Models that contain the Modeling Application : MOOSE/PyMOOSE (Home Page)

(" ... We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. ...")
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    Models   Description
1.  Discrimination on behavioral time-scales mediated by reaction-diffusion in dendrites (Bhalla 2017)
Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data.
2.  Information trans. through Entopeduncular nucleus modified by synaptic plasticity (Gorodetsky et al)
Multicompartmental model of EP neuron was created using automatic parameter optimization. We included both short term plasticity and long term plasticity. We simulated the response to inputs from globus pallidus, striatum and subthalamic nucleus. We show that dopamine long term plasticity enhances information transmission from striatum and reduces GPe and STN information transmission.
3.  Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)
A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
4.  Parameter optimization using CMA-ES (Jedrzejewski-Szmek et al 2018)
"Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity. Since developing a biophysically realistic, single neuron model is exceedingly difficult, software has been developed for automatically adjusting parameters of computational neuronal models. The ideal optimization software should work with commonly used neural simulation software; thus, we present software which works with models specified in declarative format for the MOOSE simulator. Experimental data can be specified using one of two different file formats. The fitness function is customizable as a weighted combination of feature differences. The optimization itself uses the covariance matrix adaptation-evolutionary strategy, because it is robust in the face of local fluctuations of the fitness function, and deals well with a high-dimensional and discontinuous fitness landscape. We demonstrate the versatility of the software by creating several model examples of each of four types of neurons (two subtypes of spiny projection neurons and two subtypes of globus pallidus neurons) by tuning to current clamp data. ..."
5.  Reconstructed neuron (cerebellar, hippocampal, striatal) sims using predicted diameters (Reed et al)
Many neuron morphologies in NeuroMorpho.org do not contain accurate dendritic diameters that are needed for simulations. We used a set of archives which did have realistic morphologies to derive equations predicting dendritic diameter, and to create morphologies using the predictions. The equations and new morphologies are derived by 1. extracting morphology features from swc files (morph_feature_extract.py) 2. using multiple regression to derive equations predicting diameter, (morph_feature_extract.py ) 3. using the equations to create the new morphology files from original swc file (shape_shifter.py). The python programs are all available from github.com/neurord/ShapeShifter We simulated the original morphologies and the morphologies with predicted diameter in Moose, evaluating the response to current injection and synaptic input. The code provided implements those simulations
6.  Striatum D1 Striosome and Matrix Upstates (Prager et al., 2020)
"...We show that dopamine oppositely shapes responses to convergent excitatory inputs in mouse striosome and matrix striatal spiny projection neurons (SPNs). Activation of postsynaptic D1 dopamine receptors promoted the generation of long-lasting synaptically evoked 'up-states' in matrix SPNs but opposed it in striosomes, which were more excitable under basal conditions. Differences in dopaminergic modulation were mediated, in part, by dendritic voltage-gated calcium channels (VGCCs): pharmacological manipulation of L-type VGCCs reversed compartment-specific responses to D1 receptor activation..."

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