Short-term plasticity as cause-effect hypothesis testing in distal reward learning

Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related. Only the repetition of reward episodes helps distinguish true cause-effect relationships from coincidental occurrences. In the model proposed here, a novel plasticity rule employs short and long-term changes to evaluate hypotheses on cause-effect relationships. Transient weights represent hypotheses that are consolidated in long-term memory only when they consistently predict or cause future rewards. The main objective of the model is to preserve existing network topologies when learning with ambiguous information flows. Learning is also improved by biasing the exploration of the stimulus-response space towards actions that in the past occurred before rewards. The model indicates under which conditions beliefs can be consolidated in long-term memory, it suggests a solution to the plasticity-stability dilemma, and proposes an interpretation of the role of short-term plasticity.Comment: Biological Cybernetics, September 201

The Project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots: A Tool-box for Research on Intrinsic Motivations and Cumulative Learning

The goal of this paper is to furnish a tool-box for research on intrinsic motivations and cumulative learning based on the main ideas produced within the Integrated Project "IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots". IM-CLeVeR is a project funded by the European Commission under the 7th Framework Programme (FP7/2007-2013), \u27\u27Challenge 2 - Cognitive Systems, Interaction, Robotics\u27\u27, grant agreement No. ICTIP- 231722

Developmental learning of internal models for robotics

Abstract: Robots that operate in human environments can learn motor skills asocially, from selfexploration, or socially, from imitating their peers. A robot capable of doing both can be more ~daptiveand autonomous. Learning by imitation, however, requires the ability to understand the actions ofothers in terms ofyour own motor system: this information can come from a robot's own exploration. This thesis investigates the minimal requirements for a robotic system than learns from both self-exploration and imitation of others. .Through self.exploration and computer vision techniques, a robot can develop forward 'models: internal mo'dels of its own motor system that enable it to predict the consequences of its actions. Multiple forward models are learnt that give the robot a distributed, causal representation of its motor system. It is demon~trated how a controlled increase in the complexity of these forward models speeds up the robot's learning. The robot can determine the uncertainty of its forward models, enabling it to explore so as to improve the accuracy of its???????predictions. Paying attention fO the forward models according to how their uncertainty is changing leads to a development in the robot's exploration: its interventions focus on increasingly difficult situations, adapting to the complexity of its motor system. A robot can invert forward models, creating inverse models, in order to estimate the actions that will achieve a desired goal. Switching to socialleaming. the robot uses these inverse model~ to imitate both a demonstrator's gestures and the underlying goals of their movement.Imperial Users onl

Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots

This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them

Motivated cognition: Neural and computational mechanisms of curiosity, attention and intrinsic motivation

International audienceBased on a synthesis of findings from psychology, neuroscience, and machine learning, we propose a unified theory of curiosity as a form of motivated cognition. Curiosity, we propose, is comprised of a family of mechanisms that range in complexity from simple heuristics based on novelty, salience, or surprise, to drives based on reward and uncertainty reduction and finally, to self-directed metacognitive processes. These mechanisms, we propose, have evolved to allow agents to discover useful regularities in the world ! steering them toward niches of maximal learning progress and away from both random and highly familiar tasks. We emphasize that curiosity arises organically in conjunction with cogni- tion and motivation, being generated by cognitive processes and in turn, motivating them. We hope that this view will spur the systematic study of curiosity as an integral aspect of cognition and decision making during development and adulthood