title: A model biological neural network: the cephalopod vestibular system.
abstract: Artificial neural networks (ANNs) have become increasingly sophisticated and are widely used for the extraction of patterns or meaning from complicated or imprecise datasets. At the same time, our knowledge of the biological systems that inspired these ANNs has also progressed and a range of model systems are emerging where there is detailed information not only on the architecture and components of the system but also on their ontogeny, plasticity and the adaptive characteristics of their interconnections. We describe here a biological neural network contained in the cephalopod statocysts; the statocysts are analogous to the vertebrae vestibular system and provide the animal with sensory information on its orientation and movements in space. The statocyst network comprises only a small number of cells, made up of just three classes of neurons but, in combination with the large efferent innervation from the brain, forms an 'active' sense organs that uses feedback and feed-forward mechanisms to alter and dynamically modulate the activity within cells and how the various components are interconnected. The neurons are fully accessible to physiological investigation and the system provides an excellent model for describing the mechanisms underlying the operation of a sophisticated neural network.
authors.affiliation: Faculty of Science, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK. rwilliamson@plymouth.ac.uk
authors.forename: Roddy
authors.initials: R
authors.lastname: Williamson
authors.forename: Abdul
authors.initials: A
authors.lastname: Chrachri
mesh_terms: D000818:Animals; D049830:Cephalopoda / Q000502:physiology*; D018069:Hair Cells, Vestibular / Q000502:physiology; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*; D014722:Vestibule, Labyrinth / Q000166:cytology / Q000502:physiology*