15 Jun
2020
15 Jun
'20
2:24 p.m.
Dear all,
I´m writing a master thesis on spiking neural networks and how transparent they are. For
that I need to implement a SNN network and train it. So I started with Brian but that is
much to complex and I don´t need something special. So I decided to use PyNest. I did all
the tutorials but I´m missing a tutorial how to train the network. I don´t know how to put
in a dataset to train the model. I haven´t found anything to this topic. So my questions
are:
1. Can PyNest train set up a SNN and train it trough data and if not is there another
simulator who can do this?
2. How do I do it? Is there anything I missed to read or can someone send me an example?
This would be very helpful.
Thanks for your help.
Best,
Sonja
15 Jun
15 Jun
3:05 p.m.
Dear Sonja,
If you're doing deep/machine learning, it could make sense to look at different
simulators like Norse (https://github.com/norse/norse/, disclaimer: I'm a maintainer),
or BindsNET (https://github.com/BindsNET/bindsnet).
Both have explicit examples on how to build and train spiking models based on the PyTorch
framework (https://pytorch.org/), cf.
https://github.com/norse/notebooks/tree/master/notebooks.
Sincerely,
Jens Egholm Pedersen
jensegholm(a)protonmail.com
‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Monday, 15. June 2020 14:24, <s.kraemer96(a)gmx.net> wrote:
Dear all,
I´m writing a master thesis on spiking neural networks and how transparent they are. For
that I need to implement a SNN network and train it. So I started with Brian but that is
much to complex and I don´t need something special. So I decided to use PyNest. I did all
the tutorials but I´m missing a tutorial how to train the network. I don´t know how to put
in a dataset to train the model. I haven´t found anything to this topic. So my questions
are:
1. Can PyNest train set up a SNN and train it trough
data and if not is there another simulator who can do this?
2. How do I do it? Is there anything I missed to read or can someone send me an example?
This would be very helpful.
Thanks for your help.
Best,
Sonja
NEST Users mailing list -- users(a)nest-simulator.org
To unsubscribe send an email to users-leave(a)nest-simulator.org
5:18 p.m.
Dear Sonja,
nest is a spiking neural network simulator and not a machine learning library.
My current knowledge on SNN is that there is no established learning algorithm as there
are best-practices for ANNs.
Here is a brief overview over some methods:
There ist STDP for correlation learning, reward based STDP is a reinforcement learning
algorithm still being researched. Another option is to train a ANN and then convert it to
a SNN.
SNN don’t have derivative of the activation function, therefore backprop is not
transferable easily to SNN. There are methods like BPTT and e-prop to make backprop work.
There might be more methods in the area of backprop adaptions. I am not an expert on
this.
SNNs can also be used for reservoir computing which is yet another thing
(https://gitlab.com/aiCTX/rockpool).
I am not sure which learning algorithm norse uses, they mention Policy gradient.
Kind regards,
Benedikt S. Vogler
Am 15.06.2020 um 14:24 schrieb s.kraemer96(a)gmx.net:
Dear all,
I´m writing a master thesis on spiking neural networks and how transparent they are. For
that I need to implement a SNN network and train it. So I started with Brian but that is
much to complex and I don´t need something special. So I decided to use PyNest. I did all
the tutorials but I´m missing a tutorial how to train the network. I don´t know how to put
in a dataset to train the model. I haven´t found anything to this topic. So my questions
are:
1. Can PyNest train set up a SNN and train it trough data and if not is there another
simulator who can do this?
2. How do I do it? Is there anything I missed to read or can someone send me an example?
This would be very helpful.
Thanks for your help.
Best,
Sonja
_______________________________________________
NEST Users mailing list -- users(a)nest-simulator.org
To unsubscribe send an email to users-leave(a)nest-simulator.org
5:36 p.m.
I recently read a potentially relevant review on this subject from earlier
this year: A review of learning in biologically plausible spiking neural
networks
<https://www.sciencedirect.com/science/article/abs/pii/S0893608019303181?fbclid=IwAR3Zu4aj38sb4pchp-jtzaizsIYvYao-QAZpFC2Ay8Nb672fipM-TDGE9eY>
.
