  This paper presents an extension of the BrainScaleS accelerated analog
neuromorphic hardware model. The scalable neuromorphic architecture is extended
by the support for multi-compartment models and non-linear dendrites. These
features are part of a \SI{65}{\nano\meter} prototype ASIC. It allows to
emulate different spike types observed in cortical pyramidal neurons: NMDA
plateau potentials, calcium and sodium spikes. By replicating some of the
structures of these cells, they can be configured to perform coincidence
detection within a single neuron. Built-in plasticity mechanisms can modify not
only the synaptic weights, but also the dendritic synaptic composition to
efficiently train large multi-compartment neurons. Transistor-level simulations
demonstrate the functionality of the analog implementation and illustrate
analogies to biological measurements.
