Abstract

To understand single neuron computation it is desirable that realistic input patterns be given to model neurons in the study of the input-output function. Spike train objects can be used to generate input patterns in place of a full network model, which is often not available. I will discuss how spike trains can be generated as probability distributions, or read from experimental data. Correlations between spike trains can be constructed to test temporal coding by neural models Examples and exercises including strategies for data analysis will be given.

This tutorial is intended for the intermediate or advanced level Genesis user. It is assumed that the user knows how objects and elements are handled in GENESIS. The set of GENESIS scripts in the GUM02_JAEGER directory makes up a modular tutorial of how timetables and various synaptic elements can be used to study synaptic integration. All simulations in this tutorial are based on a single compartment model. The main script is GUM02_JAEGER/master.g. The documentation is also available in the MS-Word file GUM02_JAEGER/gum02_jaeger_tut.doc.

See the tutorial (HTML format)

See the related Powerpoint presentation How neurons compute with thousands of inputs each second (jaeger_utsa_2002.ppt)

Download the tutorial and GENESIS scripts (gzipped tar file) gum02_jaeger.tar.gz