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Changes for page NESTML

Last modified by abonard on 2025/06/13 16:09
From version 1.1
edited by adavison
on 2023/02/27 16:46
Change comment: There is no comment for this version
To version 21.1
edited by abonard
on 2025/05/23 13:19
Change comment: There is no comment for this version

Summary

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1 -XWiki.adavison
1 +XWiki.abonard
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1 -Available tutorials:
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3 -* [[Izhikevich tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/izhikevich/nestml_izhikevich_tutorial.html||rel=" noopener noreferrer" target="_blank"]] (beginner)
4 -* [[Active dendrite tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/active_dendrite/nestml_active_dendrite_tutorial.html||rel=" noopener noreferrer" target="_blank"]] (advanced)
5 -* [[Dopamine-modulated STDP synapse tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.html||rel=" noopener noreferrer" target="_blank"]] (advanced)
6 -* [[Ornstein-Uhlenbeck noise tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/ornstein_uhlenbeck_noise/nestml_ou_noise_tutorial.html||rel=" noopener noreferrer" target="_blank"]] (advanced)
7 -* [[STDP synapse tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.html||rel=" noopener noreferrer" target="_blank"]] (advanced)
8 -* [[STDP windows tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/stdp_windows/stdp_windows.html||rel=" noopener noreferrer" target="_blank"]] (advanced)
9 9  
3 +* ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
4 +
5 +=== **Beginner** ===
6 +
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"]] ===
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9 +**Level**: beginner(%%) **Type**: interactive tutorial
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11 +Spike-frequency adaptation (SFA) is the empirically observed phenomenon where the firing rate of a neuron decreases for a sustained, constant stimulus. Learn how to model SFA using threshold adaptation and an adaptation current.
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