[Comp-neuro] NEURON v. 6.2 available

Ted Carnevale carnevalet at sbcglobal.net
Tue Aug 12 19:35:52 CEST 2008


The newest standard distribution of NEURON is version 6.2, which is
available from http://www.neuron.yale.edu/neuron/install/install.html

This is principally a "bug fix" release, but there have also been
some improvements of features and functionality.  Of the latter, the
most noteworthy have to do with Python.

--------------------------
Changes that affect Python
--------------------------

All communication with hoc from Python is now accomplished uniformly
via the neuron.h object.  In other words, do this first
   import neuron
   h = neuron.h
Then, for example,
   h.Section() returns new section
   h.cas() returns currently accessed section
   h.allsec() is an iterator over all sections

Python allows use of a nrn.Segment object as the argument to a
PointProcess constructor or loc function. That is, IClamp(section(x))
is an alternative to IClamp(x, sec = section).
Also, section(0.5).sec is the section, and section(0.5).x is the arc
location value 0.5.

The following new Vector functions are much (> 50 times !) faster than
a Python loop over the elements:
--Vector.from_python(source) fills the Vector with doubles from a
   Python list or NumPy 1-d array. The Vector is resized and returned.
--Vector.to_python() returns a Python list of doubles.
--Vector.to_python(target) fills the target with doubles from the
   hoc Vector and returns the target object.  Note that if the target
   is a NumPy 1-d array, it must already be sized the same as the Vector.

----------------------------------
Other changes that deserve mention
----------------------------------

CellBuild.cexport() is public and can be used from hoc with an argument
of 1 (or 0) to force (or prevent) writing the cell info to the
simulation instance.

Vector.play in continuous mode uses linear extrapolation of the last
two time points when a value is requested outside the time domain of
the vector.  This allows more efficient variable time step approach
to a discontinuity as it keeps the first derivative continuous when
cvode asks for a value past the next discontinuity (the discontinuity
event will cause a retreat to the proper time).


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