12 May
2020
12 May
'20
4:12 p.m.
Dear Benedict,
Regarding your second question, from a quick glance at that paper it seems
the authors do not state explicitly that their computation is faster than
NEST. They probably mean that computing the weight change only when a
presynaptic spike arrives is faster than evaluating their update code at
each time step (which is not what NEST does, but what a
straightforward simulation of the authors' hardware would do: see the line "in
contrast to the implementation on BSS2"). As for why the authors chose to
implement plasticity outside of NEST rather than pick some existing model
of reward-modulated STDP, this is not explained in the paper, but one might
suppose that the reason may have been to ensure that the plasticity is
identical to their hardware setup.
Anyway, synaptic weight change computation in NEST is also triggered by a
presynaptic spike (just because we only need to know the up-to-date weight
value when a postsynaptic spike has to be transmitted). So, there does not
seem to be anything that needs improvement in this matter. Please correct
me if I get something wrong.
Sincerely yours,
Alex Serenko
Graduate student at Kurchatov Institute, Moscow, Russia
вт, 12 мая 2020 г. в 16:07, Benedikt S. Vogler <benedikt.s.vogler(a)tum.de>de>:
Hello everyone!
I am writing to you regarding two matters:
Reset Network/Kernel in nest2->nest 3
In the last developer conference, Daphne Cornelisse talked about that she
used *ResetNetwork*() to solve her problem.
*ResetNetwork()* is marked as deprecated. No one said anything, so I got
confused why this is apparently the recommended way or at least approved.
She showed me, that it works (in her case). It is deprecated, so there is
probably some good reasoning behind it. The documentation says: "
ResetNetwork is deprecated and will be removed in NEST 3, because this
function is not fully able to reset network and simulator state. What are
the edge cases where the use causes problems?
In this ticket, it is stated that the feature is just removed with any
replacement.
https://github.com/nest/nest-simulator/issues/525
Thus, in nest3 there is only *ResetKernel*().
This means that you have to rebuild the network for any application where
you do multiple simulations with different input or parameter changes. I am
using nest3 for reinforcement learning and in each training episode, I have
to extract all the weights and save them, reset the kernel, reconstruct the
net, then load all the weights. This adds a lot of overhead in performance
and bloats my code. I basically have another front-end storing the net and
talking to the nest back-end.
Therefore, the update to nest3 is a downgrade for many applications. I
don’t have a solution for this issue, but I want to spark some discussion
as I learned that I am not the only nest user to stumble into this issue.
STDP Performance boost by manual computation in python
In the paper "Demonstrating Advantages of Neuromorphic Computation: A
Pilot Study“ by Wunderlich et al. (
https://www.frontiersin.org/articles/10.3389/fnins.2019.00260/full) some
performance improvement on STDP was reported.
"The synaptic weight updates in each iteration were restricted to those
synapses which transmitted spikes, i.e., the synapses from the active input
unit to all output units (32 out of the 1,024 synapses), as the
correlation a+ of all other synapses is zero in a perfect simulation
without fixed-pattern noise. This has the effect of reducing the overall
time required to simulate one iteration[…]“
The provided source code (
https://github.com/electronicvisions/model-sw-pong/blob/976e0778ca05cfd96c4…)
indeed contains a manual computation of STDP. When using the nest library I
don’t expect to do some computation in python to be faster. It appears to
me that the nest implementation is computing STDP every time, even without
spikes? Maybe someone can comment on this whether this can be improved in
nest?
Kind regards,
Benedikt S. Vogler
--
Benedikt S. Vogler
benedikt.s.vogler(a)tum.de
Student M.Sc. Robotics, Cognition, Intelligence
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