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An Optimized Structure-Function Design Principle Underlies Efficient Signaling Dynamics in Neurons.
Dynamic signaling on branching axons is critical for rapid and efficient communication between neurons in the brain. Efficient signaling in axon arbors depends on a trade-off between the time it takes action potentials to reach synaptic terminals (temporal cost) and the amount of cellular material associated with the wiring path length of the neuron's morphology (material cost). However, where the balance between structural and dynamical considerations for achieving signaling efficiency is, and the design principle that neurons optimize to preserve this balance, is still elusive. In this work, we introduce a novel analysis that compares morphology and signaling dynamics in axonal networks to address this open problem. We show that in Basket cell neurons the design principle being optimized is the ratio between the refractory period of the membrane, and action potential latencies between the initial segment and the synaptic terminals. Our results suggest that the convoluted paths taken by axons reflect a design compensation by the neuron to slow down signaling latencies in order to optimize this ratio. Deviations in this ratio may result in a breakdown of signaling efficiency in the cell. These results pave the way to new approaches for investigating more complex neurophysiological phenomena that involve considerations of neuronal structure-function relationships
Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow
We review modeling of astrocyte ion dynamics with a specific focus on the
implications of so-called spatial potassium buffering, where excess potassium
in the extracellular space (ECS) is transported away to prevent pathological
neural spiking. The recently introduced Kirchoff-Nernst-Planck (KNP) scheme for
modeling ion dynamics in astrocytes (and brain tissue in general) is outlined
and used to study such spatial buffering. We next describe how the ion dynamics
of astrocytes may regulate microscopic liquid flow by osmotic effects and how
such microscopic flow can be linked to whole-brain macroscopic flow. We thus
include the key elements in a putative multiscale theory with astrocytes
linking neural activity on a microscopic scale to macroscopic fluid flow.Comment: 27 pages, 7 figure
Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures
We introduce Arbor, a performance portable library for simulation of large
networks of multi-compartment neurons on HPC systems. Arbor is open source
software, developed under the auspices of the HBP. The performance portability
is by virtue of back-end specific optimizations for x86 multicore, Intel KNL,
and NVIDIA GPUs. When coupled with low memory overheads, these optimizations
make Arbor an order of magnitude faster than the most widely-used comparable
simulation software. The single-node performance can be scaled out to run very
large models at extreme scale with efficient weak scaling.
HPC, GPU, neuroscience, neuron, softwareComment: PDP 2019 27th Euromicro International Conference on Parallel,
Distributed and Network-based Processin
Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits
Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community
Systems level circuit model of C. elegans undulatory locomotion: mathematical modeling and molecular genetics
To establish the relationship between locomotory behavior and dynamics of
neural circuits in the nematode C. elegans we combined molecular and
theoretical approaches. In particular, we quantitatively analyzed the motion of
C. elegans with defective synaptic GABA and acetylcholine transmission,
defective muscle calcium signaling, and defective muscles and cuticle
structures, and compared the data with our systems level circuit model. The
major experimental findings are: (i) anterior-to-posterior gradients of body
bending flex for almost all strains both for forward and backward motion, and
for neuronal mutants, also analogous weak gradients of undulatory frequency,
(ii) existence of some form of neuromuscular (stretch receptor) feedback, (iii)
invariance of neuromuscular wavelength, (iv) biphasic dependence of frequency
on synaptic signaling, and (v) decrease of frequency with increase of the
muscle time constant. Based on (i) we hypothesize that the Central Pattern
Generator (CPG) is located in the head both for forward and backward motion.
Points (i) and (ii) are the starting assumptions for our theoretical model,
whose dynamical patterns are qualitatively insensitive to the details of the
CPG design if stretch receptor feedback is sufficiently strong and slow. The
model reveals that stretch receptor coupling in the body wall is critical for
generation of the neuromuscular wave. Our model agrees with our behavioral
data(iii), (iv), and (v), and with other pertinent published data, e.g., that
frequency is an increasing function of muscle gap-junction coupling.Comment: Neural control of C. elegans motion with genetic perturbation
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