  This paper presents a biologically plausible method for converting
real-valued input into spike trains for processing with spiking neural
networks. The proposed method mimics the adaptive behaviour of retinal ganglion
cells and allows input neurons to adapt their response to changes in the
statistics of the input. Thus, rather than passively receiving values and
forwarding them to the hidden and output layers, the input layer acts as a
self-regulating filter which emphasises deviations from the average while
allowing the input neurons to become effectively desensitised to the average
itself. Another merit of the proposed method is that it requires only one input
neuron per variable, rather than an entire population of neurons as in the case
of the commonly used conversion method based on Gaussian receptive fields. In
addition, since the statistics of the input emerge naturally over time, it
becomes unnecessary to pre-process the data before feeding it to the network.
This enables spiking neural networks to process raw, non-normalised streaming
data. A proof-of-concept experiment is performed to demonstrate that the
proposed method operates as expected.
