93 research outputs found
Source coding by efficient selection of ground states clusters
In this letter, we show how the Survey Propagation algorithm can be
generalized to include external forcing messages, and used to address
selectively an exponential number of glassy ground states. These capabilities
can be used to explore efficiently the space of solutions of random NP-complete
constraint satisfaction problems, providing a direct experimental evidence of
replica symmetry breaking in large-size instances. Finally, a new lossy data
compression protocol is introduced, exploiting as a computational resource the
clustered nature of the space of addressable states.Comment: 4 pages, 4 figure
Directed Flow of Information in Chimera States
We investigated interactions within chimera states in a phase oscillator
network with two coupled subpopulations. To quantify interactions within and
between these subpopulations, we estimated the corresponding (delayed) mutual
information that -- in general -- quantifies the capacity or the maximum rate
at which information can be transferred to recover a sender's information at
the receiver with a vanishingly low error probability. After verifying their
equivalence with estimates based on the continuous phase data, we determined
the mutual information using the time points at which the individual phases
passed through their respective Poincar\'{e} sections. This stroboscopic view
on the dynamics may resemble, e.g., neural spike times, that are common
observables in the study of neuronal information transfer. This discretization
also increased processing speed significantly, rendering it particularly
suitable for a fine-grained analysis of the effects of experimental and model
parameters. In our model, the delayed mutual information within each
subpopulation peaked at zero delay, whereas between the subpopulations it was
always maximal at non-zero delay, irrespective of parameter choices. We
observed that the delayed mutual information of the desynchronized
subpopulation preceded the synchronized subpopulation. Put differently, the
oscillators of the desynchronized subpopulation were 'driving' the ones in the
synchronized subpopulation. These findings were also observed when estimating
mutual information of the full phase trajectories. We can thus conclude that
the delayed mutual information of discrete time points allows for inferring a
functional directed flow of information between subpopulations of coupled phase
oscillators
Functional connectivity and neuronal dynamics: insights from computational methods
International audienceBrain functions rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying flexible information routing are still, however, largely unknown. Here, we hypothesize that the emergence of a multiplicity of possible information routing patterns is due to the richness of the complex dynamics that can be supported by an underlying structural network. Analyses of circuit computational models of interacting brain areas suggest that different dynamical states associated with a given connectome mechanistically implement different information routing patterns between system's components. As a result, a fast, network-wide and self-organized reconfiguration of information routing patterns-and Functional Connectivity networks, seen as their proxy-can be achieved by inducing a transition between the available intrinsic dynamical states. We present here a survey of theoretical and modelling results, as well as of sophisticated metrics of Functional Connectivity which are compliant with the daunting task of characterizing dynamic routing from neural data. Theory: Function follows dynamics, rather than structure Neuronal activity conveys information, but which target should this information be-pushed‖ to, or which source should new information be-pulled‖ from? The problem of dynamic information routing is ubiquitous in a distributed information processing system as the brain. Brain functions in general require the control of distributed networks of interregional communication on fast timescales compliant with behavior, but incompatible with plastic modifications of connectivity tracts (Bressler & Kelso, 2001; Varela et al., 2001). This argument led to notions of connectivity based on information exchange-or more generically, an-interaction‖-between brain regions or neuronal populations, rather than based on the underlying STRUCTURAL CONNECTIVITY (SC, i.e. anatomic). An entire zoo of data-driven metrics has been introduced in the literature and this chapter will review some of them. Notwithstanding, they track simple correlation, or directed causal influence (Friston, 2011) or information transfer (Wibral et al., 2014) between time-series of activity. Thes
Model-free reconstruction of neuronal network connectivity from calcium imaging signals
A systematic assessment of global neural network connectivity through direct
electrophysiological assays has remained technically unfeasible even in
dissociated neuronal cultures. We introduce an improved algorithmic approach
based on Transfer Entropy to reconstruct approximations to network structural
connectivities from network activity monitored through calcium fluorescence
imaging. Based on information theory, our method requires no prior assumptions
on the statistics of neuronal firing and neuronal connections. The performance
of our algorithm is benchmarked on surrogate time-series of calcium
fluorescence generated by the simulated dynamics of a network with known
ground-truth topology. We find that the effective network topology revealed by
Transfer Entropy depends qualitatively on the time-dependent dynamic state of
the network (e.g., bursting or non-bursting). We thus demonstrate how
conditioning with respect to the global mean activity improves the performance
of our method. [...] Compared to other reconstruction strategies such as
cross-correlation or Granger Causality methods, our method based on improved
Transfer Entropy is remarkably more accurate. In particular, it provides a good
reconstruction of the network clustering coefficient, allowing to discriminate
between weakly or strongly clustered topologies, whereas on the other hand an
approach based on cross-correlations would invariantly detect artificially high
levels of clustering. Finally, we present the applicability of our method to
real recordings of in vitro cortical cultures. We demonstrate that these
networks are characterized by an elevated level of clustering compared to a
random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted
for publicatio
ARE THERE DISCRETE GAMMA SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL LEARNING ?
International audienc
Gender bias in scholarly peer review
Abstract Peer review is the cornerstone of scholarly publishing and it is essential that peer reviewers are appointed on the basis of their expertise alone. However, it is difficult to check for any bias in the peer-review process because the identity of peer reviewers generally remains confidential. Here, using public information about the identities of 9000 editors and 43000 reviewers from the Frontiers series of journals, we show that women are underrepresented in the peer-review process, that editors of both genders operate with substantial same-gender preference (homophily), and that the mechanisms of this homophily are gender-dependent. We also show that homophily will persist even if numerical parity between genders is reached, highlighting the need for increased efforts to combat subtler forms of gender bias in scholarly publishing
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