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3.1 | 3 | * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== ))) |
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41.1 | 5 | * ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== ))) |
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3.1 | 7 | === **Beginner** === |
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3.1 | 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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3.1 | 11 | **Level**: beginner(%%) **Type**: interactive tutorial |
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3.1 | 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. |
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40.1 | 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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40.1 | 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. | ||
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41.1 | 19 | === **Advanced** === |
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41.1 | 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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