Creating neuron models
Learn how to write the Izhikevich spiking neuron model in NESTML.
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.
Learn how to model a dendritic action potential in an existing NESTML neuron.
Implement the Ornstein-Uhlenbeck process in NESTML and use it to inject a noise current into a neuron.
Creating synapse models
An STDP window describes how the strength of the synapse changes as a function of the relative timing of pre- and postsynaptic spikes. Several different STDP model variants with different window functions are implemented.
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.
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.