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Identifier 000373405
Title Computational modeling of observational learning inspired by the cortical underpinnings of human and monkey primates
Alternative Title Υπολογιστική μοντελοποίηση της μάθησης μέσω παρατήρησης εμπνευσμένη από τις βιολογικές διεργασίες σε πιθήκους και ανθρώπους
Author Χουρδάκης, Εμμανουήλ Μηνάς
Thesis advisor Τραχανιάς, Πάνος
Abstract In the current thesis we have studied the cognitive process of observational learning from a computational modeling perspective. In this context we have employed data from neuroscientific experiments, including higher-level imaging and single-cell recordings, in order to develop two computational models of observational learning, inspired by the neurophysiology of human and Macaque primates. To accomplish this we have devised a framework for designing computational models based on neuroscientific findings, and used it in order to develop two novel implementations of the cortical process in simulated agents. To facilitate learning during observation, both models are based on the intuition that, during action execution and observation, the activated cortical networks in the two primates overlap extensively. As a result, both agents treat perception as an active, cross-modal, simulation of others’ actions and learn new motor skills without the active involvement of their body. The first model maintains adequate consistency with the relevant brain areas and connectivity in Macaques, and effectively provides insights about the cortical underpinnings of observational learning, which can be summarized in three categories: (i) neuronal, i.e. how learning can be implemented at the cellular level during observation, (ii) regional, by identifying the potential role of a certain region in associating the motor representation with the visual image of the observer, (iii) system, how the emergent pattern of activations observed during action observation and action execution is formed, and what are the reasons for the lower activations during observation. In addition, due to the use of the aforementioned modeling methodology, the agent is able to exhibit three important behavioral functions: (i) observational learning in a similar manner as its biological counterparts, (ii) knowledge generalization to different domains and knowledge integration on top of existing representations and (iii) embodiment correspondence based on the overlapping pathway of activations. The second model employs a phenomenological approach to design a motor control system that is loosely based on the function of the regions that become active in humans during execution and observation. For this reason we have developed novel implementations for each of the subsidiary motor control processes, and integrated them in order to produce an agent able to learn only by observation. The main contributions include: (i) a model that replicates the reward prediction properties of the dopaminergic neurons in the Basal Ganglia, used to implement a variant of reinforcement learning, (ii) a way to segregate the multidimensional control of the embodiment of the agent to basis functions using a novel primitive model, (iii) a method to implement embodiment correspondence using associative Page iv networks, which enables an agent to develop and match symbolic representations of its own body and the demonstrator’s, (iv) how higher-order motor control can be designed as an epiphenomenon of the motor control system, i.e. as a subsidiary process built on top of basis motor functions and (v) how learning can be implemented during observation using simple motor rules that can be derived only by observation
Language English
Subject Computational Modeling
Neural Networks
Overlapping Pathways
Επικαλυπτόμενα μονοπάτια
Νευρωνικά δίκτυα
Υπολογιστική μοντελοποίηση
Issue date 2012
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
Permanent Link https://elocus.lib.uoc.gr//dlib/4/1/c/metadata-dlib-1333520068-597182-7403.tkl Bookmark and Share
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