37 research outputs found
Evolution and development of Brain Networks: From Caenorhabditis elegans to Homo sapiens
Neural networks show a progressive increase in complexity during the time
course of evolution. From diffuse nerve nets in Cnidaria to modular,
hierarchical systems in macaque and humans, there is a gradual shift from
simple processes involving a limited amount of tasks and modalities to complex
functional and behavioral processing integrating different kinds of information
from highly specialized tissue. However, studies in a range of species suggest
that fundamental similarities, in spatial and topological features as well as
in developmental mechanisms for network formation, are retained across
evolution. 'Small-world' topology and highly connected regions (hubs) are
prevalent across the evolutionary scale, ensuring efficient processing and
resilience to internal (e.g. lesions) and external (e.g. environment) changes.
Furthermore, in most species, even the establishment of hubs, long-range
connections linking distant components, and a modular organization, relies on
similar mechanisms. In conclusion, evolutionary divergence leads to greater
complexity while following essential developmental constraints
From modular to centralized organization of synchronization in functional areas of the cat cerebral cortex
Recent studies have pointed out the importance of transient synchronization
between widely distributed neural assemblies to understand conscious
perception. These neural assemblies form intricate networks of neurons and
synapses whose detailed map for mammals is still unknown and far from our
experimental capabilities. Only in a few cases, for example the C. elegans, we
know the complete mapping of the neuronal tissue or its mesoscopic level of
description provided by cortical areas. Here we study the process of transient
and global synchronization using a simple model of phase-coupled oscillators
assigned to cortical areas in the cerebral cat cortex. Our results highlight
the impact of the topological connectivity in the developing of
synchronization, revealing a transition in the synchronization organization
that goes from a modular decentralized coherence to a centralized synchronized
regime controlled by a few cortical areas forming a Rich-Club connectivity
pattern.Comment: 24 pages, 8 figures. Final version published in PLoS On
Perspectives on the Neuroscience of Cognition and Consciousness
The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness
Stages of neuronal network formation
Graph theoretical approaches have become a powerful tool for
investigating the architecture and dynamics of complex networks. The topology
of network graphs revealed small-world properties for very different real
systems among these neuronal networks. In this study, we observed the early
development of mouse retinal ganglion cell (RGC) networks in vitro using timelapse
video microscopy. By means of a time-resolved graph theoretical analysis
of the connectivity, shortest path length and the edge length, we were able to
discover the different stages during the network formation. Starting from single
cells, at the first stage neurons connected to each other ending up in a network
with maximum complexity. In the further course, we observed a simplification of
the network which manifested in a change of relevant network parameters such
as the minimization of the path length. Moreover, we found that RGC networks
self-organized as small-world networks at both stages; however, the optimization
occurred only in the second stage
Mapping Human Whole-Brain Structural Networks with Diffusion MRI
Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world
Chimera-like states in modular neural networks
Chimera states, namely the coexistence of coherent and incoherent behavior, were previously analyzed in complex networks. However, they have not been extensively studied in modular networks. Here, we consider a neural network inspired by the connectome of the C. elegans soil worm, organized into six interconnected communities, where neurons obey chaotic bursting dynamics. Neurons are assumed to be connected with electrical synapses within their communities and with chemical synapses across them. As our numerical simulations reveal, the coaction of these two types of coupling can shape the dynamics in such a way that chimera-like states can happen. They consist of a fraction of synchronized neurons which belong to the larger communities, and a fraction of desynchronized neurons which are part of smaller communities. In addition to the Kuramoto order parameter ?, we also employ other measures of coherence, such as the chimera-like ? and metastability ? indices, which quantify the degree of synchronization among communities and along time, respectively. We perform the same analysis for networks that share common features with the C. elegans neural network. Similar results suggest that under certain assumptions, chimera-like states are prominent phenomena in modular networks, and might provide insight for the behavior of more complex modular networks
Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome
The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors