The Koniocortex-Like Network: A New Biologically Plausible Unsupervised Neural Network

In this paper we present a new unsupervised neural network whose architecture resembles the koniocortex, the first cortical layer receiving sensory inputs. For easiness, its properties were incorporated in a step by step manner along successive network versions. In some cases, the version improvement consists in the replacement of a non-biological property by a biologically plausible one. Initially (version 0) the network was merely an scaffold implementing the Bayes Decision Rule. The first network version incorporated metaplasticity and intrinsic plasticity, but neural competition was not biological. In a second version, competition naturally occurred due to the interplay between lateral inhibition and homeostatic properties. Finally, in the koniocortex-like network, competition and pattern classification emerges naturally due to the interplay of inhibitory interneurons and previous version's properties. An example of numerical character recognition is presented for illustrating the main characteristics of the network.