A brief introduction to Weightless Neural Systems

Mimicking biological neurons by focusing on the excitatory/inhibitory decoding performed by the dendritic trees is a different and attractive alternative to the integrate-and-fire McCullogh-Pitts neuron stylisation. In such alternative analogy, neurons can be seen as a set of RAM nodes addressed by Boolean inputs and producing Boolean outputs. The shortening of the semantic gap between the synaptic-centric model introduced by the McCullogh-Pitts neuron and the dominating, binary digital, computational environment, is among the interesting benefits of the weightless neural approach. This paper presents an overview of the most representative paradigms of weightless neural systems and corresponding applications, at abstraction levels ranging from pattern recognition to artificial consciousness.