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Author

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1		-XWiki.jessicamitchell
	1	+XWiki.abonard

Content

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1		-Available tutorials:
2	2
3		-=== [[Izhikevich tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/izhikevich/nestml_izhikevich_tutorial.html||rel=" noopener noreferrer" target="_blank"]] ===
4	4
5		-//Level: beginner//
	3	+* ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
6	6
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"]] ===
	5	+* ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
9	9
10		-//Level: advanced//
	7	+=== **Beginner** ===
11	11
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"]] ===
	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"]] ===
14	14
15		-//Level: advanced//
	11	+**Level**: beginner(%%) **Type**: interactive tutorial
16	16
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"]] ===
	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"]] ===
19	19
20		-//Level: advanced//
	16	+**Level**: beginner(%%) **Type**: interactive tutorial
21	21
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"]] ===
	18	+Learn how to start to use NESTML by writing the Izhikevich spiking neuron model in NESTML.
	19	+=== **Advanced** ===
24	24
25		-//Level: advanced//
	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"]] ===
26	26
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"]] ===
	23	+**Level**: advanced(%%) **Type**: interactive tutorial
29	29
30		-//Level: advanced//
	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	+=== [[Creating synapse models – Triplet STDP synapse>>https://nestml.readthedocs.io/en/latest/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.html||rel=" noopener noreferrer" target="_blank"]] ===
31	31
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.
	28	+**Level**: advanced(%%) **Type**: interactive tutorial
33	33
	30	+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. In this tutorial, we will learn to formulate triplet rule (which considers sets of three spikes, i.e., two presynaptic and one postsynaptic spikes or two postsynaptic and one presynaptic spikes) for Spike Timing-Dependent Plasticity (STDP) learning model using NESTML and simulate it with NEST simulator.
	31	+