625 research outputs found
Statistical pairwise interaction model of stock market
Financial markets are a classical example of complex systems as they comprise
many interacting stocks. As such, we can obtain a surprisingly good description
of their structure by making the rough simplification of binary daily returns.
Spin glass models have been applied and gave some valuable results but at the
price of restrictive assumptions on the market dynamics or others are
agent-based models with rules designed in order to recover some empirical
behaviours. Here we show that the pairwise model is actually a statistically
consistent model with observed first and second moments of the stocks
orientation without making such restrictive assumptions. This is done with an
approach based only on empirical data of price returns. Our data analysis of
six major indices suggests that the actual interaction structure may be thought
as an Ising model on a complex network with interaction strengths scaling as
the inverse of the system size. This has potentially important implications
since many properties of such a model are already known and some techniques of
the spin glass theory can be straightforwardly applied. Typical behaviours, as
multiple equilibria or metastable states, different characteristic time scales,
spatial patterns, order-disorder, could find an explanation in this picture.Comment: 11 pages, 8 figure
Redundant variables and Granger causality
We discuss the use of multivariate Granger causality in presence of redundant
variables: the application of the standard analysis, in this case, leads to
under-estimation of causalities. Using the un-normalized version of the
causality index, we quantitatively develop the notions of redundancy and
synergy in the frame of causality and propose two approaches to group redundant
variables: (i) for a given target, the remaining variables are grouped so as to
maximize the total causality and (ii) the whole set of variables is partitioned
to maximize the sum of the causalities between subsets. We show the application
to a real neurological experiment, aiming to a deeper understanding of the
physiological basis of abnormal neuronal oscillations in the migraine brain.
The outcome by our approach reveals the change in the informational pattern due
to repetitive transcranial magnetic stimulations.Comment: 4 pages, 5 figures. Accepted for publication in Physical Review
Retinal metric: a stimulus distance measure derived from population neural responses
The ability of the organism to distinguish between various stimuli is limited
by the structure and noise in the population code of its sensory neurons. Here
we infer a distance measure on the stimulus space directly from the recorded
activity of 100 neurons in the salamander retina. In contrast to previously
used measures of stimulus similarity, this "neural metric" tells us how
distinguishable a pair of stimulus clips is to the retina, given the noise in
the neural population response. We show that the retinal distance strongly
deviates from Euclidean, or any static metric, yet has a simple structure: we
identify the stimulus features that the neural population is jointly sensitive
to, and show the SVM-like kernel function relating the stimulus and neural
response spaces. We show that the non-Euclidean nature of the retinal distance
has important consequences for neural decoding.Comment: 5 pages, 4 figures, to appear in Phys Rev Let
Neuronal assembly dynamics in supervised and unsupervised learning scenarios
The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions
Expanding the Transfer Entropy to Identify Information Subgraphs in Complex Systems
We propose a formal expansion of the transfer entropy to put in evidence
irreducible sets of variables which provide information for the future state of
each assigned target. Multiplets characterized by a large contribution to the
expansion are associated to informational circuits present in the system, with
an informational character which can be associated to the sign of the
contribution. For the sake of computational complexity, we adopt the assumption
of Gaussianity and use the corresponding exact formula for the conditional
mutual information. We report the application of the proposed methodology on
two EEG data sets
Regional Initiatives in Support of Surveillance in East Africa: The East Africa Integrated Disease Surveillance Network (EAIDSNet) Experience.
