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.
Dopamine-modulated STDP synapse
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.