Schema-based learning of adaptable and flexible prey- catching in anurans II. Learning after lesioning

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.

Are We Ready for a Natural History of Motor Learning?

“…In anurans (frogs and toads), movements are initiated from the midbrain not the forebrain (Abbie and Adey, 1950). It is notable that despite no significant cortical role in the planning or control of movement, anurans are capable of learning new preycatching behavior after hypoglossal nerve transection through concatenating pre-established synergies—mouth opening, neck extension, and body lunge (Corbacho et al, 2005). It could be conjectured that this process can be accomplished by BG connections with the brainstem.…”

Towards the Self-constructive Brain: emergence of adaptive behavior

“…Then, in section 6.2, we have also described how the predictive schema and its dual schemas can be learned to represent these relevant patterns of interaction. We claim that these internal models also allow the system to recover after a lesion (Adami, 2006;Bongard, Zykov & Lipson, 2006;Corbacho, 1997;Corbacho, Nishikawa, Weerasuriya Liaw & Arbib, 2005b).…”

“…We introduce the predictive (forward internal models) and its associated dual schemas as active processes capturing relevant patterns of interaction; and we suggest that the brain is composed of myriads of these patterns. These predictive internal models exist all over the brain and a variety of examples can be found in the literature regarding different brain areas/functionalities, for instance: visual (Berkes, Orban, Lengyel & Fiser, 2011;Corbacho, 1997;Rao & Ballard, 1999), sensorimotor (Corbacho & Arbib, 1995;Mehta & Schaal, 2002;Wolpert, Ghahramani & Jordan, 1995), and motor (Corbacho & Arbib, 1996;Corbacho, Nishikawa, Weerasuriya, Liaw & Arbib, 2005b;Desmurget & Grafton, 2000;Flanagan & Wing, 1997;Miall & Wolpert, 1996;Shadmer, Smith & Krakauer, 2010). Yet, these only represent the "tip of the iceberg', since they are all over all the different brain structures because they are the result of a central principle of organization (Butz, 2008;Clark, 2013;Corbacho, 1997;Corbacho & Arbib, 1997a,c;Downing, 2009;Grossberg, 2009;Haruno, Wolpert & Kawato, 2001;Hawkins, 2004;Hoffmann, Berner, Butz, Herbort, Kiesel, Kunde & Lenhard, 2007;Sigaud, Butz, Pezzulo & Herbort, 2013;.…”

“…corresponding to the parameter tuning. A specific spatio-temporal mapping learning algorithm to learn this mapping is presented in (Corbacho et al, 2005b) and a generalized information-theoretic measure is presented in (Sanchez-Montanes & Corbacho, 2004).…”