Introductory lecture: Biology and Physics of Information Processing

• John Hertz (Nordita)

Room: 132:028

Generalized Linear Models for Networks of Spiking Neurons I

• Sara A. Solla (Northwestern University)

Room: 132:028

A zero-parameter quantitative prediction from a theory of the primary visual cortex and its match with data

• Zhaoping Li (UCL)

Room: 132:028

The intersection of reward and memory

• Barry Richmond (NIH, Bethesda)

Room: 132:028

The inverse Ising model: Bethe reconstruction and mean--field reconstruction at low temperature

• Johaness Berg (University of Cologne)

Room: 132:028

Generalized Linear Models for Networks of Spiking Neurons II

• Sara A. Solla (Northwestern University)

Room: 132:028

Collaboration in social networks

• Matteo Marsili (Abdus Salam ICTP)

Room: 132:028

Inference of interactions in assemblies of stochastic Integrate-and-Fire neurons from spike recordings

• Remi Monasson (ENS, Paris)

Room: 132:028

NORDITA Seminar: From Neural Activity to Movement

• Sara A. Solla (Northwestern University)

Room: 132:028

Information about what: the haphazard relation between single units and behaviour

• Barry Richmond (NIH, Bethesda)

Room: 132:028

Squeezing information out of codon bias

• Matteo Marsili (ICTP)

Room: 132:028

Exploring population of neurons involved with motor pattern generation: Electrical activity and network structure

• Rune Berg (University of Copenhagen)

Room: 132:028

Mechanostability of proteins and virus capsids

• Marek Cieplek (Institute of Physics, Polish Academy of Sciences)

Room: 132:028

Islands of equilibrium in non-equilibrium systems

• David Saad (Aston University Birmingham)

Room: 132:028 Equilibrium is a fundamental concept in statistical physics; it assumes that while the system dynamics is governed by microscopic interactions, some systems eventually reach a state where macroscopic observables remain unchanged. The evolution of many such systems is driven by the corresponding Hamiltonian energy function and their states converge to the equilibrium Gibbs-Boltzmann distribution, from which all macroscopic properties can be computed. However, the process governing the dynamics of many other systems cannot be derived from a Hamiltonian; such systems neither obey detailed balance nor converge to an equilibrium state. While many real systems, for example in the financial, social and biological areas, are inherently not in equilibrium, some of their constituents exhibit equilibrium-like behaviour in emerging localised or non-localised domains. In this work we show such behaviour in model systems defined on densely and sparsely connected networks, as they provide a useful representation of many natural and technological systems. Equilibrium domains are shown to emerge either abruptly or gradually depending on the system parameters, for instance temperature, and disappear, becoming indistinguishable from the remainder of the system for other parameter values. Consequently, such domains may exist, under some conditions, within a non-equilibrium system but may be difficult to identify.

Dynamic cavity method and dynamic mean-field for diluted Ising systems

• Hamed Mahmoudi (Aston University, Birmingham)

Room: 132:028 The stationary state of Ising models with Glauber dynamics is studied. In case of fully connected networks, naive mean field approximation is investigated to describe long-time limit of magnetizations. For diluted networks dynamic cavity method is used for a wide range of parameters. The comparison between these two methods show that dynamic BP outperforms naive mean field in diluted networks.

Keeping one's eye on the ball: Inferring latent dynamical state from ensemble neural activity.

• Maneesh Sahani (University College London)

Room: 132:028

A phase diagram for latching dynamics

• Alessandro Treves (SISSA)

Room: 132:028

A selective cluster expansion for the Inverse Ising problem

• Simona Cocco (ENS, Paris)

Room: 132:028

Inference: an algebraic perspective

• Iacopo Mastromatteo (SISSA)

Room: 132:028 I will present a general framework which can be used to reconstruct probability distributions for strings of binary variables. While the problem of inference can be analytically controlled for small systems, a description of some of the regularization prescriptions needed to treat large systems will be provided, together with a discussion concerning their symmetries. Finally, I will present several possible applications of these methods, namely i) exact, fast inference for 1-D periodic systems, ii) exact, fast inference for tree-like graphs, iii) approximate, fast inference for generic graphs.

Neurons in networks: stochastic dynamics, synchronization, oscillations and all that

• Vincent Hakim (ENS, Paris)

Room: 132:028

The inverse Ising problem for one-dimensional chains with arbitrary finite-range couplings

• Giacomo Gori (ICTP)

Room: 132:028 Parameter estimation is a central issue in system modeling, the typical setting is to start with a given set of measurements and extract the parameters of a model supposed to describe the system under scrutiny. The recent availability of large datasets coming from the complex system has made even more pressing the quest for efficient models, and the related parameter extraction techniques. We study Ising chains with arbitrary multispin finite-range couplings, providing an explicit solution of the associated inverse Ising problem, i.e. the problem of inferring the values of the coupling constants from the correlation functions. As an application, we reconstruct the couplings of chain Ising Hamiltonians having exponential or power-law two-spin plus three- or four-spin couplings. The generalization of the method to ladders and to Ising systems where a mean-field interaction is added to general finite-range couplings is also discussed.

