Abstract
The previous companion paper describes the initial (seed) schema architecture that gives rise to the observed prey-catching behavior. In this second paper in the series we describe the fundamental adaptive processes required during learning after lesioning. Following bilateral transections of the hypoglossal nerve, anurans lunge toward mealworms with no accompanying tongue or jaw movement. Nevertheless anurans with permanent hypoglossal transections eventually learn to catch their prey by first learning to open their mouth again and then lunging their body further and increasing their head angle.
In this paper we present a new learning framework, called schema-based learning (SBL). SBL emphasizes the importance of the current existent structure (schemas), that defines a functioning system, for the incremental and autonomous construction of ever more complex structure to achieve ever more complex levels of functioning. We may rephrase this statement into the language of Schema Theory (Arbib 1992, for a comprehensive review) as the learning of new schemas based on the stock of current schemas. SBL emphasizes a fundamental principle of organization called coherence maximization, that deals with the maximization of congruence between the results of an interaction (external or internal) and the expectations generated for that interaction. A central hypothesis consists of the existence of a hierarchy of predictive internal models (predictive schemas) all over the control center-brain-of the agent. Hence, we will include predictive models in the perceptual, sensorimotor, and motor components of the autonomous agent architecture. We will then show that predictive models are fundamental for structural learning. In particular we will show how a system can learn a new structural component (augment the overall network topology) after being lesioned in order to recover (or even improve) its original functionality. Learning after lesioning is a special case of structural learning but clearly shows that solutions cannot be known/hardwired a priori since it cannot be known, in advance, which substructure is going to break down.
Similar content being viewed by others
References
Almasbakk C, Whiting HTA, Helgerud J (2001) The efficient learner. Biol Cybern 84(2):75–83
Anderson CW, Nishikawa KC (1993) A prey type dependent hypoglossal feedback system. in the frog Rana Pipiens. Brain Behav Evol 42:189–196
Anderson CW, Nishikawa KC (1996) The roles of visual and proprioceptive information during motor program choice in frogs. J Comp Physiol A 179:753–762
Arbib MA (1972) The metaphorical brain. Wiley-Interscience, New York
Arbib MA (1987) Levels of modeling of mechanisms of visually guided behavior. Behav Brain Sci 10:407–465
Arbib MA (1992) Schema theory. In: Shapiro S (eds) The encyclopedia of artificial intelligence, 2nd edn. Wiley Interscience, New York, pp. 1427–1443
Arbib MA, Lieblich I (1977) Motivational learning of spatial behavior. In: Metzler J (eds) Systems neuroscience. Academic Press, New York, pp. 119–165
Barela JA, Jeka JJ, Clark JE (1999) The use of somatosensory information during the acquisition of independent upright stance. Infant Behav Dev 22(1):87–102
Bell CC, Han V, Sugawara Y, Grant K (1997) Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature 287:278–281
Bell CC, Han V, Sugawara Y, Grant K (1999) Plasticity in the mormyrid electrosensory lobe. J Exp Biol 202:1339–1347
Corbacho F (1997) Schema-based learning: towards a theory of organization for adaptive autonomous agents. PhD Thesis, University of Southern California
Corbacho F (1998) Schema-based learning. Artif Intell V 101:370–373
Corbacho F, Arbib MA (1995) Learning to Detour. J Adapt Behav 3(4):419–468
Corbacho F, Arbib MA (1997) Schema-based learning: biologically inspired principles of dynamic organization. In: Proceedings of IWANN-97. Lecture notes in computer science 1240. Springer, Berlin Heidelberg New York
Corbacho F, Nishikawa KC, Liaw J-S, Arbib MA (1996a) An expectation-based model of adaptable and flexible prey-catching in anurans. Society for neuroscience.abs. 644.2
Corbacho F, Nishikawa KC, Liaw J-S, Arbib MA (1996b) Adaptable and flexible prey-catching in anurans. In: Proceedings of the workshop on sensorimotor coordination: amphibians, models, and comparative studies. Sedona, Arizona
Craik K (1943) The nature of explanation. Cambridge University Press, Cambridge
Ewert J-P (1997) Neural correlates of key stimulus and releasing mechanisms: a case study and two concepts. Trends Neurosci 20(8):332–339
