# How to fix the value of a synapse after learning

**URL:** <https://brian.discourse.group/t/how-to-fix-the-value-of-a-synapse-after-learning/889>\
**Category:** Support\
**Tags:** synapses, plasticity\
**Created:** [30 January 2023 14:21 UTC](https://brian.discourse.group/t/how-to-fix-the-value-of-a-synapse-after-learning/889 "2023-01-30T14:21:06Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![caccola816](https://avatars.discourse-cdn.com/v4/letter/c/f17d59/32.png) [@caccola816](https://brian.discourse.group/u/caccola816)\
**Post date:** [30 January 2023 14:21 UTC](https://brian.discourse.group/t/how-to-fix-the-value-of-a-synapse-after-learning/889/1 "2023-01-30T14:21:06Z")

</div>

# Description of problem

Hello! I’m trying to use spiking neural network to learn MNIST dataset. I defined a STDP synapse with a variable ‘learned’ that it should be set equal to 0 after learning, so that the weights that encode a specific input remain fixed.

# Minimal code to reproduce problem

```auto
class Simulation():

     def __init__ ():
          self.excpop = NeuronGroup()
          self.input_layer = NeuronGroup()
        self.Sinput = Synapses(self.input_layer, self.excpop,
                         '''w:1 
                            dx / dt = -x / tau_p : 1 (event-driven)
                            dy / dt = -y / tau_m : 1 (event-driven)
                            plastic : 1 (shared)
                            W_max : 1 (shared)
                            n_p : 1 (shared)
                            learned : 1''',
                            on_pre='''scx+=w
                                      x += apre
                                      w = clip(w-w*n_m*y*plastic*learned/W_max, 0, W_max)''',
                            on_post='''y += apost
                                       w = clip(w+(1-w/W_max)*n_p*(x - xtar)*plastic*learned, 0, W_max)''',
                            name='Sinput')
              self.Sinput.learned = '1'
        def train():
               self.net.run()
               #after learning
               self.Sinput['w>0.3'].learned = 0.

```

But it doesn’t work. the variable self.Sinput.learned that are defined in each synapse are always 1 and so the network keeps chaning the weights. I don’t understand why i can’t set it to 0 with that expression.  
Is the idea a good way to fix synapses after learning?

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**Author:** ![mstimberg](https://yyz2.discourse-cdn.com/free1/user_avatar/brian.discourse.group/mstimberg/32/11_2.png) [@mstimberg](https://brian.discourse.group/u/mstimberg)\
**Post date:** [30 January 2023 15:43 UTC](https://brian.discourse.group/t/how-to-fix-the-value-of-a-synapse-after-learning/889/2 "2023-01-30T15:43:30Z")

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Hi @caccola816. This is a perfectly reasonable way of doing things, but unfortunately you ran into a confusing syntax issue (on the good side, the fix is straightforward).

When you write: `self.Sinput['w>0.3']` you are creating a so-called “synaptic subgroup”, i.e. a group of all synapses that have `w>0.3`. Currently, such a group is not very useful: it can only be used as a stand-in for synaptic indices. It does not have the full functionality of a `Synapses` object, in particular you cannot access synaptic variables via such a subgroup. If you tried something like `print(self.Sinput['w>0.3'] .learned)` you would get an `AttributeError`, stating that it does not have a `learned` attribute. Now, in Python assigning to a non-existing attribute of an object will create that attribute, i.e. your “synaptic subgroup” will end up with a new attribute `learned` with the value 0, but this will not be in any way connected to the actual synaptic variable. I know this is all very confusing, and I am currently working on a quite big change to Brian’s syntax handling ([Non-contiguous subgroups by mstimberg · Pull Request #1229 · brian-team/brian2 · GitHub](https://github.com/brian-team/brian2/pull/1229)) which should also take care of this issue.

Until then, there is fortunately a different way to express this: instead of indexing the `Synapses` object, you can index the _synaptic variable_. I.e., the following will work as you’d expect:

```python
self.Sinput.learned['w>0.3'] = 0.

```

Here’s a simplified version of your code that shows how after 1s, all synaptic weights that were above 0.3 stop updating:

> **code**
>
> ```auto
> from brian2 import *
> 
> class Simulation:
> def __init__ (self):
> self.excpop = PoissonGroup(10, rates=10*Hz)
> self.input_layer = PoissonGroup(10, rates=10*Hz)
> self.Sinput = Synapses(self.input_layer, self.excpop,
> '''w : 1 
> dx / dt = -x / (20*ms) : 1 (event-driven)
> dy / dt = -y / (20*ms) : 1 (event-driven)
> W_max : 1 (constant, shared)
> learned : 1''',
> on_pre='''x += 1
> w = clip(w + y*learned, 0, W_max)''',
> on_post='''y -= 1
> w = clip(w + x*learned, 0, W_max)''',
> name='Sinput')
> self.Sinput.connect(j='i')
> self.Sinput.w = 'rand()'
> self.Sinput.W_max = 1
> self.Sinput.learned = 1
> self.weight_mon = StateMonitor(self.Sinput, 'w', record=True, dt=1*ms)
> 
> self.net = Network(self.excpop, self.input_layer, self.Sinput, self.weight_mon)
> 
> def train(self):
> self.net.run(1*second)
> # after learning
> self.Sinput.learned['w>0.3'] = 0.
> 
> def test(self):
> self.net.run(1*second)
> 
> def plot(self):
> plt.plot(self.weight_mon.t/ms, self.weight_mon.w.T)
> plt.axvline(1*second/ms, color='gray', linestyle='--')
> plt.xlabel('Time (ms)')
> plt.ylabel('Weight')
> plt.show()
> 
> if __name__ == ' __main__':
> sim = Simulation()
> sim.train()
> sim.test()
> sim.plot()
> 
> ```

![Figure_1](https://global.discourse-cdn.com/free1/uploads/brian/original/1X/b1a40e45ca56f6f9789198f5e0d2481f94f5b24b.png)

---

<div class="post-metadata">

**Author:** ![caccola816](https://avatars.discourse-cdn.com/v4/letter/c/f17d59/32.png) [@caccola816](https://brian.discourse.group/u/caccola816)\
**Post date:** [30 January 2023 17:28 UTC](https://brian.discourse.group/t/how-to-fix-the-value-of-a-synapse-after-learning/889/3 "2023-01-30T17:28:18Z")

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Yeaah now it works! 🙂

Thank you very much! Indeed I was getting very confused results eheh