Circuits that contain the Modeling Application : NetPyNE (Home Page)

("NetPyNE (Network development Python package for NEURON) is a python package to facilitate the development, parallel simulation and analysis of biological neuronal networks using the NEURON simulator. Although NEURON already enables multiscale simulation ranging from the molecular to the network level, NEURON for networks, often requiring parallel simulations, requires substantial programming. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for experimentalists. NetPyNe is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.")
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    Models   Description
1. Multiscale model of primary motor cortex circuits predicts in vivo dynamics (Dura-Bernal et al 2023)
Understanding cortical function requires studying multiple scales: molecular, cellular, circuit and behavior. We developed a multiscale biophysically-detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity and dendritic synapse locations were constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions, and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicted in vivo layer- and cell type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generated mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell type-specific multiscale dynamics associated with several experimental conditions and behaviors.
2. Parallelizing large networks in NEURON (Lytton et al. 2016)
"Large multiscale neuronal network simulations and innovative neurotechnologies are required for development of these models requires development of new simulation technologies. We describe here the current use of the NEURON simulator with MPI (message passing interface) for simulation in the domain of moderately large networks on commonly available High Performance Computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike passing paradigm and post-simulation data storage and data management approaches. We also compare three types of networks, ..."
3. Potjans-Diesmann cortical microcircuit model in NetPyNE (Romaro et al 2021)
The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we re-implemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for rescaling the network size which preserves first and second order statistics, building on existing work on network theory. The new implementation enables using more detailed neuron models with multicompartment morphologies and multiple biophysically realistic channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, and generally multiscale interactions in the network. The rescaling method provides flexibility to increase or decrease the network size if required when running these more realistic simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.
4. Realistic barrel cortical column - NetPyNE (Huang et al., 2022)
Reconstructed rodent barrel cortical column (thalamic filter-and-fire input, L4 and L2/3 spiking neurons) based on measured distributions, so each run will create a different connectivity). Includes 13 types of inhibitory and excitatory neurons, implemented as Izhikevich neurons. Includes both a Matlab and a Python (NetPyNe) implementation.
5. Spinal Dorsal Horn Network Model (Medlock et al 2022)
To explore spinal dorsal horn (SDH) network function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based model neurons that reproduce the characteristic firing patterns of excitatory and inhibitory spinal neurons. Excitatory spinal neuron subtypes defined by calretinin, somatostatin, delta-opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents (Aß, Ad, and C-fibres) whose firing rates were also matched to experimental data.
6. Theta-gamma phase amplitude coupling in a hippocampal CA1 microcircuit (Ponzi et al. 2023)
Using a data-driven model of a hippocampal microcircuit, we demonstrate that theta-gamma phase amplitude coupling (PAC) can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma..

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