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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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2.1 | 8 | |
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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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2.1 | 10 | |
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3.1 | 11 | **Level**: beginner(%%) **Type**: interactive tutorial |
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2.1 | 12 | |
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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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2.1 | 15 | |
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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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40.1 | 20 | |
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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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42.1 | 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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41.1 | 27 | |
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42.1 | 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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43.1 | 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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42.1 | 32 | |
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43.1 | 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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44.1 | 37 | === [[Creating synapse models – STDP windows>>https://nestml.readthedocs.io/en/latest/tutorials/stdp_windows/stdp_windows.html||rel=" noopener noreferrer" target="_blank"]] === |
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43.1 | 38 | |
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44.1 | 39 | **Level**: advanced(%%) **Type**: interactive tutorial |
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| 41 | An STDP window describes how the strength of the synapse changes as a function of the relative timing of pre- and postsynaptic spikes. In this tutorial we will be implementing several different STDP model variants with different window functions in NESTML. | ||
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45.1 | 42 | === [[Creating neuron models – Inhomogeneous Poisson generator>>https://nestml.readthedocs.io/en/latest/tutorials/inhomogeneous_poisson/inhomogeneous_poisson.html||rel=" noopener noreferrer" target="_blank"]] === |
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44.1 | 43 | |
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45.1 | 44 | **Level**: advanced(%%) **Type**: interactive tutorial |
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| 46 | This tutorial will show you how to create a model that emits spikes according to an inhomogeneous Poisson distribution. | ||
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46.1 | 47 | === [[Creating neuron models – Ornstein-Uhlenbeck noise tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/ornstein_uhlenbeck_noise/nestml_ou_noise_tutorial.html||rel=" noopener noreferrer" target="_blank"]] === |
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45.1 | 48 | |
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46.1 | 49 | **Level**: advanced(%%) **Type**: interactive tutorial |
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| 51 | This tutorial will show you how to implement the Ornstein-Uhlenbeck process in NESTML and use it to inject a noise current into a neuron. | ||
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47.1 | 52 | === [[Creating neuron models – Active dendrite tutorial>>https://nestml.readthedocs.io/en/latest/tutorials/active_dendrite/nestml_active_dendrite_tutorial.html||rel=" noopener noreferrer" target="_blank"]] === |
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46.1 | 53 | |
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47.1 | 54 | **Level**: advanced(%%) **Type**: interactive tutorial |
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| 56 | This tutorial will show you how to model a dendritic action potential in an existing NESTML neuron. | ||
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