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3 results for “biophysical interaction”
A biophysical model of two interacting cortical areas
<p>We present a large-scale, data-driven, biophysically-detailed computational model of two interacting cortical areas, based on data from rodent somatosensory cortex.</p> <p>This model is derived from a previous model (described in two manuscripts: <a href="https://doi.org/10.7554/eLife.99688.1" target="_blank" rel="noopener">anatomy</a>, <a href="https://doi.org/10.1101/2023.05.17.541168" target="_blank" rel="noopener">physiology</a>), but it consists of a reduced setting tailored to the study of inter-areal interactions in cortical sensory processing. Details of the model and initial results can be found <a href="https://doi.org/10.1101/2024.10.13.618022" target="_blank" rel="noopener">here</a>.</p> <h3>Description</h3> <p>The model describes a system of two otherwise isolated cortical areas (X and Y), where area X is a primary sensory and area Y is the first higher-order area in a cortical processing hierarchy. Each area consists of about 200K morphologically-detailed conductance-based neurons, distributed across six cortical layers and of 60 different morphological types and 212 morpho-electrical types.</p> <p>The model incorporates the following connectivity:</p> <ul> <li>Local touch-based connectivity within each area.</li> <li>Thalamocortical innervation from both VPM (core-type) and POm (matrix-type) nuclei to area X.</li> <li>Long-range data-driven projections between both areas with characteristic laminar termination profiles.</li> </ul> <p>Additionally, each area receives nonspecific background noise to exhibit spontaneous activity comparable to experimental recordings of per-layer mean firing rates.</p> <h3>Setup</h3> <p>The model is provided in the <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007696" target="_blank" rel="noopener">SONATA</a> format and can be run using <a href="https://github.com/BlueBrain/neurodamus/" target="_blank" rel="noopener">Neurodamus</a>, a simulator frontend for <a href="https://www.neuron.yale.edu/neuron/" target="_blank" rel="noopener">NEURON</a>. Synaptic and ion channel mechanisms specific for <a href="https://github.com/BlueBrain/neurodamus-models/tree/main/neocortex" target="_blank" rel="noopener">neocortical</a> neurons are also required to run this model (build instructions <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files" target="_blank" rel="noopener">here</a>).</p> <p>To setup the model, all files must be placed in the same directory and all the <a href="https://www.nongnu.org/lzip/" target="_blank" rel="noopener">Lzip</a>-compressed TAR archives must be extracted. The total uncompressed size is 219 GB.</p> <pre><code>$ mkdir model_root # download all files into model_root $ cd model_root $ for file in *.tar.lz; do tar -xf $file; done</code></pre> <h3>Simulation</h3> <p>We provide some example configuration files (under<em> </em><strong>example_simulation_configs</strong>) for simulations of spontaneous and evoked activity, as well as some network manipulations (layer-wise pathway blocks and TTX application).</p> <p>In order to run a simulation, copy <strong>simulation_config.json</strong> into a new directory and set the <em>network</em> key to the path of the directory containing the extracted model (optionally, set the <em>output</em> key as well). Instructions for running a simulation can be found <a href="https://github.com/BlueBrain/neurodamus?tab=readme-ov-file#examples" target="_blank" rel="noopener">here</a> and documentation for the simulation configuration file can be found <a href="https://sonata-extension.readthedocs.io/en/latest/sonata_simulation.html" target="_blank" rel="noopener">here</a>.</p> <h3>Analysis</h3> <p>Analysis of model composition and connectivity, as well as of simulation outputs, can be performed using <a href="https://github.com/BlueBrain/snap" target="_blank" rel="noopener">Blue Brain SNAP</a> or by directly accessing the HDF5 files with <a href="https://github.com/BlueBrain/libsonata" target="_blank" rel="noopener">libsonata</a>. Documentation on the SONATA format for all files making up the model can be found <a href="https://sonata-extension.readthedocs.io/en/latest/sonata_overview.html" target="_blank" rel="noopener">here</a>.</p> <h3>Computational resources</h3> <p>Approximate scaling of computational resources is as follows (based on simulations of 5 s biological time running on a cluster with 40 cores @ 2.5 GHz and 376 GB of RAM per node, one MPI process per core, using <a href="https://doi.org/10.3389/fninf.2019.00063" target="_blank" rel="noopener">CoreNEURON</a>):</p> <ul> <li>Memory per process = 1106 GB / N ** 0.87</li> <li>Simulation time = 4278 h / N ** 0.93</li> </ul> <p>For example, running with N = 1000 processes (25 nodes) results in 107 GB memory usage per node and 7h16m simulation time for 5 s biological time. Longer simulations scale approximately linearly in time, taking 13h13m for 10 s biological time and 21h48m for 15 s biological time.</p> <h3>Changelog</h3> <p>v1.0.1<br>Fixed (unused) key "node_sets_file" in example simulation configuration files.</p>
Frequency-dependent viscosity of salmon ovarian fluid has biophysical implications for sperm-egg interactions
<p>Gamete-level sexual selection of externally fertilising species is usually achieved by modifying sperm behaviour with mechanisms thought to alter the chemical environment in which gametes perform. In fish this can be accomplished through the ovarian fluid, a substance released with the eggs at spawning. While its biochemical effects in relation to sperm energetics have been investigated, the influence of the physical environment in which sperm compete remains poorly explored. Our objective was therefore to gain insights on the physical structure of this fluid and potential impacts on reproduction. Using soft-matter physics approaches of steady-state and oscillatory viscosity measurements, we subjected salmon ovarian fluids to variable shear stresses and frequencies resembling those exerted by sperm swimming through the fluid near eggs. We show that this fluid, which in its relaxed state is a gel-like substance, displays a non-Newtonian viscoelastic and shear-thinning profile, where the viscosity decreases with increasing shear rates. We concurrently find that this fluid obeys the Cox-Merz rule below 7.6 Hz and infringes it above, thus indicating a shear-thickening phase where viscosity increases provided it is probed gently enough. This suggests the presence of a unique frequency-dependant structural network with relevant implications on sperm energetics and fertilisation dynamics.</p>
Frequency-dependent viscosity of salmon ovarian fluid has biophysical implications for sperm-egg interactions
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