  Spiking neural networks (SNNs) with a lattice architecture are introduced in
this work, combining several desirable properties of SNNs and self-organized
maps (SOMs). Networks are trained with biologically motivated, unsupervised
learning rules to obtain a self-organized grid of filters via cooperative and
competitive excitatory-inhibitory interactions. Several inhibition strategies
are developed and tested, such as (i) incrementally increasing inhibition level
over the course of network training, and (ii) switching the inhibition level
from low to high (two-level) after an initial training segment. During the
labeling phase, the spiking activity generated by data with known labels is
used to assign neurons to categories of data, which are then used to evaluate
the network's classification ability on a held-out set of test data. Several
biologically plausible evaluation rules are proposed and compared, including a
population-level confidence rating, and an $n$-gram inspired method. The
effectiveness of the proposed self-organized learning mechanism is tested using
the MNIST benchmark dataset, as well as using images produced by playing the
Atari Breakout game.
