title: Equivalence between RAM-based neural networks and probabilistic automata.
abstract: In this letter, the computational power of a class of random access memory (RAM)-based neural networks, called general single-layer sequential weightless neural networks (GSSWNNs), is analyzed. The theoretical results presented, besides helping the understanding of the temporal behavior of these networks, could also provide useful insights for the developing of new learning algorithms.
authors.forename: Marcilio C P
authors.initials: MC
authors.lastname: de Souto
authors.forename: Teresa B
authors.initials: TB
authors.lastname: Ludermir
authors.forename: Wilson R
authors.initials: WR
authors.lastname: de Oliveira
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*