title: Chemical implementation and thermodynamics of collective neural networks.
abstract: The chemical implementation of a neuron and connections among neurons described in prior work is used to construct collective neural networks. With stated approximations, these chemical networks are reduced to networks of the Hopfield type. Chemical networks approaching a stationary or equilibrium state provide a Liapunov function with the same extremal properties as Hopfield's energy function. Numerical comparisons of chemical and Hopfield networks with small numbers (2-16) of neurons show agreement on the results of given computations.
authors.affiliation: Max-Planck-Institut für Biophysikalische Chemie, Göttingen, Federal Republic of Germany.
authors.forename: A
authors.initials: A
authors.lastname: Hjelmfelt
authors.forename: J
authors.initials: J
authors.lastname: Ross
mesh_terms: D002384:Catalysis; D055598:Chemical Phenomena; D002621:Chemistry; D007700:Kinetics; D016571:Neural Networks, Computer; D013816:Thermodynamics