5 Aug
2020
5 Aug
'20
9:28 a.m.
Hello all,
I remember seeing a presentation some years back where a large
connectome was read directly into NEST from disk. I think it was stored
in HDF5. One of the main complications was the round robin neuron
placement complicating manners when a MPI rank size different then the
stored version was used: all synapses needed to be transported to a
different node. It was expensive.
Looking ahead, how some connectomes are generated in EBRAINS. Large
pre-processing pipelines with complicated touch detectection and 3d
placement of neurons (Markram Cell 2015) having this functionality might
come in handy in the future.
2cents.
Wouter
On 04-Aug-20 10:33, Benedikt S. Vogler wrote:
Hey all,
this sound similarly to my later suggestion in the reset issue:
https://github.com/nest/nest-simulator/issues/1618#issuecomment-633972232
I wanted to do some more research on how loading and saving is already
currently supported in the library (PyNest and c++ back-end side) before
working on the implementation. I would be interested in working on this
implementation or gladly join anyone, who wants to work on this.
Kind regards,
Benedikt
--
Wouter Klijn <w.klijn(a)fz-juelich.de>
Team Leader Multiscale simulation and design
SimLab Neuroscience
Jülich Supercomputing Centre
Institute for Advanced Simulation
Forschungszentrum Jülich
http://www.fz-juelich.de/ias/jsc/slns
Office: +49 2461 61-3523
Fax # : +49 2461 61-6656
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Am 04.08.2020 um 10:15 schrieb LOW, SOCK CHING
<sockching.low(a)upf.edu
<mailto:sockching.low@upf.edu>>:
Hi Charl,
Thanks for your prompt reply. What I mean to say is that I'd like to
save the network parameters, specifically the connections (i.e.
weights, delays etc.) and neuron parameters. There is no plasticity in
the network, as I am simply initialising a network of neurons with
probabilistic, random connections between each other (a liquid state
machine, in other words).
My intention is to use the network in a real-time control
architecture, so I don't need to replicate /exactly /the same output
for a given input stimulation but I do need an output that is
comparable. Currently, I am using the states of the network to train a
classifier offline, but this classifier is fitted to the behaviour of
the network that it trains on. To be able to use the classifier again,
I will need to recreate that network, which leads me to the question
of "saving the entire network". Once I can do so, it becomes possible
to run simulations episode by episode rather than queueing all the
episodes into a single batch for simulation, which is critical for my
purpose. I hope that clarifies the question.
Cheers,
Sock Ching
On Tue, 4 Aug 2020 at 09:01, Charl Linssen <nest-users(a)turingbirds.com
<mailto:nest-users@turingbirds.com>> wrote:
__
Dear Sock Ching,
When you say "I would like to save the network", could you
elaborate on what exact part of the state you want to save and
recall? For instance, in case of simulating plasticity, all the
weights can be (re)set by calling SetStatus() on each connection
object. The same goes for neuronal state variables. There are a
few things that cannot be reset at the time of writing, such at
the random number generators, but this is only an issue if you try
to reproduce the same run *exactly*, multiple times in a row. The
biggest issue might be that spike buffers (spikes in transit)
cannot be reset. Potentially, you could work around this by
allowing a small "startup transient", i.e. ignoring whatever your
network produces in the first few hundred or so milliseconds after
starting simulation. If this startup transient is causing you
trouble, however, please feel invited to submit a feature request
on our GitHub repository for spike buffer reset functionality.
(Please provide as much technical detail as possible.)
There is already some prior discussion on this topic at
https://github.com/nest/nest-simulator/issues/1618, where it was
decided that just having a global "ResetNetwork()" function was
not feasible, because it is not clear for the general case what
this function would do. So in case you open a new GitHub issue,
please try to specify as precisely as possible what parts of your
network state need to be (re)set.
Hope this helps, please don't hesitate to share your further
experiences.
Best regards,
Charl
On Mon, Aug 3, 2020, at 19:19, LOW, SOCK CHING wrote:
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Hi,
I am new to pynest and am using it to implement a liquid state
machine. It works marvelously for generating the network. My
workflow is as follows:
- Initialise kernel
- Get stimulation episodes
- Create neurons (including spike generators)
- Connect neurons
- nest.Simulate()
- nest.GetStatus(recording_neurons) for readout at relevant
timepoints
I would like to save the network so I can run nest.Simulate() on
other episodes in a real-time application, is there a
straight-forward way to do so?
I have tried pickling
conn = nest.GetConnections()
but to my knowledge that does not include a lot of details about
the connection, like the weight. It also does not return where
there is /no/ connections, which means simply using
nest.SetConnections(conn)
prior to nest.Simulate() will not work to reproduce the
previously generated network even if I initialise with all the
same variables. I have found the functions GetNetwork() and
GetNodes() but I'm not sure how I can use them, or if they are
even useful for what I need.
Cheers,
Sock Ching
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