31,576 research outputs found
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
Structural Properties of the Caenorhabditis elegans Neuronal Network
Despite recent interest in reconstructing neuronal networks, complete wiring
diagrams on the level of individual synapses remain scarce and the insights
into function they can provide remain unclear. Even for Caenorhabditis elegans,
whose neuronal network is relatively small and stereotypical from animal to
animal, published wiring diagrams are neither accurate nor complete and
self-consistent. Using materials from White et al. and new electron micrographs
we assemble whole, self-consistent gap junction and chemical synapse networks
of hermaphrodite C. elegans. We propose a method to visualize the wiring
diagram, which reflects network signal flow. We calculate statistical and
topological properties of the network, such as degree distributions, synaptic
multiplicities, and small-world properties, that help in understanding network
signal propagation. We identify neurons that may play central roles in
information processing and network motifs that could serve as functional
modules of the network. We explore propagation of neuronal activity in response
to sensory or artificial stimulation using linear systems theory and find
several activity patterns that could serve as substrates of previously
described behaviors. Finally, we analyze the interaction between the gap
junction and the chemical synapse networks. Since several statistical
properties of the C. elegans network, such as multiplicity and motif
distributions are similar to those found in mammalian neocortex, they likely
point to general principles of neuronal networks. The wiring diagram reported
here can help in understanding the mechanistic basis of behavior by generating
predictions about future experiments involving genetic perturbations, laser
ablations, or monitoring propagation of neuronal activity in response to
stimulation
Topological exploration of artificial neuronal network dynamics
One of the paramount challenges in neuroscience is to understand the dynamics
of individual neurons and how they give rise to network dynamics when
interconnected. Historically, researchers have resorted to graph theory,
statistics, and statistical mechanics to describe the spatiotemporal structure
of such network dynamics. Our novel approach employs tools from algebraic
topology to characterize the global properties of network structure and
dynamics.
We propose a method based on persistent homology to automatically classify
network dynamics using topological features of spaces built from various
spike-train distances. We investigate the efficacy of our method by simulating
activity in three small artificial neural networks with different sets of
parameters, giving rise to dynamics that can be classified into four regimes.
We then compute three measures of spike train similarity and use persistent
homology to extract topological features that are fundamentally different from
those used in traditional methods. Our results show that a machine learning
classifier trained on these features can accurately predict the regime of the
network it was trained on and also generalize to other networks that were not
presented during training. Moreover, we demonstrate that using features
extracted from multiple spike-train distances systematically improves the
performance of our method
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
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