Comments (7)
I would start with a WIP pull request that makes netpyne
an optional dependency (nested import) and shows how you can obtain the same result (with a test).
However, even before spending too much time doing this, I would suggest writing an example. How do you expect the user to interact with these new capabilities? If they change the network, in what ways would they do it in the context of EEG/MEG signals?
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This is more work than I plan on undertaking. Instead, I may first adopt some of the changes in PR #55 into the NetPyNE branch above.
If they change the network, in what ways would they do it in the context of EEG/MEG signals?
- performance: it would allow for reconfiguration of the network (e.g. synaptic weights) without tearing it down (necessary to avoid memory leaks) and rebuilding it
- it helps the HNN project goals by inferring a mechanistic understanding of EEG/MEG signals, not limited by the static network configuration used in HNN (and mne-python)
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Oops, I meant mne-neuron instead of mne-python ^^
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okay netpyne adoption is in my mental roadmap :) However, I don't want to rush it. Since the HNN code base is small it allows me to iterate fast, try things and learn more about Neuron. Adding a minimal GUI would not be that difficult (hopefully) once the code is a bit settled. In the meanwhile, I am hoping netpyne codebase improves a bit in terms of documentation / API etc. so one doesn't have to spend hours figuring out details of how it works.
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Example of how this could look:
#93
This hides Neuron stuff inside of netpyne. Everything in hnn-core can be pickled, which makes it easier for joblibs and MPI to coexist.
Now network.py and cell.py just define the netpyne specification (not object-oriented) which is passed into netpyne. Work remains to make this more readable, and there are some questions about hard-coded values.
Note, several organizational splits:
- Moved spiking functions out of Net and into their own class Spike
- Moved simulation functions into simulation.py. These basically call netpyne simulation functions.
- MPI features/complexity in mpi.py. This is for advanced use cases on cluster compute systems.
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maybe we need a netpyne coding sprint to understand and improve netpyne ... :)
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True. I think that'd be very helpful...
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Related Issues (20)
- BUG (GUI): Dipole plot scaling HOT 13
- GUI exporting simulations to csv HOT 6
- GUI load simulation from csv HOT 1
- GUI load network params and simulations from an hdf5 HOT 1
- Remove unnecessary files from param folder HOT 6
- GUI loading of base network connectivity from API
- BUG: `pick_connection` returns incorrect values when applied to network with no cell-cell connections HOT 3
- `plot_cells` error when users pass in a generic matplotlib.Axes instance HOT 8
- GUI: Clean up dev debug checks HOT 2
- Add Virtual Environment Directories to `.gitignore` HOT 6
- GUI: Creating figures with no data throws multiple exceptions
- hnn_widget.ipynb fails at cell 2 with list index out of range HOT 6
- GUI importing csv data files HOT 4
- API: network configurations I/O from hierarchical json
- Coverage not uploading to Codecov
- API: Change add_tonic_bias argument structure HOT 3
- API: Add name parameter to add_tonic_bias method
- qt issue hnn_core, also gui issue missing tabs HOT 1
- MPI timing out waiting for child process HOT 11
- GUI: Fix File Upload button height
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