  Nowadays we witness a miniaturisation trend in the semiconductor industry
backed up by groundbreaking discoveries and designs in nanoscale
characterisation and fabrication. To facilitate the trend and produce ever
smaller, faster and cheaper computing devices, the size of nanoelectronic
devices is now reaching the scale of atoms or molecules - a technical goal
undoubtedly demanding for novel devices. Following the trend, we explore an
unconventional route of implementing a reservoir computing on a single protein
molecule and introduce neuromorphic connectivity with a small-world networking
property. We have chosen Izhikevich spiking neurons as elementary processors,
corresponding to the atoms of verotoxin protein, and its molecule as a
'hardware' architecture of the communication networks connecting the
processors. We apply on a single readout layer various training methods in a
supervised fashion to investigate whether the molecular structured Reservoir
Computing (RC) system is capable to deal with machine learning benchmarks. We
start with the Remote Supervised Method, based on
Spike-Timing-Dependent-Plasticity, and carry on with linear regression and
scaled conjugate gradient back-propagation training methods. The RC network is
evaluated as a proof-of-concept on the handwritten digit images from the MNIST
dataset and demonstrates acceptable classification accuracy in comparison with
other similar approaches.
