MOOSE-Notebooks

These notebooks present hands-on tutorials for topics in modeling in Neuroscience and Systems Biology. The contents are divided into three groups, Beginner, Intermediate, and Advanced.

How to run these notebooks

These notebooks use the MOOSE neuro-simulator. It is available as a Python module. You can either install it on your own computer, or install it on a Google Colab environment. You can also use binder to run all the notebooks in a single environment.

Beginner

1. Using MOOSE on Colab
2. Using MOOSE on Binder
3. MOOSE overview

Biophysics of Neurons

1. Resting membrane potential Discussion of Nernst-Planck and Nernst equation and Goldman-Hodgkin-Katz equation
2. Getting started with compartmental modelling in MOOSE Shows how to create a passive neuronal compartment in MOOSE
3. More complex current injection protocol Shows how to generate various patterns of (current) pulses for injecting into a compartment
4. Multi-compartmental neuron model Describes how you can connect several compartments to create a neuronal cable
5. Cable theory Derives the cable equation and then shows its approximation using a voltage clamped multicompartmental cable
6. Branching neurites How does branching affect electrical signals through neurons
7. Hodgkin and Huxley's model of K+ current Implements Hodgkin and Huxley's model of K+ current. This is the first step towards modeling action potentials in the squid giant axon.
8. Hodgkin and Huxley's model of Na+ current Implements Hodgkin and Huxley's model of Na+ current. The Na+ current is responsible for depolarizing the neuron for an action potential, whereas the K+ current repolarizes it.
9. Action potentials Puts together the Na+ and K+ currents to demonstrate Hodgkin and Huxley's model of action potential generation in the squid giant axon.
10. Direction selectivity Shows how centripetal sequence of inputs (i.e., starting at distal dendrites and moving towards the soma) is more effective at depolarizing the soma compared to a centrifugal sequence of inputs.

Biochemistry of Neurons

1. Modeling a simple chemical reaction Shows how to setup a reversible chemcial reaction following the law of mass action.
2. Modeling Michaelis-Menten enzymatic reaction Demonstration of enzymatic reaction with Michaelis-Menten type kinetics and Lineweaver-Burk plot.
3. Simple chemical reaction system with bistability Explains bistability and shows how to implement this in a reaction system.
4. Stochastic chemical model A simple bistable chemical system, but adressing the randomness that becomes prominent when the number of interacting particles is small.

Intermediate

1. Synapses Explains the components of the basic synapse model in MOOSE. There can be multiple ways to model a synapse at different complexities, all based on these basic ideas.
2. Leaky integrate and fire (LIF) neurons and synapse Implementation of a network of simple integrate-and-fire neurons connected via synapses.
3. Synapse between two biophysical model neurons Two single-compartment neuron models with Hodgkin-Huxley dynamics connected by a synapse.