title: Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.
abstract: The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains.
authors.affiliation: Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Tokyo 153-8902, Japan. Electronic address: alex.w.nz@gmail.com.
authors.forename: Alexander
authors.initials: A
authors.lastname: Woodward
authors.affiliation: Departamento de Ciencias de la Computacion, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autonoma de Mexico, Mexico; Centro de Ciencias de la Complejidad, Universidad Nacional Autonoma de Mexico, Mexico.
authors.forename: Tom
authors.initials: T
authors.lastname: Froese
authors.affiliation: Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Tokyo 153-8902, Japan.
authors.forename: Takashi
authors.initials: T
authors.lastname: Ikegami
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*
keywords: Altered states of consciousness; Global neural coordination; Hopfield network; Psychedelics; Self-optimization; Spiking neurons