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Dr Matteo Marsili (Abdus Salam ICTP)05/09/2011, 09:30Advanced inference techniques allow one to reconstruct the pattern of interaction from high dimensional data sets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to a phase transition. On one side, we show that the reparameterization invariant metrics in the space of probability...Go to contribution page
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Wilson Truccolo05/09/2011, 11:00I will review past and ongoing work on the statistical modeling of neuronal ensemble point processes based on conditional intensity functions. These functions incorporate intrinsic single-neuron and network dynamics, as well as extrinsic inputs. I will illustrate the approach and current challenges with applications to single and dual 96-microelectrode cortical- array recordings...Go to contribution page
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Gasper Tkacik (IST, Austria)05/09/2011, 12:00In most areas of the brain, information is encoded in the correlated activity of large populations of neurons. Here we build probabilistic models of such population codes using maximum entropy principle from new recordings of more than 100 retinal ganglion cells from a dense patch on the salamander retina. We illustrate how the pairwise maximum entropy (Ising-like) models can be...Go to contribution page
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Peter Latham (University College London)05/09/2011, 14:00The general problem faced by the sensory system is to translate from spikes at sensory receptors to stimuli in the outside world. Here we consider how the brain might do this for olfaction, possibly the simplest of the senses. Our starting point is a linear generative model in which the response of each odorant receptor neuron is a linear combination of odors in the outside world;...Go to contribution page
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Sara A. Solla (Northwestern University)05/09/2011, 15:30Generalized Linear Models provide a framework for the systematic description of neural activity. The formulation of these models is based on the exponential family of probability distributions; the case of Bernoulli and Poisson distributions are relevant to the case of stochastic spiking. In this approach, the time-dependent firing rate of individual neurons is modeled in terms of ...Go to contribution page
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Enzo Marinari (University of Rome La Sapienza)06/09/2011, 09:30The analysis of non-equilibrium steady states of biochemical reaction networks relies on finding the configurations of fluxes and chemical potentials satisfying stoichiometric (mass balance) and thermodynamic (energy balance) constraints. Efficient methods to explore such states are crucial to predict reaction directionality, calculate physiologic ranges of variability, estimate...Go to contribution page
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Hakon Tjelmeland (NTNU)06/09/2011, 11:00In the presentation we propose computationally feasible approximations to binary Markov random fields. The basis of the approximation is the forward-backward algorithm. This exact algorithm is computationally feasible only for fields defined on small graphs. The forward part of the algorithm computes a series of joint marginal distributions by summing out each variable in turn. We...Go to contribution page
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David Saad (Aston University Birmingham)06/09/2011, 12:00Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modelling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient...Go to contribution page
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Bert Kappen (Radboud University)06/09/2011, 14:00In this talk, I present a new model and solution method for sparse regression. The model introduces binary selector variables $s_i$ for the features $i$ in a way that is similar to the original garrote model. The posterior probability for $s_i$ is computed in the variational approximation. I refer to this method as the Variational Garrote (VG). The VG is compared numerically with...Go to contribution page
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Prof. Riccardo Zecchina (Politecnico di Torino)06/09/2011, 15:30
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Michael Biehl (University of Groningen)07/09/2011, 09:30An introduction to distance based classification of multi- dimensional data is given.The popular Learning Vector Quantization (LVQ) will serve as the main example in this talk. Here, typical representatives of the classes (prototypes) are determined from labelled example data in a supervised training process. In the working phase,the prototypes parameterize a classifier which can...Go to contribution page
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Ingve Simonsen (NTNU)07/09/2011, 11:00Our contemporary societies rely more and more on a steady and reliable power supply for their well-functioning. During the last few decades a number of large-scale power blackouts have been witnessed around the world, and this has caused major concerns among politicians and citizens. In this talk we will mention a few major power blackouts and discuss the sequence of events and why...Go to contribution page
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Manfred Opper (TU Berlin)07/09/2011, 12:00Continuous time Markov processes (such as jump processes and diffusions) play an important role in the modelling of dynamical systems in many scientific areas ranging from physics to systems biology. In a variety of applications, the stochastic state of the system as a function of time is not directly observed. One has only access to a set of nolsy observations taken at...Go to contribution page
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Prof. Marc Mezard (Universite de Paris Sud)07/09/2011, 14:00
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Yasser Roudi (Kavli Inst./Nordita)07/09/2011, 15:30
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HongLi Zeng (Aalto University)07/09/2011, 16:30In the presentation, I will focus on the reconstruction of the network structure based on the synthetic data produced by asynchronously update Ising model. The inferred structure can be obtained by three different approaches: naive mean field (nMF) approximation, Thouless-Anderson-Palmer (TAP) approximation and an exact learning method. I will explain these approaches briefly for...Go to contribution page
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Hamed Mahmoudi (Aalto University)07/09/2011, 16:50The 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...Go to contribution page
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Aree Witoelar (Kavli Inst./NTNU)07/09/2011, 17:10
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Peter Sollich (Kings College London)08/09/2011, 09:30In systems biology we are encouraged to think in terms of networks to try and understand the complex behaviour of cells. There is much uncertainty in the identification process of proteins, so often the complete network of reactions in a protein interaction network (PIN) is unknown. Even in cases where the whole network is known relatively accurately, it is typically very large and...Go to contribution page
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Silvio Franz (University of Paris-Sud)08/09/2011, 11:00
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Sara A. Solla08/09/2011, 12:00Coupling large numbers of relatively simple elements often results in networks with complex computational abilities. Examples abound in biological systems - from genetic to neural networks, from metabolic networks to immune systems, from networks of proteins to networks of economic and social agents. Recent and continuing increases in the experimental ability to simultaneously...Go to contribution page
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Prof. Sara Solla (Northwestern University)
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Wilson Truccolo (Brown University)
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Prof. Riccardo Zecchina (Politecnico di Torino)
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Yasser Roudi (Nordita)
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Prof. Marc Mezard (Universite de Paris Sud)
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Michael Biehl (University of Groningen)
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Mikko Alava (HUT, Espoo, Finland)
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Peter Sollich (Kings College London)
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Sara Solla (Northwestern University)
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