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3	3	* ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
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	5	+* ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
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5	5	=== **Beginner** ===
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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"]] ===
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9	9	**Level**: beginner(%%) **Type**: interactive tutorial
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11	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.
	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.
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