A competitive network of grid cells

• Benjamin Dunn (Kavli Institute, NTNU)

Room: 132:028

An introduction to stochastic thermodynamics

• Ralf Eichhorn (Nordita)

Room: 132:028

A refined second law for finite-time stochastic thermodynamics

• Erik Aurell (KTH)

Room: 132:028

Random Matrix Theory: old tricks for new dogs

• Pierpaolo Vivo (LPTMS, Universite Paris-Sud)

Room: 132:028 I present an overview of recent developments of Random Matrix Theory (RMT) to different problems arising in finance, biology, mathematics, telecommunications and elsewhere. Originally rooted in nuclear physics in the 50s, RMT has become a very important tool for scientists working at the interface between several disciplines, with an impressive number of recent developments. Spectral properties of matrices with random entries can be studied analytically in some cases. I will introduce and discuss the so-called 'constrained Coulomb gas method' which has helped solving many problems involving linear statistics on the eigenvalues in a very transparent and physically sound way. An outlook on possible future developments is also offered.

Relevance learning in the analysis of biomedical data

• Michael Biehl (Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen)

Room: 132:028 A brief introduction to the distance based analysis and classification of multi-dimensional data is given. The popular Learning Vector Quantization (LVQ) will serve as the main example in this talk. A key issue in LVQ and many related methods is the choice of a suitable similarity or distance measure. So-called relevance learning schemes employ parameterized distance measures which are optimized in a data-driven training process. The recently introduced Matrix Relevance LVQ, based on generalized Euclidean distances, will be discussed in greater detail. Matrix Relevance Learning and Divergence based LVQ are illustrated in terms of a number of real world applications from the biomedical domain. Examples include the diagnosis of Acute Myeloid Leukaemia based on flow cytometry and the detection of malignancy in adrenal tumors from steroid metabolomics data. Application examples illustrate how relevance learning can be used for efficient multivariate analysis and classification, for the identification of most relevant biomarkers, and for the visualization of complex, labelled data sets.

Cellular mechanisms for the representation of space by entorhinal cortex

• Lisa Giocomo

Room: 132:028

Multiscale modeling of sensory cortical column

• Gaute Einevoll (Norwegian University of Life Sciences, As)

Room: 132:028

Biophysical dynamics of unconconscious vision

• Per Roland (University of Copenhagen)

Room: 132:028

Active decorrelation in local cortical networks

• Markus Diesman (Research Center Juelich)

Room: 132:028 Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent theoretical and experimental studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. By means of a linear network model and simulations of networks of leaky integrate-and-fire neurons, we show that shared-input correlations are efficiently suppressed by inhibitory feedback. To elucidate the effect of feedback, we compare the responses of the intact recurrent network and systems where the statistics of the feedback channel is perturbed. The suppression of spike-train correlations and population-rate fluctuations by inhibitory feedback can be observed both in purely inhibitory and in excitatory-inhibitory networks. The effect is fully understood by a linear theory and becomes already apparent at the macroscopic level of population averaged activity. At the microscopic level, shared-input correlations are suppressed by spike-train correlations: In purely inhibitory networks, they are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between excitatory (E) and inhibitory (I) neurons, but a consequence of a particular structure of correlations among the three possible pairings (EE, EI, II).

Tactile capacities of the rat whisker system and evidence about underlying neuronal activity

• Mathew Diamond (SISSA)

Room: 132:028

Functional modularity of the grid map

• Tor Stensola (Kavli Institute, NTNU)

Room: 132:028

Approaches for deriving correlation structures in massively parallel spike data

• Sonja Grün (Research Center Juelich)

Room: 132:028

Phase transitions and memory effects in the dynamics of noisy Boolean networks

• Alexander Muzaika (COIN, Helsinki)

Room: 132:028 The generating functional method is employed to investigate the synchronous dynamics of Boolean networks, providing an exact result for the system dynamics. The framework accommodates a variety of Boolean function distributions and can be used to study both the noisy and noiseless regimes; it enables one to calculate correlation functions at different times that are inaccessible via commonly used approximations. It is also used to determine conditions for the approximations used in past to be valid, explore phases of the system under different levels of noise and obtain results for models with strong memory effects, where existing approximations break down. Links between noisy Boolean networks and fault-tolerant computation are identified and results common to both system types are highlighted.

Connectivity inference in asynchronously updated kinetic Ising models

• HongLi Zeng (Alto University)

Room: 132:028

Correlations, information and all that

• Peter Latham (Gatsby Unit, University College London)

Room: 132:028