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Author

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1		-XWiki.abonard
	1	+XWiki.jessicamitchell

Content

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	1	+Available tutorials:
1	1
	3	+=== [[Izhikevich tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/izhikevich/nestml_izhikevich_tutorial.html||rel=" noopener noreferrer" target="_blank"]] ===
2	2
3		-* ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
	5	+//Level: beginner//
4	4
5		-* ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
	7	+Learn how to write the Izhikevich spiking neuron model in NESTML.
	8	+=== [[Active dendrite tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/active_dendrite/nestml_active_dendrite_tutorial.html||rel=" noopener noreferrer" target="_blank"]] ===
6	6
7		-=== **Beginner** ===
	10	+//Level: advanced//
8	8
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"]] ===
	12	+Learn how to model a dendritic action potential in an existing NESTML neuron.
	13	+=== [[Dopamine-modulated STDP synapse tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.html||rel=" noopener noreferrer" target="_blank"]] ===
10	10
11		-**Level**: beginner(%%) **Type**: interactive tutorial
	15	+//Level: advanced//
12	12
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"]] ===
	17	+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.
	18	+=== [[Ornstein-Uhlenbeck noise tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/ornstein_uhlenbeck_noise/nestml_ou_noise_tutorial.html||rel=" noopener noreferrer" target="_blank"]] ===
15	15
16		-**Level**: beginner(%%) **Type**: interactive tutorial
	20	+//Level: advanced//
17	17
18		-Learn how to start to use NESTML by writing the Izhikevich spiking neuron model in NESTML.
19		-=== **Advanced** ===
	22	+Implement the Ornstein-Uhlenbeck process in NESTML and use it to inject a noise current into a neuron.
	23	+=== [[STDP synapse tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.html||rel=" noopener noreferrer" target="_blank"]] ===
20	20
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"]] ===
	25	+//Level: advanced//
22	22
23		-**Level**: advanced(%%) **Type**: interactive tutorial
	27	+A triplet STDP rule is sensitive to third-order correlations of pre- and postsynaptic spike times, and accounts better for experimentally seen dependence on timing and frequency.
	28	+=== [[STDP windows tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/stdp_windows/stdp_windows.html||rel=" noopener noreferrer" target="_blank"]] ===
24	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.
	30	+//Level: advanced//
26	26
	32	+An STDP window describes how the strength of the synapse changes as a function of the relative timing of pre- and postsynaptic spikes. Several different STDP model variants with different window functions are implemented.
	33	+