  At present, implementation of learning mechanisms in spiking neural networks
(SNN) cannot be considered as a solved scientific problem despite plenty of SNN
learning algorithms proposed. It is also true for SNN implementation of
reinforcement learning (RL), while RL is especially important for SNNs because
of its close relationship to the domains most promising from the viewpoint of
SNN application such as robotics. In the present paper, I describe an SNN
structure which, seemingly, can be used in wide range of RL tasks. The
distinctive feature of my approach is usage of only the spike forms of all
signals involved - sensory input streams, output signals sent to actuators and
reward/punishment signals. Besides that, selecting the neuron/plasticity
models, I was guided by the requirement that they should be easily implemented
on modern neurochips. The SNN structure considered in the paper includes
spiking neurons described by a generalization of the LIFAT (leaky
integrate-and-fire neuron with adaptive threshold) model and a simple spike
timing dependent synaptic plasticity model (a generalization of
dopamine-modulated plasticity). My concept is based on very general assumptions
about RL task characteristics and has no visible limitations on its
applicability. To test it, I selected a simple but non-trivial task of training
the network to keep a chaotically moving light spot in the view field of an
emulated DVS camera. Successful solution of this RL problem by the SNN
described can be considered as evidence in favor of efficiency of my approach.
