  In the reinforcement learning (RL) tasks, the ability to predict receiving
reward in the near or more distant future means the ability to evaluate the
current state as more or less close to the target state (labelled by the reward
signal). In the present work, we utilize a spiking neural network (SNN) to
predict time to the next target event (reward - in case of RL). In the context
of SNNs, events are represented as spikes emitted by network neurons or input
nodes. It is assumed that target events are indicated by spikes emitted by a
special network input node. Using description of the current state encoded in
the form of spikes from the other input nodes, the network should predict
approximate time of the next target event. This research paper presents a novel
approach to learning the corresponding predictive model by an SNN consisting of
leaky integrate-and-fire (LIF) neurons. The proposed method leverages specially
designed local synaptic plasticity rules and a novel columnar-layered SNN
architecture. Similar to our previous works, this study places a strong
emphasis on the hardware-friendliness of the proposed models, ensuring their
efficient implementation on modern and future neuroprocessors. The approach
proposed was tested on a simple reward prediction task in the context of one of
the RL benchmark ATARI games, ping-pong. It was demonstrated that the SNN
described in this paper gives superior prediction accuracy in comparison with
precise machine learning techniques, such as decision tree algorithms and
convolutional neural networks.
