8,900 research outputs found
A geometric network model of intrinsic grey-matter connectivity of the human brain
Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuro- science is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct âshortcutsâ through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections
Spreading dynamics on spatially constrained complex brain networks
The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way
Brain architecture: A design for natural computation
Fifty years ago, John von Neumann compared the architecture of the brain with
that of computers that he invented and which is still in use today. In those
days, the organisation of computers was based on concepts of brain
organisation. Here, we give an update on current results on the global
organisation of neural systems. For neural systems, we outline how the spatial
and topological architecture of neuronal and cortical networks facilitates
robustness against failures, fast processing, and balanced network activation.
Finally, we discuss mechanisms of self-organization for such architectures.
After all, the organization of the brain might again inspire computer
architecture
Nonlinear signaling on biological networks: the role of stochasticity and spectral clustering
Signal transduction within biological cells is governed by networks of
interacting proteins. Communication between these proteins is mediated by
signaling molecules which bind to receptors and induce stochastic transitions
between different conformational states. Signaling is typically a cooperative
process which requires the occurrence of multiple binding events so that
reaction rates have a nonlinear dependence on the amount of signaling molecule.
It is this nonlinearity that endows biological signaling networks with robust
switch-like properties which are critical to their biological function. In this
study, we investigate how the properties of these signaling systems depend on
the network architecture. Our main result is that these nonlinear networks
exhibit bistability where the network activity can switch between states that
correspond to a low and high activity level. We show that this bistable regime
emerges at a critical coupling strength that is determined by the spectral
structure of the network. In particular, the set of nodes that correspond to
large components of the leading eigenvector of the adjacency matrix determines
the onset of bistability. Above this transition, the eigenvectors of the
adjacency matrix determine a hierarchy of clusters, defined by its spectral
properties, which are activated sequentially with increasing network activity.
We argue further that the onset of bistability occurs either continuously or
discontinuously depending upon whether the leading eigenvector is localized or
delocalized. Finally, we show that at low network coupling stochastic
transitions to the active branch are also driven by the set of nodes that
contribute more strongly to the leading eigenvector.Comment: 30 pages, 12 figure
Optimal modularity and memory capacity of neural reservoirs
The neural network is a powerful computing framework that has been exploited
by biological evolution and by humans for solving diverse problems. Although
the computational capabilities of neural networks are determined by their
structure, the current understanding of the relationships between a neural
network's architecture and function is still primitive. Here we reveal that
neural network's modular architecture plays a vital role in determining the
neural dynamics and memory performance of the network of threshold neurons. In
particular, we demonstrate that there exists an optimal modularity for memory
performance, where a balance between local cohesion and global connectivity is
established, allowing optimally modular networks to remember longer. Our
results suggest that insights from dynamical analysis of neural networks and
information spreading processes can be leveraged to better design neural
networks and may shed light on the brain's modular organization
Decelerated spreading in degree-correlated networks
While degree correlations are known to play a crucial role for spreading
phenomena in networks, their impact on the propagation speed has hardly been
understood. Here we investigate a tunable spreading model on scale-free
networks and show that the propagation becomes slow in positively (negatively)
correlated networks if nodes with a high connectivity locally accelerate
(decelerate) the propagation. Examining the efficient paths offers a coherent
explanation for this result, while the -core decomposition reveals the
dependence of the nodal spreading efficiency on the correlation. Our findings
should open new pathways to delicately control real-world spreading processes
Griffiths phases in infinite-dimensional, non-hierarchical modular networks
Griffiths phases (GPs), generated by the heterogeneities on modular networks,
have recently been suggested to provide a mechanism, rid of fine parameter
tuning, to explain the critical behavior of complex systems. One conjectured
requirement for systems with modular structures was that the network of modules
must be hierarchically organized and possess finite dimension. We investigate
the dynamical behavior of an activity spreading model, evolving on
heterogeneous random networks with highly modular structure and organized
non-hierarchically. We observe that loosely coupled modules act as effective
rare-regions, slowing down the extinction of activation. As a consequence, we
find extended control parameter regions with continuously changing dynamical
exponents for single network realizations, preserved after finite size
analyses, as in a real GP. The avalanche size distributions of spreading events
exhibit robust power-law tails. Our findings relax the requirement of
hierarchical organization of the modular structure, which can help to
rationalize the criticality of modular systems in the framework of GPs.Comment: 14 pages, 8 figure
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