Changes for page NESTML

Last modified by abonard on 2025/06/13 16:09

From version 41.1
edited by abonard
on 2025/06/13 16:09
Change comment: There is no comment for this version
To version 43.1
edited by abonard
on 2025/06/13 16:09
Change comment: There is no comment for this version

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23 23  **Level**: advanced(%%) **Type**: interactive tutorial
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25 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"]] ===
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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.
31 +=== [[Creating synapse models – Active dendrite third-factor STDP synapse>>https://nestml.readthedocs.io/en/latest/tutorials/stdp_third_factor_active_dendrite/stdp_third_factor_active_dendrite.html||rel=" noopener noreferrer" target="_blank"]] ===
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33 +**Level**: advanced(%%) **Type**: interactive tutorial
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35 +An STDP rule that is modulated by a “third factor”, in this case the dendritic action potential current of the postsynaptic neuron with an active dendrite.
36 +In this tutorial, the neuron with dendritic action potentials from the NESTML active dendrite tutorial is combined with a spike-timing dependent synaptic plasticity model. The dendritic action potential current acts as the “third factor” in the learning rule (in addition to pre- and postsynaptic spike timings) and is used to gate the weight update: changes in the weight can only occur during the postsynaptic neuron’s dendritic action potential.
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