Attention: Due to an upgrade beginning on Friday, the 22nd August 2025, any new collabs, groups and any changes to teams will not be kept beyond the weekend. User creation will also be disabled during this time. The actual upgrade will take place on Wednesday, 27th August. On that day, any service requiring a Keycloak login will be unavailable for the duration of the upgrade. Thank you for your understanding as we complete this important work.


Changes for page NESTML

Last modified by abonard on 2025/06/13 16:09
From version 40.1
edited by abonard
on 2025/06/13 16:09
Change comment: There is no comment for this version
To version 41.1
edited by abonard
on 2025/06/13 16:09
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -2,6 +2,8 @@
2 2  
3 3  * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
4 4  
5 +* ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
6 +
5 5  === **Beginner** ===
6 6  
7 7  === [[Creating neuron models – Spike-frequency adaptation (SFA)>>https://nestml.readthedocs.io/en/latest/tutorials/spike_frequency_adaptation/nestml_spike_frequency_adaptation_tutorial.html||rel=" noopener noreferrer" target="_blank"]] ===
... ... @@ -14,4 +14,11 @@
14 14  **Level**: beginner(%%) **Type**: interactive tutorial
15 15  
16 16  Learn how to start to use NESTML by writing the Izhikevich spiking neuron model in NESTML.
19 +=== **Advanced** ===
17 17  
21 +=== [[Creating synapse models – Dopamine-modulated STDP synapse>>https://nestml.readthedocs.io/en/latest/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.html||rel=" noopener noreferrer" target="_blank"]] ===
22 +
23 +**Level**: advanced(%%) **Type**: interactive tutorial
24 +
25 +Adding dopamine modulation to the weight update rule of an STDP synapse allows it to be used in reinforcement learning tasks. This allows a network to learn which of the many cues and actions preceding a reward should be credited for the reward. In this tutorial, a dopamine-modulated STDP model is created in NESTML, and we characterize the model before using it in a network (reinforcement) learning task.
26 +