Temporal coding is one approach to representing information in spiking neural
networks. An example of its application is the location of sounds by barn owls
that requires especially precise temporal coding. Dependent upon the azimuthal
angle, the arrival times of sound signals are shifted between both ears. In
order to deter- mine these interaural time differences, the phase difference of
the signals is measured. We implemented this biologically inspired network on a
neuromorphic hardware system and demonstrate spike-timing dependent plasticity
on an analog, highly accelerated hardware substrate. Our neuromorphic
implementation enables the resolution of time differences of less than 50 ns.
On-chip Hebbian learning mechanisms select inputs from a pool of neurons which
code for the same sound frequency. Hence, noise caused by different synaptic
delays across these inputs is reduced. Furthermore, learning compensates for
variations on neuronal and synaptic parameters caused by device mismatch
intrinsic to the neuromorphic substrate.Comment: 7 pages, 7 figures, presented at IJCNN 2013 in Dallas, TX, USA. IJCNN
2013. Corrected version with updated STDP curves IJCNN 201
