[Comp-neuro] more talks and tutorials at the NEURON Simulator Meeting

Ted Carnevale ted.carnevale at yale.edu
Thu Apr 13 04:02:22 CEST 2006

Register now for the NEURON Simulator Meeting, which
will be held May 5-7 at UT Austin--see
The registration deadline is Friday, April 21.

Here are two more reasons to attend:

Talk:  "Rule-based artificial cell (RBAC) models in NEURON"
Speaker:  Bill Lytton
There are a variety of artificial cell models available in NEURON.
These have the advantage of using forward prediction of spike times
in order to avoid the overhead of integration.  Additionally, they
can be used in hybrid networks with compartmental models in order
to run large models at vastly increased speed.  We have developed
new artificial neuron models that use rule sets to determine firing
based on different types of inputs and several internal state
variables.  In addition to speed, this approach cleanly isolates
the various synaptic and internal state variables so that they can
be manipulated in a way that is difficult in the complex
parameterizations of full multi-compartment/multi-ion-channel/ 
multi-synapse models.  The basic rule remains the same as that of
the integrate-and-fire model: fire when the state variable exceeds a
fixed threshold.  Additional rules were added to provide adaptation,
bursting, depolarization blockade, Mg-sensitive NMDA conductance,
anode-break depolarization, and others.  The implementation is event
driven, providing additional speed-up by avoiding numerical

Tutorial:  "Recent advances in the CellBuilder:  managing models
with spatially varying parameters"
Speaker:  Ted Carnevale
This tutorial will show how to use the CellBuilder, one of
NEURON's GUI tools, to construct and manage neuron models in
which one or more parameters vary with location as functions of
an independent variable.  This recent enhancement graphically
supports the idiom
   forsec subset for (x, 0) { rangevar_suffix(x) = f(p(x)) }
where rangevar_suffix(x) is the parameter of interest, p(x) is
a domain function over the subset, and f is any expression.
Built-in domain functions are arc (path) distance from the
soma, radial distance from a point, and distance along an axis
in the xy plane.


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