Does anyone have thoughts on how to think about the differences (esp. in
terms of learning) between BindsNet, Norse, and Rockpool?
Best wishes,
Josh
On Mon, Jun 15, 2020 at 11:18 AM Benedikt S. Vogler <
benedikt.s.vogler(a)tum.de> wrote:
Dear Sonja,
nest is a spiking neural network simulator and not a machine learning
library.
My current knowledge on SNN is that there is no established learning
algorithm as there are best-practices for ANNs.
Here is a brief overview over some methods:
There ist STDP for correlation learning, reward based STDP is a
reinforcement learning algorithm still being researched. Another option is
to train a ANN and then convert it to a SNN.
SNN don’t have derivative of the activation function, therefore backprop
is not transferable easily to SNN. There are methods like BPTT and e-prop
to make backprop work. There might be more methods in the area of backprop
adaptions. I am not an expert on this.
SNNs can also be used for reservoir computing which is yet another thing (
https://gitlab.com/aiCTX/rockpool).
I am not sure which learning algorithm norse uses, they mention Policy
gradient.
Kind regards,
Benedikt S. Vogler
Am 15.06.2020 um 14:24 schrieb
s.kraemer96(a)gmx.net:
Dear all,
I´m writing a master thesis on spiking neural networks and how
transparent they
are. For that I need to implement a SNN network and train
it. So I started with Brian but that is much to complex and I don´t need
something special. So I decided to use PyNest. I did all the tutorials but
I´m missing a tutorial how to train the network. I don´t know how to put in
a dataset to train the model. I haven´t found anything to this topic. So my
questions are:
1. Can PyNest train set up a SNN and train it
trough data and if not
is there another simulator who can do this?
2. How do I do it? Is there anything I missed
to read or can someone
send me an example? This would be very helpful.
Thanks for your help.
Best,
Sonja
_______________________________________________
NEST Users mailing list -- users(a)nest-simulator.org
To unsubscribe send an email to users-leave(a)nest-simulator.org
_______________________________________________
NEST Users mailing list -- users(a)nest-simulator.org
To unsubscribe send an email to users-leave(a)nest-simulator.org
7:05 p.m.
Dear Josh,
it looks like both Norse and Rockpool support
stochastic-gradient-descent-based learning using automatic
differentiation provided by the supported libraries (e.g., pytorch,
jax), i guess augmented with various tricks to make this work with the
temporal and spiking nature of LIF-like models.
bindsnet in contrast seems to be focused on implementing more
traditional learning paradigms that do not necessarily rely on
(back)propagating errors, such as reward-modulated STDP.
Best,
Jakob
On 6/15/20 5:36 PM, Joshua Stern wrote:
> I recently read a potentially relevant review on this subject from
> earlier this year: A review of learning in biologically plausible
> spiking neural networks
>
<https://www.sciencedirect.com/science/article/abs/pii/S0893608019303181?fbclid=IwAR3Zu4aj38sb4pchp-jtzaizsIYvYao-QAZpFC2Ay8Nb672fipM-TDGE9eY>.
>
>
> Does anyone have thoughts on how to think about the differences (esp. in
> terms of learning) between BindsNet, Norse, and Rockpool?
>
> Best wishes,
> Josh
>
>
>
>
>
> On Mon, Jun 15, 2020 at 11:18 AM Benedikt S. Vogler
> <benedikt.s.vogler(a)tum.de <mailto:benedikt.s.vogler@tum.de>> wrote:
>
> Dear Sonja,
>
> nest is a spiking neural network simulator and not a machine
> learning library.
> My current knowledge on SNN is that there is no established learning
> algorithm as there are best-practices for ANNs.
> Here is a brief overview over some methods:
> There ist STDP for correlation learning, reward based STDP is a
> reinforcement learning algorithm still being researched. Another
> option is to train a ANN and then convert it to a SNN.
> SNN don’t have derivative of the activation function, therefore
> backprop is not transferable easily to SNN. There are methods like
> BPTT and e-prop to make backprop work. There might be more methods
> in the area of backprop adaptions. I am not an expert on this.
