  Sophisticated machine learning struggles to transition onto battery-operated
devices due to the high-power consumption of neural networks. Researchers have
turned to neuromorphic engineering, inspired by biological neural networks, for
more efficient solutions. While previous research focused on artificial neurons
and synapses, an essential component has been overlooked: dendrites. Dendrites
transmit inputs from synapses to the neuron's soma, applying both passive and
active transformations. However, neuromorphic circuits replace these
sophisticated computational channels with metallic interconnects. In this
study, we introduce a versatile circuit that emulates a segment of a dendrite
which exhibits gain, introduces delays, and performs integration. We show how
sound localisation - a biological example of dendritic computation - is not
possible with the existing passive dendrite circuits but can be achieved using
this proposed circuit. We also find that dendrites can form bursting neurons.
This significant discovery suggests the potential to fabricate neural networks
solely comprised of dendrite circuits.
