Creating neuron models

• Izhikevich tutorial

  Learn how to write the Izhikevich spiking neuron model in NESTML.

• Spike-frequency adaptation

  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.

• Active dendrite tutorial

  Learn how to model a dendritic action potential in an existing NESTML neuron.

• Ornstein-Uhlenbeck noise

  Implement the Ornstein-Uhlenbeck process in NESTML and use it to inject a noise current into a neuron.

• Inhomogeneous Poisson generator

  Create a model that emits spikes according to an inhomogeneous Poisson distribution.

• Sequence learning network

  A network learns to predict and autonomously replay sequences of items.

Creating synapse models

• STDP windows

  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.

• Triplet STDP synapse

  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.

• Third-factor modulated STDP

  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.

• 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.