Wiki source code of NESTML

Version 14.1 by abonard on 2025/04/10 15:15
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3 * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
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5 * ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
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7 === **Beginner** ===
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9 === [[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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11 **Level**: beginner(%%) **Type**: interactive tutorial
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13 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.
14 === [[Creating neuron models – Izhikevich tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/izhikevich/nestml_izhikevich_tutorial.html||rel=" noopener noreferrer" target="_blank"]] ===
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16 **Level**: beginner(%%) **Type**: interactive tutorial
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18 Learn how to start to use NESTML by writing the Izhikevich spiking neuron model in NESTML.
19 === **Advanced** ===
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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"]] ===
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23 **Level**: advanced(%%) **Type**: interactive tutorial
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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.