Beginner
Creating neuron models – Spike-frequency adaptation (SFA)
Level: beginner Type: interactive tutorial
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.
Creating neuron models – Izhikevich tutorial
Level: beginner Type: interactive tutorial
Learn how to start to use NESTML by writing the Izhikevich spiking neuron model in NESTML.
Advanced
Creating synapse models – Dopamine-modulated STDP synapse
Level: advanced Type: interactive tutorial
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.
Creating synapse models – Triplet STDP synapse
Level: advanced Type: interactive tutorial
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.