[Neuroimaging] Interest in modeling library for NiPy
j.davidgriffiths at gmail.com
Mon Nov 7 08:09:23 EST 2016
OK well a few navigational points first:
DCM and SPM in general are obviously well developed code bases but leave a
lot to be desired in terms of explicit documentation.
The manual doesn't even begin to touch on half of the stuff squirreled away
inside the 'toolbox' folder
A general browse through that folder is well worth doing.
More specifically: many of the main DCM workshorse functions are the ones
in this folder with 'dcm' in the title:
I think that contains all of the main inversion routines, which don't
necessarily have 'dcm' names, such as
...as well as high level model comparison functions like
The DCM M/EEG toolbox
then has lots of model specific things with the (relative to fMRI) more
detailed and more developed M/EEG neurophysiological models.
Also worth looking at the DEM (dynamic expectation maximization) toolbox
and Neural models toolbox
General point: there are a number 'demo' functions littered around; e.g.
Which seem to me to be often the best place to look for general
On 7 November 2016 at 05:11, Marmaduke Woodman <mmwoodman at gmail.com> wrote:
> On Fri, Nov 4, 2016 at 7:18 PM, John Griffiths <j.davidgriffiths at gmail.com
> > wrote:
> the distinction is between inference on parameters vs. inference on models
>> (parametric/non-parametric has separate meanings); and not DCM's estimates
>> of effective connectivity parameters per se but rather model
>> evidence/fit/frenergy metrics and comparisons thereof. Certainly it is
>> essential to support both.
> I would focus first on the former: an API would allow specification of a
> dataset, a generative model and an inference scheme; the results would be
> inference diagnostics and posteriors.
> One could build on that to specific multiple models or a model space and
> comparison criteria.
> Anyone with experience in DCM's API might be able to suggest how to make
> that user friendly?
>> PyMC3 does look like the way to go.
> Edward (http://edwardlib.org) is a new one also worth looking at, because
> it builds mainly on TensorFlow. I'm not sure even HMC will scale to full
> size neuroimaging data (though networks with several or tens of nodes would
> work), so it's important to keep the variational schemes available.
> Neuroimaging mailing list
> Neuroimaging at python.org
Dr. John Griffiths
Post-Doctoral Research Fellow
Rotman Research Institute, Baycrest
School of Physics
University of Sydney
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the Neuroimaging