  This paper targets the problem of encoding information into binary cell
assemblies. Spiking neural networks and k-winners-take-all models are two
common approaches, but the first is hard to use for information processing and
the second is too simple and lacks important features of the first. We present
an intermediate model that shares the computational ease of kWTA and has more
flexible and richer dynamics. It uses explicit inhibitory neurons to balance
and shape excitation through an iterative procedure. This leads to a recurrent
interaction between inhibitory and excitatory neurons that better adapts to the
input distribution and performs such computations as habituation,
decorrelation, and clustering. To show these, we investigate Hebbian-like
learning rules and propose a new learning rule for binary weights with multiple
stabilization mechanisms. Our source code is publicly available.
