# MOOGLI¶

## Use Moogli for plotting¶

loadMorphology.main()[source]

Demonstrates how one can visualise morphology of a neuron using the MOOSE.

# MatPlotLib¶

## Displaying time-series plots¶

crossComptNeuroMesh.main()[source]

This example illustrates how to define a kinetic model embedded in a NeuroMesh, and undergoing cross-compartment reactions. It is completely self-contained and does not use any external model definition files. Normally one uses standard model formats like SBML or kkit to concisely define kinetic and neuronal models. This example creates a simple reaction:

a <==> b <==> c


in which a, b, and c are in the dendrite, spine head, and PSD respectively. The model is set up to run using the Ksolve for integration. Although a diffusion solver is set up, the diff consts here are set to zero. The display has two parts: Above is a line plot of concentration against compartment#. Below is a time-series plot that appears after # the simulation has ended. The plot is for the last (rightmost) compartment. Concentrations of a, b, c are plotted for both graphs.

## Animation of values along axis¶

diffSpinyNeuron.main()[source]

This example illustrates and tests diffusion embedded in the branching pseudo-1-dimensional geometry of a neuron. An input pattern of Ca stimulus is applied in a periodic manner both on the dendrite and on the PSDs of the 13 spines. The Ca levels in each of the dend, the spine head, and the spine PSD are monitored. Since the same molecule name is used for Ca in the three compartments, these are automagially connected up for diffusion. The simulation shows the outcome of this diffusion. This example uses an external electrical model file with basal dendrite and three branches on the apical dendrite. One of those branches has the 13 spines. The model is set up to run using the Ksolve for integration and the Dsolve for handling diffusion. The timesteps here are not the defaults. It turns out that the chem reactions and diffusion in this example are sufficiently fast that the chemDt has to be smaller than default. Note that this example uses rates quite close to those used in production models. The display has four parts:

1. animated line plot of concentration against main compartment#.
2. animated line plot of concentration against spine compartment#.
3. animated line plot of concentration against psd compartment#.
4. time-series plot that appears after the simulation has ended.
diffSpinyNeuron.makeChemModel(compt, doInput)[source]

This function setus up a simple chemical system in which Ca input comes to the dend and to selected PSDs. There is diffusion between PSD and spine head, and between dend and spine head.

:: Ca_input ------> Ca // in dend and spine head only.

reacDiffBranchingNeuron.main()[source]

This example illustrates how to define a kinetic model embedded in the branching pseudo 1-dimensional geometry of a neuron. This means that diffusion only happens along the axis of dendritic segments, not radially from inside to outside a dendrite, nor tangentially around the dendrite circumference. The model oscillates in space and time due to a Turing-like reaction-diffusion mechanism present in all compartments. For the sake of this demo, the initial conditions are set to be slightly different on one of the terminal dendrites, so as to break the symmetry and initiate oscillations. This example uses an external model file to specify a binary branching neuron. This model does not have any spines. The electrical model is used here purely for the geometry and is not part of the computations. In this example we build an identical chemical model throughout the neuronal geometry, using the makeChemModel function. The model is set up to run using the Ksolve for integration and the Dsolve for handling diffusion.

The display has two parts:

1. Animated pseudo-3D plot of neuronal geometry, where each point represents a diffusive voxel and moves in the y-axis to show changes in concentration.
2. Time-series plot that appears after the simulation has ended. The plots are for the first and last diffusive voxel, that is, the soma and the tip of one of the apical dendrites.
reacDiffBranchingNeuron.makeChemModel(compt)[source]

This function sets up a simple oscillatory chemical system within the script. The reaction system is:

s ---a---> a  // s goes to a, catalyzed by a.
s ---a---> b  // s goes to b, catalyzed by a.
a ---b---> s  // a goes to s, catalyzed by b.
b -------> s  // b is degraded irreversibly to s.


in sum, a has a positive feedback onto itself and also forms b. b has a negative feedback onto a. Finally, the diffusion constant for a is 1/10 that of b.