The East African Integrated Disease Surveillance Network (EAIDSNet) was formed in response to a growing frequency of cross-border malaria outbreaks in the 1990s and a growing recognition that fragmented disease interventions, coupled with weak laboratory capacity, were making it difficult to respond in a timely manner to the outbreaks of malaria and other infectious diseases. The East Africa Community (EAC) partner states, with financial support from the Rockefeller Foundation, established EAIDSNet in 2000 to develop and strengthen the communication channels necessary for integrated cross-border disease surveillance and control efforts. The objective of this paper is to review the regional EAIDSNet initiative and highlight achievements and challenges in its implementation. Major accomplishments of EAIDSNet include influencing the establishment of a Department of Health within the EAC Secretariat to support a regional health agenda; successfully completing a regional field simulation exercise in pandemic influenza preparedness; and piloting a web-based portal for linking animal and human health disease surveillance. The strategic direction of EAIDSNet was shaped, in part, by lessons learned following a visit to the more established Mekong Basin Disease Surveillance (MBDS) regional network. Looking to the future, EAIDSNet is collaborating with the East, Central and Southern Africa Health Community (ECSA-HC), EAC partner states, and the World Health Organization to implement the World Bank-funded East Africa Public Health Laboratory Networking Project (EAPHLNP). The network has also begun lobbying East African countries for funding to support EAIDSNet activities
Detection of subthreshold pulses in neurons with channel noise
Neurons are subject to various kinds of noise. In addition to synaptic noise,
the stochastic opening and closing of ion channels represents an intrinsic
source of noise that affects the signal processing properties of the neuron. In
this paper, we studied the response of a stochastic Hodgkin-Huxley neuron to
transient input subthreshold pulses. It was found that the average response
time decreases but variance increases as the amplitude of channel noise
increases. In the case of single pulse detection, we show that channel noise
enables one neuron to detect the subthreshold signals and an optimal membrane
area (or channel noise intensity) exists for a single neuron to achieve optimal
performance. However, the detection ability of a single neuron is limited by
large errors. Here, we test a simple neuronal network that can enhance the
pulse detecting abilities of neurons and find dozens of neurons can perfectly
detect subthreshold pulses. The phenomenon of intrinsic stochastic resonance is
also found both at the level of single neurons and at the level of networks. At
the network level, the detection ability of networks can be optimized for the
number of neurons comprising the network.Comment: 14 pages, 9 figure
FireDock: a web server for fast interaction refinement in molecular docking†
Structural details of protein–protein interactions are invaluable for understanding and deciphering biological mechanisms. Computational docking methods aim to predict the structure of a protein–protein complex given the structures of its single components. Protein flexibility and the absence of robust scoring functions pose a great challenge in the docking field. Due to these difficulties most of the docking methods involve a two-tier approach: coarse global search for feasible orientations that treats proteins as rigid bodies, followed by an accurate refinement stage that aims to introduce flexibility into the process. The FireDock web server, presented here, is the first web server for flexible refinement and scoring of protein–protein docking solutions. It includes optimization of side-chain conformations and rigid-body orientation and allows a high-throughput refinement. The server provides a user-friendly interface and a 3D visualization of the results. A docking protocol consisting of a global search by PatchDock and a refinement by FireDock was extensively tested. The protocol was successful in refining and scoring docking solution candidates for cases taken from docking benchmarks. We provide an option for using this protocol by automatic redirection of PatchDock candidate solutions to the FireDock web server for refinement. The FireDock web server is available at http://bioinfo3d.cs.tau.ac.il/FireDock/
Grouping time series by pairwise measures of redundancy
A novel approach is proposed to group redundant time series in the frame of
causality. It assumes that (i) the dynamics of the system can be described
using just a small number of characteristic modes, and that (ii) a pairwise
measure of redundancy is sufficient to elicit the presence of correlated
degrees of freedom. We show the application of the proposed approach on fMRI
data from a resting human brain and gene expression profiles from HeLa cell
culture.Comment: 4 pages, 8 figure
Weak pairwise correlations imply strongly correlated network states in a neural population
Biological networks have so many possible states that exhaustive sampling is
impossible. Successful analysis thus depends on simplifying hypotheses, but
experiments on many systems hint that complicated, higher order interactions
among large groups of elements play an important role. In the vertebrate
retina, we show that weak correlations between pairs of neurons coexist with
strongly collective behavior in the responses of ten or more neurons.
Surprisingly, we find that this collective behavior is described quantitatively
by models that capture the observed pairwise correlations but assume no higher
order interactions. These maximum entropy models are equivalent to Ising
models, and predict that larger networks are completely dominated by
correlation effects. This suggests that the neural code has associative or
error-correcting properties, and we provide preliminary evidence for such
behavior. As a first test for the generality of these ideas, we show that
similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and
Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah
(http://cosyne.org
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