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3.1 | 3 | * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== ))) |
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32.1 | 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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31.1 | 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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31.1 | 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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32.1 | 19 | === **Advanced** === |
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32.1 | 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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33.1 | 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"]] === |
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32.1 | 27 | |
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33.1 | 28 | **Level**: advanced(%%) **Type**: interactive tutorial |
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| 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. | ||
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