This version of the documentation is NOT an official release. You are looking at ‘latest’, which is in active and ongoing development. You can change versions on the bottom left of the screen.

pp_cond_exp_mc_urbanczik – Two-compartment point process neuron with conductance-based synapses


pp_cond_exp_mc_urbanczik is an implementation of a two-compartment spiking point process neuron with conductance-based synapses as it is used in 1. It is capable of connecting to an Urbanczik synapse.

The model has two compartments: soma and dendrite, labeled as s and p, respectively. Each compartment can receive spike events and current input from a current generator. Additionally, an external (rheobase) current can be set for each compartment.

Synapses, including those for injection external currents, are addressed through the receptor types given in the receptor_types entry of the state dictionary. Note that in contrast to the single-compartment models, all synaptic weights must be positive numbers! The distinction between excitatory and inhibitory synapses is made explicitly by specifying the receptor type of the synapse. For example, receptor_type=dendritic_exc results in an excitatory input and receptor_type=dendritic_inh results in an inhibitory input to the dendritic compartment.

Multicompartment models and synaptic delays

Note that in case of multicompartment models that represent the dendrite explicitly, the interpretation of the synaptic delay in NEST requires careful consideration. In NEST, the delay is at least one simulation time step and is assumed to be located entirely at the postsynaptic side. For point neurons, it represents the time it takes for an incoming spike to travel along the postsynaptic dendrite before it reaches the soma, see panel a). Conversely, if the synaptic weight depends on the state of the postsynaptic neuron, the delay also represents the time it takes for the information on the state to propagate back through the dendrite to the synapse.

For multicompartment models in NEST, this means the delay is positioned directly behind the incoming synapse, that is, before the first dendritic compartment on the postsynaptic side, see panel b). Therefore, the delay specified in the synapse model does not account for any delay that might be associated with information traveling through the explicitly modeled dendritic compartments.

In the Urbanczik synapse, the change of the synaptic weight is driven by an error signal, which is the difference between the firing rate of the soma (derived from the somatic spike train \(S_{post}\)) and the dendritic prediction of the firing rate of the soma (derived from the dendritic membrane potential \(V\)). The original publication 1 does not assume any delay in the interaction between the soma and the dendritic compartment. Therefore, we evaluate the firing rate and the dendritic prediction at equal time points to calculate the error signal at that time point. Due to the synaptic delay \(d\), the synapse combines a delayed version of the error signal with the presynaptic spike train (\(S_{pre}\)), see panel c).


Figure 40 a) Two point neurons (red circles pre and post) connected via a synapse. In NEST, the delay is entirely on the postsynaptic side, and in the case of point neurons, it is interpreted as the dendritic delay. b) Two two-compartment neuron models composed of a somatic (green) and a dendritic (blue) compartment. The soma of the presynaptic neuron is connected to the dendrite of the postsynaptic neuron. The synaptic delay is located behind the synapse and before the dendrite. c) Time trace of the State variables that enter the Urbanczik-Senn rule. Due to the synaptic delay \(d\), the presynaptic spike train (top) is combined with a delayed version of the postsynaptic quantities; the dendritic membrane potential (middle) and the somatic spike train (bottom).


The following parameters can be set in the status dictionary. Parameters for each compartment are collected in a sub-dictionary; these sub-dictionaries are called “soma” and “dendritic”, respectively. In the list below, these parameters are marked with an asterisk.



Membrane potential



Leak reversal potential



Capacity of the membrane



Excitatory reversal potential



Inhibitory reversal potential



Leak conductance



Rise time of the excitatory synaptic alpha function



Rise time of the inhibitory synaptic alpha function



Constant input current



Coupling between soma and dendrite



Coupling between dendrite and soma



Duration of refractory period

See Weight adaptation according to the Urbanczik-Senn plasticity to learn more.


The neuron model uses standard units of NEST instead of the unitless quantities used in 1.


All parameters that occur for both compartments are stored as C arrays, with index 0 being soma.




SpikeEvent, CurrentEvent, DataLoggingRequest



R. Urbanczik, W. Senn (2014). Learning by the Dendritic Prediction of Somatic Spiking. Neuron, 81, 521 - 528.