Dr
Matteo Marsili
(Abdus Salam ICTP)
05/09/2011, 09:30
Advanced 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...
Gasper Tkacik
(IST, Austria)
05/09/2011, 12:00
In 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...
Peter Latham
(University College London)
05/09/2011, 14:00
The 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;...
Sara A. Solla
(Northwestern University)
05/09/2011, 15:30
Generalized 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 ...
Enzo Marinari
(University of Rome La Sapienza)
06/09/2011, 09:30
The 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...
David Saad
(Aston University Birmingham)
06/09/2011, 12:00
Not 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...
Bert Kappen
(Radboud University)
06/09/2011, 14:00
In 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...
Prof.
Riccardo Zecchina
(Politecnico di Torino)
06/09/2011, 15:30
Michael Biehl
(University of Groningen)
07/09/2011, 09:30
An 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...
Manfred Opper
(TU Berlin)
07/09/2011, 12:00
Continuous 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...
Prof.
Marc Mezard
(Universite de Paris Sud)
07/09/2011, 14:00
HongLi Zeng
(Aalto University)
07/09/2011, 16:30
In 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...
Hamed Mahmoudi
(Aalto University)
07/09/2011, 16:50
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...
Aree Witoelar
(Kavli Inst./NTNU)
07/09/2011, 17:10
Peter Sollich
(Kings College London)
08/09/2011, 09:30
In 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...
Silvio Franz
(University of Paris-Sud)
08/09/2011, 11:00
