M., Roudi Y. (2016) Sparse model selection in the highly under-sampled regime, J. Stat. Mech., 093404
Rowland D., Roudi Y. , Moser M-B., Moser E. (2016) 10 years of grid cells, Annual Rev. of Neurosci. , 39:2
Borysov S., Roudi Y. , Balatsky A. (2015) US stock market interaction network as learned by the Boltzmann Machine, EPJ B , 88(12): 1-14
Battistin C., Hertz J., Tyrcha J., Roudi Y. (2015) Belief-Propagation and replicas for inference and learning in a kinetic Ising model with hidden spins, J Stat. Mech. , P05021
Roudi Y., Taylor G. (2015) Learning with hidden variables, Curr. Opin. Neurobio., 35: 110-118
Roudi Y., Dunn B., Hertz J. (2015) Multi-neuronal activity and functional connectivity in cell assemblies, Curr. Opin. Neurobiol., 32:38- 44
Dunn B., Morreaunet M., Roudi Y. (2015) Correlations and functional connections in a population of grid cells, PLoS Comp. Biol. 11(2): e1004052.
Moser E. I., Roudi Y. , Witter M. P., Kentros C., Bonhoeffer T., Moser M-B (2014) Grid cells and cortical representation, Nat. Rev. Neurosci. 15:466-481
Zeng H-L., Hertz J., Roudi Y. (2014) L1 regularization for reconstruction of a non-equilibrium Ising model, Phys. Scrip. , 80 (10): 105002
Moser E. I., Moser M-B., Roudi Y. (2014) Network mechanisms of grid cells, Phil. Trans. Roy. Soc. , 369:20120511.
Roudi Y., Moser E. I. (2014) Grid cells in an inhibitory network, Nature Neuroscience, 17:639
Marsili M., Mastromatteo I., Roudi Y. (2013) On sampling and modeling complex systems, J. Stat. Mech. , P09003
Latham P., Roudi Y. (2013) Role of stimulus-dependent correlation in population coding,
in Principles of Neural Coding , S. Panzeri and R. Q. Quiroga Eds
Hertz J., Roudi Y. , Tyrcha J. (2013) Ising model for inferring network structure from spike data,
in Principles of Neural Coding , S. Panzeri and R. Q. Quiroga Eds
Zeng H-L., Alava M., Aurell E., Hertz J., Roudi Y. (2013) Maximum likelihood reconstruction of Ising models with asynchronous updates, Phys. Rev. Lett. 110:210601
Dunn B., Roudi Y. (2013), Learning and inference in a nonequilibrium model with hidden nodes, Phys. Rev. E. , 87:022127
Tyrcha J., Roudi Y. , Marsili M., Hertz J. (2013) The effect of nonstationarity on models inferred from neural data, J. Stat. Mech., P03005
Couey J. J.*, Witoelar A.*, Zhang Sh-J.*, Ye J., Dunn B., Czajkowski R., Moser M-B., Moser E.I., Roudi Y. , Witter M.P. (2013) Recurrent inhibitory circuitry as a mechanism for grid formation, Nat. Neuro. 16 (3):309 equal contribution *
Bonnevie T., Dunn B., Fyhn M., Hafting T., Derdikman D., Kubie J.L., Roudi Y. , Moser E.I., Moser M-B. (2013) Grid cells require excitatory drive from the hippocampus, Nat. Neuro. 16 (3):318
Bomash I., Roudi Y. , Nirenberg S (2013) A virtual retina: a tool for studying population coding, PLoS One , 8(1): e53363
Sakellariou J., Roudi Y. , Mezard M., Hertz J. (2012) Effect of coupling asymmetry on mean-field solutions of the direct and inverse Sherrington-Kirkpatrick model, Phil. Mag., 92: 272
Giacomo L., Roudi Y. (2012) The neural encoding of space in parahippocampal cortices, Frontiers in Neural Circuits , 6:53
Roudi Y., Hertz J. (2011) Dynamical TAP equations for non-equilibrium Ising spin glasses, J. Stat. Mech., P03031
Roudi Y., Hertz J. (2011) Mean field theory for nonequilibrium network reconstruction, Phys. Rev. Lett., 106(4):048702
Witoelar A., Roudi Y. (2011) Neural network reconstruction using kinetic Ising models with memory, BMC Neuroscience , 12 (Suppl 1):P274
Tyrcha J., Roudi Y. , Hertz J. (2011) Network inference from non-stationary spike trains, BMC Neuroscience , 12 (Suppl 1):P150
Aurell E., Ollion C., Roudi Y. (2010) Dynamics and performance of susceptibility propagation on synthetic data, Euro. Phys. J. B , 77:587
Hertz J., Roudi Y. , Thorning A., Tyrcha J., Aurell E., Zeng H. (2010) Inferring network connectivity using kinetic Ising models, BMC Neuroscience , 11 (Suppl 1):P51
Roudi Y., Aurell E., Hertz J. (2009) Statistical physics of pairwise probability models, Front. in Comp. Neurosci., 3:22
Roudi Y., Tyrcha J., Hertz J. (2009) Ising model for neural data: model quality and approximate methods for extracting functional connectivity, Phys. Rev. E, 79:051915
Roudi Y., Tyrcha J., Hertz J. (2009) Fast and reliable methods for extracting functional connectivity in large populations, BMC Neuroscience, 10, Suppl. 1 (selected for oral presentation at CNS 2009)
Roudi Y., Nirenberg S., Latham P. E. (2009) Pairwise maximum entropy models for studying large biological systems: when they can and when they can’t work, PLoS Comp. Biol., 5(5): e1000380.
Latham P. E., Roudi Y. (2009) Mutual Information, Scholarpedia , 4(1):1658.
Roudi Y., Treves A. (2008) Representing where along with what information in a model of a cortical patch, PLoS Comp. Biol. 4(3): e1000012.
Roudi Y., Latham P. E. (2007) A balanced memory network, PLoS Comp. Biol. 3(9):1679
Roudi Y., Treves A. (2006) Localized activity profiles and storage capacity of rate-based autoassociative networks, Phys. Rev. E, 73:061904.
Roudi Y., Treves A. (2004) An associative network with spatially organized connectivity, J. Stat. Mech. P07010.
Roudi Y., Treves A. (2003) Disappearance of spurious states in analog associative memories, Phys. Rev. E 67:041906.
Roudi Y. (1999) Breaking the strings that connect hanging masses, Journal of Physics (Iran), 17:99.
Kavli Institute for Systems Neuroscience and Centre for Neural Computation
Olav Kyrres gate 9, 7030 Trondheim, Norway