> SNNs can also be used for reservoir computing which is yet another
> thing (https://gitlab.com/aiCTX/rockpool).
> I am not sure which learning algorithm norse uses, they mention
> Policy gradient.
>
> Kind regards,
> Benedikt S. Vogler
>
>
> > Am 15.06.2020 um 14:24 schrieb s.kraemer96(a)gmx.net
> <mailto:s.kraemer96@gmx.net>:
> >
> > Dear all,
> > I´m writing a master thesis on spiking neural networks and how
> transparent they are. For that I need to implement a SNN network and
> train it. So I started with Brian but that is much to complex and I
> don´t need something special. So I decided to use PyNest. I did all
> the tutorials but I´m missing a tutorial how to train the network. I
> don´t know how to put in a dataset to train the model. I haven´t
> found anything to this topic. So my questions are:
> > 1. Can PyNest train set up a SNN and train it trough data and
> if not is there another simulator who can do this?
> > 2. How do I do it? Is there anything I missed to read or can
> someone send me an example? This would be very helpful.
> > Thanks for your help.
> >
> > Best,
> > Sonja
> > _______________________________________________
> > NEST Users mailing list -- users(a)nest-simulator.org
> <mailto:users@nest-simulator.org>
> > To unsubscribe send an email to users-leave(a)nest-simulator.org
> <mailto:users-leave@nest-simulator.org>
> _______________________________________________
> NEST Users mailing list -- users(a)nest-simulator.org
> <mailto:users@nest-simulator.org>
> To unsubscribe send an email to users-leave(a)nest-simulator.org
> <mailto:users-leave@nest-simulator.org>
>
>
> _______________________________________________
> NEST Users mailing list -- users(a)nest-simulator.org
> To unsubscribe send an email to users-leave(a)nest-simulator.org
>
7:07 p.m.
Dear Josh,
it looks like both Norse and Rockpool support
stochastic-gradient-descent-based learning using automatic
differentiation provided by the supported libraries (e.g., pytorch,
jax), i guess augmented with various tricks to make this work with the
temporal and spiking nature of LIF-like models.
bindsnet in contrast seems to be focused on implementing more
traditional learning paradigms that do not necessarily rely on
(back)propagating errors, such as reward-modulated STDP.
Best,
Jakob
On 6/15/20 5:36 PM, Joshua Stern wrote:
> I recently read a potentially relevant review on this subject from
> earlier this year: A review of learning in biologically plausible
> spiking neural networks
>
<https://www.sciencedirect.com/science/article/abs/pii/S0893608019303181?fbclid=IwAR3Zu4aj38sb4pchp-jtzaizsIYvYao-QAZpFC2Ay8Nb672fipM-TDGE9eY>.
>
>
> Does anyone have thoughts on how to think about the differences (esp. in
> terms of learning) between BindsNet, Norse, and Rockpool?
>
> Best wishes,
> Josh
>
>
>
>
>
> On Mon, Jun 15, 2020 at 11:18 AM Benedikt S. Vogler
> <benedikt.s.vogler(a)tum.de <mailto:benedikt.s.vogler@tum.de>> wrote:
>
> Dear Sonja,
>
> nest is a spiking neural network simulator and not a machine
> learning library.
> My current knowledge on SNN is that there is no established learning
> algorithm as there are best-practices for ANNs.
> Here is a brief overview over some methods:
> There ist STDP for correlation learning, reward based STDP is a
> reinforcement learning algorithm still being researched. Another
> option is to train a ANN and then convert it to a SNN.
> SNN don’t have derivative of the activation function, therefore
> backprop is not transferable easily to SNN. There are methods like
> BPTT and e-prop to make backprop work. There might be more methods
> in the area of backprop adaptions. I am not an expert on this.
> SNNs can also be used for reservoir computing which is yet another
> thing (https://gitlab.com/aiCTX/rockpool).
> I am not sure which learning algorithm norse uses, they mention
> Policy gradient.
