title: Schema-based learning of adaptable and flexible prey-catching in anurans I. The basic architecture.
abstract: A motor action often involves the coordination of several motor synergies and requires flexible adjustment of the ongoing execution based on feedback signals. To elucidate the neural mechanisms underlying the construction and selection of motor synergies, we study prey-capture in anurans. Experimental data demonstrate the intricate interaction between different motor synergies, including the interplay of their afferent feedback signals (Weerasuriya 1991; Anderson and Nishikawa 1996). Such data provide insights for the general issues concerning two-way information flow between sensory centers, motor circuits and periphery in motor coordination. We show how different afferent feedback signals about the status of the different components of the motor apparatus play a critical role in motor control as well as in learning. This paper, along with its companion paper, extend the model by Liaw et al. (1994) by integrating a number of different motor pattern generators, different types of afferent feedback, as well as the corresponding control structure within an adaptive framework we call Schema-Based Learning. We develop a model of the different MPGs involved in prey-catching as a vehicle to investigate the following questions: What are the characteristic features of the activity of a single muscle? How can these features be controlled by the premotor circuit? What are the strategies employed to generate and synchronize motor synergies? What is the role of afferent feedback in shaping the activity of a MPG? How can several MPGs share the same underlying circuitry and yet give rise to different motor patterns under different input conditions? In the companion paper we also extend the model by incorporating learning components that give rise to more flexible, adaptable and robust behaviors. To show these aspects we incorporate studies on experiments on lesions and the learning processes that allow the animal to recover its proper functioning.
authors.affiliation: USC Brain Project, University of Southern California, Los Angeles, 90089-0871, USA. corbacho@ii.uam.es
authors.forename: Fernando
authors.initials: F
authors.lastname: Corbacho
authors.forename: Kiisa C
authors.initials: KC
authors.lastname: Nishikawa
authors.forename: Ananda
authors.initials: A
authors.lastname: Weerasuriya
authors.forename: Jim-Shih
authors.initials: JS
authors.lastname: Liaw
authors.forename: Michael A
authors.initials: MA
authors.lastname: Arbib
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000220:Adaptation, Biological / Q000502:physiology*; D000344:Afferent Pathways / Q000166:cytology / Q000502:physiology; D000818:Animals; D001001:Anura / Q000033:anatomy & histology* / Q000502:physiology; D001522:Behavior, Animal; D003714:Denervation / Q000379:methods; D005246:Feedback / Q000502:physiology; D007002:Hypoglossal Nerve / Q000502:physiology; D061228:Hypoglossal Nerve Injuries; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009043:Motor Activity; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010465:Perception / Q000502:physiology*; D010812:Physical Stimulation / Q000379:methods; D011235:Predatory Behavior / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology