13,750 research outputs found
Discovering Functional Communities in Dynamical Networks
Many networks are important because they are substrates for dynamical
systems, and their pattern of functional connectivity can itself be dynamic --
they can functionally reorganize, even if their underlying anatomical structure
remains fixed. However, the recent rapid progress in discovering the community
structure of networks has overwhelmingly focused on that constant anatomical
connectivity. In this paper, we lay out the problem of discovering_functional
communities_, and describe an approach to doing so. This method combines recent
work on measuring information sharing across stochastic networks with an
existing and successful community-discovery algorithm for weighted networks. We
illustrate it with an application to a large biophysical model of the
transition from beta to gamma rhythms in the hippocampus.Comment: 18 pages, 4 figures, Springer "Lecture Notes in Computer Science"
style. Forthcoming in the proceedings of the workshop "Statistical Network
Analysis: Models, Issues and New Directions", at ICML 2006. Version 2: small
clarifications, typo corrections, added referenc
Discovering Brain Mechanisms Using Network Analysis and Causal Modeling
Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help in addressing it
Temporal Networks
A great variety of systems in nature, society and technology -- from the web
of sexual contacts to the Internet, from the nervous system to power grids --
can be modeled as graphs of vertices coupled by edges. The network structure,
describing how the graph is wired, helps us understand, predict and optimize
the behavior of dynamical systems. In many cases, however, the edges are not
continuously active. As an example, in networks of communication via email,
text messages, or phone calls, edges represent sequences of instantaneous or
practically instantaneous contacts. In some cases, edges are active for
non-negligible periods of time: e.g., the proximity patterns of inpatients at
hospitals can be represented by a graph where an edge between two individuals
is on throughout the time they are at the same ward. Like network topology, the
temporal structure of edge activations can affect dynamics of systems
interacting through the network, from disease contagion on the network of
patients to information diffusion over an e-mail network. In this review, we
present the emergent field of temporal networks, and discuss methods for
analyzing topological and temporal structure and models for elucidating their
relation to the behavior of dynamical systems. In the light of traditional
network theory, one can see this framework as moving the information of when
things happen from the dynamical system on the network, to the network itself.
Since fundamental properties, such as the transitivity of edges, do not
necessarily hold in temporal networks, many of these methods need to be quite
different from those for static networks
Wiring optimization explanation in neuroscience: What is Special about it?
This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system
Metrics for Graph Comparison: A Practitioner's Guide
Comparison of graph structure is a ubiquitous task in data analysis and
machine learning, with diverse applications in fields such as neuroscience,
cyber security, social network analysis, and bioinformatics, among others.
Discovery and comparison of structures such as modular communities, rich clubs,
hubs, and trees in data in these fields yields insight into the generative
mechanisms and functional properties of the graph.
Often, two graphs are compared via a pairwise distance measure, with a small
distance indicating structural similarity and vice versa. Common choices
include spectral distances (also known as distances) and distances
based on node affinities. However, there has of yet been no comparative study
of the efficacy of these distance measures in discerning between common graph
topologies and different structural scales.
In this work, we compare commonly used graph metrics and distance measures,
and demonstrate their ability to discern between common topological features
found in both random graph models and empirical datasets. We put forward a
multi-scale picture of graph structure, in which the effect of global and local
structure upon the distance measures is considered. We make recommendations on
the applicability of different distance measures to empirical graph data
problem based on this multi-scale view. Finally, we introduce the Python
library NetComp which implements the graph distances used in this work
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
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