>
> Kind regards,
> Benedikt S. Vogler
>
>
> > Am 15.06.2020 um 14:24 schrieb s.kraemer96(a)gmx.net
> <mailto:s.kraemer96@gmx.net>:
> >
> > Dear all,
> > I´m writing a master thesis on spiking neural networks and how
> transparent they are. For that I need to implement a SNN network and
> train it. So I started with Brian but that is much to complex and I
> don´t need something special. So I decided to use PyNest. I did all
> the tutorials but I´m missing a tutorial how to train the network. I
> don´t know how to put in a dataset to train the model. I haven´t
> found anything to this topic. So my questions are:
> > 1. Can PyNest train set up a SNN and train it trough data and
> if not is there another simulator who can do this?
> > 2. How do I do it? Is there anything I missed to read or can
> someone send me an example? This would be very helpful.
> > Thanks for your help.
> >
> > Best,
> > Sonja
> > _______________________________________________
> > NEST Users mailing list -- users(a)nest-simulator.org
> <mailto:users@nest-simulator.org>
> > To unsubscribe send an email to users-leave(a)nest-simulator.org
> <mailto:users-leave@nest-simulator.org>
> _______________________________________________
> NEST Users mailing list -- users(a)nest-simulator.org
> <mailto:users@nest-simulator.org>
> To unsubscribe send an email to users-leave(a)nest-simulator.org
> <mailto:users-leave@nest-simulator.org>
>
>
> _______________________________________________
> NEST Users mailing list -- users(a)nest-simulator.org
> To unsubscribe send an email to users-leave(a)nest-simulator.org
>
6:23 p.m.
Dear Sonja,
Some work that sits on the border between spiking neural network and
deeplearning:
https://arxiv.org/abs/1901.09049
Biologically inspired alternatives to backpropagation through time for
learning in recurrent neural nets
Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj, Robert
Legenstein, Wolfgang Maass
The way how recurrently connected networks of spiking neurons in
the brain acquire powerful information processing capabilities through
learning has remained a mystery. This lack of understanding is linked to
a lack of learning algorithms for recurrent networks of spiking neurons
(RSNNs) that are both functionally powerful and can be implemented by
known biological mechanisms. Since RSNNs are simultaneously a primary
target for implementations of brain-inspired circuits in neuromorphic
hardware, this lack of algorithmic insight also hinders technological
progress in that area. The gold standard for learning in recurrent
neural networks in machine learning is back-propagation through time
(BPTT), which implements stochastic gradient descent with regard to a
given loss function. But BPTT is unrealistic from a biological
perspective, since it requires a transmission of error signals backwards
in time and in space, i.e., from post- to presynaptic neurons. We show
that an online merging of locally available information during a
computation with suitable top-down learning signals in real-time
provides highly capable approximations to BPTT. For tasks where
information on errors arises only late during a network computation, we
enrich locally available information through feedforward eligibility
traces of synapses that can easily be computed in an online manner. The
resulting new generation of learning algorithms for recurrent neural
networks provides a new understanding of network learning in the brain
that can be tested experimentally. In addition, these algorithms provide
efficient methods for on-chip training of RSNNs in neuromorphic hardware.
It is my understanding that work is going on to implement this method in
NEST also.
Greets,
Wouter
On 15-Jun-20 14:24, s.kraemer96(a)gmx.net wrote:
Dear all,
I´m writing a master thesis on spiking neural networks and how transparent they are. For
that I need to implement a SNN network and train it. So I started with Brian but that is
much to complex and I don´t need something special. So I decided to use PyNest. I did all
the tutorials but I´m missing a tutorial how to train the network. I don´t know how to put
in a dataset to train the model. I haven´t found anything to this topic. So my questions
are:
1. Can PyNest train set up a SNN and train it trough data and if not is there another
simulator who can do this?
2. How do I do it? Is there anything I missed to read or can someone send me an
example? This would be very helpful.
Thanks for your help.
Best,
Sonja
_______________________________________________
NEST Users mailing list -- users(a)nest-simulator.org
To unsubscribe send an email to users-leave(a)nest-simulator.org
--
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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1405
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1405
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6 comments
7 participants
participants (7)
-
Benedikt S. Vogler -
Jakob Jordan -
Jakob Jordan -
Jens Egholm -
Joshua Stern -
s.kraemer96＠gmx.net -
Wouter Klijn