Ewert J-P, Buxbaum-Conradi H, Glagow M, Röttgen A, Schürg-Pfeiffer E, Schwippert WW (1999) Forebrain and midbrain structures involved in prey-catching behaviour of toads: stimulus-response mediating circuits and their modulating loops. Eur J Morphol 37:111–115
Ewert J-P, Buxbaum-Conradi H, Dreisvogt F, Glagow M, Merkel-Harf C Rottgen A, Schurg-Pfeifer E, Schwippert WW (2001) Neural modulation of visuomotor functions underlying prey-catching behaviour in anurans: perception, attention, motor performance, learning. Comp Biochem Physiol Part A 128:417–461
Garcia C.E, Prett DM, Morari M (1989) Model predictive control: theory and practice- a survey. Automatica 25:335–348
Gleason T, Nishikawa KC (1996) The effect of practice regime on motor learning following hypoglossal transection in toads (Bufo marinus). American Zoologist 36:116A
Grobstein P (1992) Directed movement in the frog; motor choice, spatial representation, free will?. In: Kien J, McCrohan CR, Winlow W (eds) Neurobiology of motor programme selection. Pergamon Press, Oxford
Guazzelli A, Corbacho F, Bota M, Arbib MA (1998) An implementation of the Taxon-Affordance System for Spatial Navigation. J Adap Behav 6(4):435–471
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd Edn. Prentice-Hall, Englewood Cliffs
Innocenti CM, Nishikawa KC (1994) Motor learning in toads (bufo marinus) following bilateral hypoglossal nerve transection. Am Zool 34:56A
Jordan MI, Rumelhart D (1992) Forward models: Supervised Learning with a Distal Teacher. Cogn Sci 16:307–354
Kalman RE (1960) A new approach to linear filtering and prediction theory. Trans ASME J Basic Eng 82:35–45
Kermer SC (2001) Spatio-Temporal Connectionist Networks: A Taxonomy and Review. Neural Computation 13(2):249–306
Kositsky M (2000) Motor learning and skill acquisition by sequences of elementary actions. Department of Computer Science University of Massachusetts, Amherts, PhD Thesis
Kwok TY, Yeung DY (1997) Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans Neural Netw 8(3):630
Lyons D, Arbib MA (1989) A formal model of distributed computation for Scjhema-based robot control. IEEE J Rob Autom 5:280–293
Merfeld D, Zupan L, Peterka RJ (1999) Humans use Internal Models to Estimate gravity and linear acceleration. Nature 398:615
Metcalfe JS, Chen LC, Kopp MA, Jeka JJ, Clark JE (2001) Beyond postural sway reduction: Do newly walking infants couple to a driving somatosensory stimulus? The first world congress: motor development and learning in infancy, Amsterdam
Metcalfe JS, Clark JE (2000) Coordinating perception and action: the role of sensory information in the exploration of posture. Biannual conference of the International Society for Infant Studies, Brighton
Mussa-Ivaldi FA, Bizzi E (2000) Motor learning through the combination of primitives. Philos Trans R Soc Lond B Biol Sci 355(1404):1755–69
Mussa-Ivaldi F (1999) A modular features of motor control and learning. Curr Opin Neurobiol 9:713–717
Rao RPN, Ballard DH (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 2(1):79–87
Suri RE, Schultz W (2001) Temporal difference model reproduces anticipatory neural activity. Neural Comput 13(4):841–862
Sutton RS, Barto AG (1981) An adaptive network that constructs and uses an internal model of its world. Cogn Brain Theory 4(3):217–246
van Sonderen JF, Gielen CCAM, Denier van der Gon JJ (1989) Motor program for goal-directed movements are continuously adjusted according to changes in target location. Exp Brain Res 78: 139–146
Weerasuriya A (1989) In search of the pattern generator for snapping in toads. In:Ewert J-P, Arbib MA, (eds) Visuomotor coordination, amphibians, comparisons, models, and robots. Plenum Press, NYNew York, pp 589–614
Weerasuriya A (1991) Motor pattern generators in anuran prey capture. In: Arbib MA, Ewert J-P (eds) Visual structure and integrated functions, research notes in neural computing. Springer, Berlin Heidelberg New York, pp 255–270
Weerasuriya A, Licata N (1996) Prey capture behavior in newly metamorphosed froglets (Rana Pipiens). Workshop on sensorimotor coordination, Sedona, AR
Weerasuriya A, Mills J (1996) Long term consequences of hypoglossal nerve lesions on anuran prey capture. Workshop on sensorimotor coordination. Sedona, AR
Wolpert DM, Gharhamani Z, Jordan MI (1995) An internal model for sensorimotor integration. Science 269:1880–1882
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Corbacho, F., Nishikawa, K.C., Weerasuriya, A. et al. Schema-based learning of adaptable and flexible prey- catching in anurans II. Learning after lesioning. Biol Cybern 93, 410–425 (2005). https://doi.org/10.1007/s00422-005-0014-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-005-0014-z