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Computational Models
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Modeling & Visualization Tools
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Biorealistic Model of Mouse Primary Visual Cortex (V1)
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Biologically realistic models of the mouse primary visual cortex (V1), constructed at biophysically detailed and point-neuron levels. Learn more about the model.
Publication
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Simulating Cortical Electrical Signals Using the Mouse V1 Model
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The mouse V1 model was used to simulate electrical signals (LFP/CSD). Feedback from a higher cortical area was added to obtain a better match with LFP recorded in vivo.
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Modeling the mammalian cortex: Layer IV of mouse V1
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Biologically realistic models of the cortical lamina (Layer IV; L4) of mouse primary visual cortex (V1) were constructed at the biophysically detailed and point-neuron levels, to explore mechanisms underlying cortical computations in extensive simulations with a battery of visual stimuli. Learn more about the Layer 4 model.
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MCModels: Whole brain voxel-scale connectivity model
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MCModels is a Python package providing mesoscale connectivity models for the whole adult mouse brain.
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Single neuron models
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Perisomatic biophysical single neuron models
Biophysically-detailed perisomatic-active models are optimized to reproduce the intrinsic firing patterns and action potential properties of individual cells using electrophysiological recordings and morphological reconstructions. Learn more about these models.
Publication
Generalized leaky integrate and fire neuron models
Generalized leaky integrate and fire (GLIF) point-neuron models aim to reproduce the spike times of electrophysiological current clamp data collected from mouse and human cortical neurons. Learn more about the GLIF models in the Allen Cell Types Database here.
Publication
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A Python software package for building, simulating, and analyzing large-scale neural network models at multiple levels of resolution.
Publication | BMTK GitHub | BMTK website
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A cross-platform data format for storing large-scale network models and simulation results.
Publication | SONATA GitHub
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Visual Neuronal Dynamics (VND)
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A software package for 3D visualization of neuronal network models and simulations. Developed collaboratively by groups at UIUC and Allen Institute.
VND website
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DiPDE
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The displacement integro-partial differential equation (DiPDE) population density model refers to a simulation platform for numerically solving the time evolution of coupled networks of neuronal populations. Instead of solving the subthreshold dynamics of individual model leaky-integrate-and-fire (LIF) neurons, DiPDE models the voltage distribution of a population of neurons with a single population density equation. In this way, DiPDE can facilitate the fast exploration of mesoscale (population-level) network topologies, where large populations of neurons are treated as homogeneous with random fine-scale connectivity.
Publication | DiPDE on GitHub | DiPDE website
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