14,474 research outputs found
A mechanistic model of connector hubs, modularity, and cognition
The human brain network is modular--comprised of communities of tightly
interconnected nodes. This network contains local hubs, which have many
connections within their own communities, and connector hubs, which have
connections diversely distributed across communities. A mechanistic
understanding of these hubs and how they support cognition has not been
demonstrated. Here, we leveraged individual differences in hub connectivity and
cognition. We show that a model of hub connectivity accurately predicts the
cognitive performance of 476 individuals in four distinct tasks. Moreover,
there is a general optimal network structure for cognitive
performance--individuals with diversely connected hubs and consequent modular
brain networks exhibit increased cognitive performance, regardless of the task.
Critically, we find evidence consistent with a mechanistic model in which
connector hubs tune the connectivity of their neighbors to be more modular
while allowing for task appropriate information integration across communities,
which increases global modularity and cognitive performance
Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach
This work analyzes the problem of community structure in real-world networks
based on the synchronization of nonidentical coupled chaotic R\"{o}ssler
oscillators each one characterized by a defined natural frequency, and coupled
according to a predefined network topology. The interaction scheme contemplates
an uniformly increasing coupling force to simulate a society in which the
association between the agents grows in time. To enhance the stability of the
correlated states that could emerge from the synchronization process, we
propose a parameterless mechanism that adapts the characteristic frequencies of
coupled oscillators according to a dynamic connectivity matrix deduced from
correlated data. We show that the characteristic frequency vector that results
from the adaptation mechanism reveals the underlying community structure
present in the network.Comment: 21 pages, 7 figures; Chaos, Solitons & Fractals (2012
Session 5: Development, Neuroscience and Evolutionary Psychology
Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 5: Development, Neuroscience and Evolutionary Psycholog
Conserved but flexible modularity in the zebrafish skull: implications for craniofacial evolvability
Morphological variation is the outward manifestation of development and provides fodder for adaptive evolution. Because of this contingency, evolution is often thought to be biased by developmental processes and functional interactions among structures, which are statistically detectable through forms of covariance among traits. This can take the form of substructures of integrated traits, termed modules, which together comprise patterns of variational modularity. While modularity is essential to an understanding of evolutionary potential, biologists currently have little understanding of its genetic basis and its temporal dynamics over generations. To address these open questions, we compared patterns of craniofacial modularity among laboratory strains, defined mutant lines and a wild population of zebrafish ( ). Our findings suggest that relatively simple genetic changes can have profound effects on covariance, without greatly affecting craniofacial shape. Moreover, we show that instead of completely deconstructing the covariance structure among sets of traits, mutations cause shifts among seemingly latent patterns of modularity suggesting that the skull may be predisposed towards a limited number of phenotypes. This new insight may serve to greatly increase the evolvability of a population by providing a range of 'preset' patterns of modularity that can appear readily and allow for rapid evolution
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
Hierarchical modularity in human brain functional networks
The idea that complex systems have a hierarchical modular organization
originates in the early 1960s and has recently attracted fresh support from
quantitative studies of large scale, real-life networks. Here we investigate
the hierarchical modular (or "modules-within-modules") decomposition of human
brain functional networks, measured using functional magnetic resonance imaging
(fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a
customized template to extract networks with more than 1800 regional nodes, and
we applied a fast algorithm to identify nested modular structure at several
hierarchical levels. We used mutual information, 0 < I < 1, to estimate the
similarity of community structure of networks in different subjects, and to
identify the individual network that is most representative of the group.
Results show that human brain functional networks have a hierarchical modular
organization with a fair degree of similarity between subjects, I=0.63. The
largest 5 modules at the highest level of the hierarchy were medial occipital,
lateral occipital, central, parieto-frontal and fronto-temporal systems;
occipital modules demonstrated less sub-modular organization than modules
comprising regions of multimodal association cortex. Connector nodes and hubs,
with a key role in inter-modular connectivity, were also concentrated in
association cortical areas. We conclude that methods are available for
hierarchical modular decomposition of large numbers of high resolution brain
functional networks using computationally expedient algorithms. This could
enable future investigations of Simon's original hypothesis that hierarchy or
near-decomposability of physical symbol systems is a critical design feature
for their fast adaptivity to changing environmental conditions
Duplication of modules facilitates the evolution of functional specialization
The evolution of simulated robots with three different architectures is studied. We compared a non-modular feed forward network, a hardwired modular and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state
Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
Copyright ©2014 Zhong, Cangelosi and Wermter.This is an open-access article distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsThe acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.Peer reviewedFinal Published versio
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