What: An interactive online course about the NEURON Simulation Environment
When: Starts Thursday, June 3, 2021, and meets on Tuesdays and Thursdays thereafter until Tuesday August 3.
Faculty: Ted Carnevale and Robert McDougal
Application deadline: Monday, May 31, 2021
Registration fee: None.
This course maps the first half of the NEURON Summer Course into a series of interactive online workshops about building and working with models of single neurons. It is designed for those who are planning to use NEURON in neuroscience research or teaching, or already have active projects that involve NEURON, and is suitable for users at all levels of expertise.
Beginners with little or no modeling experience will learn the philosophical basis, scientific rationale, and technical aspects of modeling.
Intermediate users will learn the best approaches to common tasks, and how to use special features of NEURON in their own work.
Experienced users will benefit from an update on new features and a review of important topics that may have escaped their attention when they first started using NEURON.
Although this course focuses on modeling individual cells, the topics it covers are the foundation for modeling networks with NEURON.
This course is not an introduction to cellular neurophysiology or Python programming. It presumes you already have a basic understanding of the anatomical and biophysical properties of neurons, and are familiar with Python. If Python is new to you, be sure to work through the Python introduction before the course starts. You might also find the Scripting NEURON basics tutorial to be helpful. If you don't know anything about biological neurons, read chapters 1, 3, 4, and 5 of Cellular and Molecular Neurophysiology (ed. C. Hammond, Academic Press, 2008) or something equivalent before the course starts.
Space is limited, and applications will be considered in the order received. The application deadline is Monday, May 31, 2021. No applications will be accepted after that date.
Submit the online application form.
Applicants will be notified of acceptance shortly after Monday, May 31.
The workshops will start at 1 pm New York time and run for 1 to 2 hours, depending on the topic at hand. Each Thursday workshop will start with a brief question and answer session, followed by introduction of new material, and ending with an assignment that participants are expected to do on their own. In each Tuesday workshop, participants will have the opportunity to present their results, discuss problems that they encountered, and show how they solved them.
Introduction to mechanistic modeling in neuroscience.
Concepts: Hypotheses and models; the importance of conceptual control; neurites, cables, sections.
Activity: Building and using a simple model cell with the CellBuilder.
Homework: Build and use a model axon. Use it to measure conduction velocity and study anode break excitation.
Branched model neurons.
Concepts: Range, range variables, spatial discretization, nodes, nseg.
Activity: Build a model pyramidal cell. Current clamp the soma and find spike threshold.
Homework: Change the current clamp to an excitatory synapse and examine how synaptic location affects response at the soma. Build a model interneuron and determine how synaptic location affects synaptic efficacy.
Adding new ion channels to NEURON with NMODL and the Channel Builder.
Homework: Given a set of channel mod files, do current and voltage clamp experiments on a virtual oocyte.
Python + NEURON.
Concepts: Programmatic specification of topology, channel distribution, and virtual experiments. Best practices.
Building and using a model cell with Python.
Concepts: Incremental revision and testing.
Activity: Producing model cells that work with the GUI and are suitable for use in network models running on parallel hardware.
Working with morphometric data.
Concepts: Data file formats. Detecting common problems with morphometric data.
Activity: Importing and evaluating morphologies. Strategies for managing anatomically detailed models.
Reproducibility in computational modeling.
Concepts: Model sharing, discovering what's in a model (model introspection and analysis).
Activity: Using ModelDB and ModelView.
Numerical methods in NEURON.
Concepts: Fixed time step and adaptive integration, parameter discontinuities.
Activity: Compare fixed time step and adaptive integration simulation results. Force a parameter change at a particular time.
The hoc programming language.
Concepts and activity: Understanding hoc code; making minor changes when necessary.
Homework: Load HOC model from ModelDB. Work with it from Python.
Questions? Contact Ted Carnevale by email (ted dot carnevale at yale dot edu)
Supported in part by:
National Institutes of Health
National Science Foundation
NEURON 2021 Online Course