Summary

• Page properties (2 modified, 0 added, 0 removed)

Details

Page properties

Author

...	...	@@ -1,1 +1,1 @@
1		-XWiki.jessicamitchell
	1	+XWiki.abonard

Content

...	...	@@ -1,33 +2,26 @@
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.
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.
33		-