264 research outputs found
Mutual information in random Boolean models of regulatory networks
The amount of mutual information contained in time series of two elements
gives a measure of how well their activities are coordinated. In a large,
complex network of interacting elements, such as a genetic regulatory network
within a cell, the average of the mutual information over all pairs is a
global measure of how well the system can coordinate its internal dynamics. We
study this average pairwise mutual information in random Boolean networks
(RBNs) as a function of the distribution of Boolean rules implemented at each
element, assuming that the links in the network are randomly placed. Efficient
numerical methods for calculating show that as the number of network nodes
N approaches infinity, the quantity N exhibits a discontinuity at parameter
values corresponding to critical RBNs. For finite systems it peaks near the
critical value, but slightly in the disordered regime for typical parameter
variations. The source of high values of N is the indirect correlations
between pairs of elements from different long chains with a common starting
point. The contribution from pairs that are directly linked approaches zero for
critical networks and peaks deep in the disordered regime.Comment: 11 pages, 6 figures; Minor revisions for clarity and figure format,
one reference adde
Double Deep-Q Learning-Based Output Tracking of Probabilistic Boolean Control Networks
In this article, a reinforcement learning (RL)-based scalable technique is presented to control the probabilistic Boolean control networks (PBCNs). In particular, a double deep- network (DD ) approach is firstly proposed to address the output tracking problem of PBCNs, and optimal state feedback controllers are obtained such that the output of PBCNs tracks a constant as well as a time-varying reference signal. The presented method is model-free and offers scalability, thereby provides an efficient way to control large-scale PBCNs that are a natural choice to model gene regulatory networks (GRNs). Finally, three PBCN models of GRNs including a 16-gene and 28-gene networks are considered to verify the presented results
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
Exploiting the theory of state space models, we derive the exact expressions
of the information transfer, as well as redundant and synergistic transfer, for
coupled Gaussian processes observed at multiple temporal scales. All of the
terms, constituting the frameworks known as interaction information
decomposition and partial information decomposition, can thus be analytically
obtained for different time scales from the parameters of the VAR model that
fits the processes. We report the application of the proposed methodology
firstly to benchmark Gaussian systems, showing that this class of systems may
generate patterns of information decomposition characterized by mainly
redundant or synergistic information transfer persisting across multiple time
scales or even by the alternating prevalence of redundant and synergistic
source interaction depending on the time scale. Then, we apply our method to an
important topic in neuroscience, i.e., the detection of causal interactions in
human epilepsy networks, for which we show the relevance of partial information
decomposition to the detection of multiscale information transfer spreading
from the seizure onset zone
The effect of development on spatial pattern separation in the hippocampus as quantified by the Homer1a immediate-early gene
ix, 51 leaves : ill. ; 29 cmThis study sought to determine whether the DG, CA3, and CA1 regions contain
uniformly excitable populations and test the hypothesis that rapid addition of new, more
excitable, granule cells in prepubescence results in a low activation probability (P1) in the
DG. The immediate-early gene Homer1a was used as a neural activity marker to quantify
activation in juvenile (P28) and adult (~5 mo) rats during track running. The main finding
was that P1 in juveniles was substantially lower not only the DG, but also CA3 and CA1.
The P1 for a DG granule cell was close to 0 in juveniles, versus 0.58 in adults. The low P1
in juveniles indicates that sparse, but non-overlapping, subpopulations participate in
encoding events. Since sparse, orthogonal coding enhances a network’s ability to
decorrelate input patterns (Marr, 1971; McNaughton & Morris, 1987), the findings
suggest that juveniles likely possess greatly enhanced pattern separation ability
Immediate-early gene Homer1a intranuclear transcription focus intensity as a measure of relative neural activation
Although immediate-early gene expression analyses using fluorescent in situ hybridization is an effective method to identify recently activated neurons; and non-Boolean variations in transcription foci have been documented, it remains unclear whether there is a systematic relationship between magnitude of neural activation and corresponding RNA signal. Here, we quantified the Homer1a response of hippocampal neurons in rats that ran laps on a familiar track to induce consistent cell firing. A strong linear trend (r2 > 0.9) in INF intensity (brightness) was observed between 1 and 25 laps, after which INF signal dispersed within the nucleus. When the integrated intranuclear fluorescence was considered instead, the linear relationship extended to 50 laps. But there was only an approximate doubling of Homer1a RNA detected for this 50-fold variation in total spiking. Thus, this low-gain dynamic range likely precludes INF intensity as a precise quantitative readout of neural activation, albeit a useful qualitative tool.Alberta Innovates - Health Solutions: Polaris Award
Natural Sciences and Engineering Research Council of Canada: RGPIN-2017-03857
National Science Foundation: 163146
Computer Aided Verification
This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency
Computer Aided Verification
This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications
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