Development of cue integration with reward-mediated learning

This thesis will first introduce in more detail the Bayesian theory and its use in integrating multiple information sources. I will briefly talk about models and their relation to the dynamics of an environment, and how to combine multiple alternative models. Following that I will discuss the experimental findings on multisensory integration in humans and animals. I start with psychophysical results on various forms of tasks and setups, that show that the brain uses and combines information from multiple cues. Specifically, the discussion will focus on the finding that humans integrate this information in a way that is close to the theoretical optimal performance. Special emphasis will be put on results about the developmental aspects of cue integration, highlighting experiments that could show that children do not perform similar to the Bayesian predictions. This section also includes a short summary of experiments on how subjects handle multiple alternative environmental dynamics. I will also talk about neurobiological findings of cells receiving input from multiple receptors both in dedicated brain areas but also primary sensory areas. I will proceed with an overview of existing theories and computational models of multisensory integration. This will be followed by a discussion on reinforcement learning (RL). First I will talk about the original theory including the two different main approaches model-free and model-based reinforcement learning. The important variables will be introduced as well as different algorithmic implementations. Secondly, a short review on the mapping of those theories onto brain and behaviour will be given. I mention the most in uential papers that showed correlations between the activity in certain brain regions with RL variables, most prominently between dopaminergic neurons and temporal difference errors. I will try to motivate, why I think that this theory can help to explain the development of near-optimal cue integration in humans. The next main chapter will introduce our model that learns to solve the task of audio-visual orienting. Many of the results in this section have been published in [Weisswange et al. 2009b,Weisswange et al. 2011]. The model agent starts without any knowledge of the environment and acts based on predictions of rewards, which will be adapted according to the reward signaling the quality of the performed action. I will show that after training this model performs similarly to the prediction of a Bayesian observer. The model can also deal with more complex environments in which it has to deal with multiple possible underlying generating models (perform causal inference). In these experiments I use di#erent formulations of Bayesian observers for comparison with our model, and find that it is most similar to the fully optimal observer doing model averaging. Additional experiments using various alterations to the environment show the ability of the model to react to changes in the input statistics without explicitly representing probability distributions. I will close the chapter with a discussion on the benefits and shortcomings of the model. The thesis continues whith a report on an application of the learning algorithm introduced before to two real world cue integration tasks on a robotic head. For these tasks our system outperforms a commonly used approximation to Bayesian inference, reliability weighted averaging. The approximation is handy because of its computational simplicity, because it relies on certain assumptions that are usually controlled for in a laboratory setting, but these are often not true for real world data. This chapter is based on the paper [Karaoguz et al. 2011]. Our second modeling approach tries to address the neuronal substrates of the learning process for cue integration. I again use a reward based training scheme, but this time implemented as a modulation of synaptic plasticity mechanisms in a recurrent network of binary threshold neurons. I start the chapter with an additional introduction section to discuss recurrent networks and especially the various forms of neuronal plasticity that I will use in the model. The performance on a task similar to that of chapter 3 will be presented together with an analysis of the in uence of different plasticity mechanisms on it. Again benefits and shortcomings and the general potential of the method will be discussed. I will close the thesis with a general conclusion and some ideas about possible future work

Modellierung primärer multisensorischer Mechanismen der räumlichen Wahrnehmung

Abstract The presented work concerns visual, aural, and multimodal aspects of spatial perception as well as their relevance to the design of artificial systems. The scientific approach chosen here, has an interdisciplinary character combining the perspectives of neurobiology, psychology, and computer science. As a result, new insights and interpretations of neurological findings are achieved and deficits of known models and applications are named and negotiated. In chapter one, the discussion starts with a review on established models of attention, which largely disregard early neural mechanisms. In the following investigations and experiments, the basic idea can be expressed as a conceptual differentiation between early spatial attention and higher cognitive functions. All neural mechanisms that are modelled within the scope of this work, can be regarded as primary and object-independent sensory processing. In chapter two and three the visual and binaural spatial representations of the brain and the specific concept of the computational topography in the central auditory system are discussed. Given the restriction of early neural processes, the aim of the actual multisensory integration, as it is described in chapter four, is not object classification or tracking but primary spatial attention. Without task- or object-related requirements all specifications of the model are derived from findings about certain multisensory structures of the midbrain. In chapter five emphasis is placed on a novel method of evaluation and parameter optimization based on biologically inspired specifications and real-world experiments. The importance of early perceptional processes to orienting behaviour and the consequences to technical applications are discussed.In der vorliegenden Arbeit werden visuelle, auditive und multimodale Formen der räumlichen Wahrnehmung und deren Relevanz für den Entwurf technischer Systeme erörtert. Der dabei vertretene wissenschaftliche Ansatz hat interdisziplinären Charakter und berücksichtigt im Umfeld der Neuroinformatik und Robotik methodische Aspekte der Neurobiologie, Wahrnehmungspsychologie und Informatik gleichermaßen. Im Ergebnis sind einerseits neue und weitergehende Interpretationen der Befunde über die natürliche Wahrnehmung möglich. Andererseits werden Defizite bestehender Simulationsmodelle und technischer Anwendungen benannt und überwunden. Den Ausgangspunkt der Untersuchungen bildet in Kapitel 1 die Diskussion und kritische Wertung etablierter Aufmerksamkeitsmodelle der Wahrnehmung, in denen frühe multisensorische Hirnfunktionen weitgehend unbeachtet bleiben. Als Grundgedanke der folgenden Untersuchungen wird die These formuliert, dass eine konzeptionelle Trennung zwischen primärer Aufmerksamkeit und höheren kognitiven Leistungen sowohl die Einordnung von sensorischen Merkmalen und neurologischen Mechanismen als auch die Modellierung und Simulation erleichtert. In den Kapiteln 2 und 3 werden zunächst die primären räumlichen Kodierungen der zentralen Hörbahn und des visuellen Systems vorgestellt und die Spezifika von projizierten und berechneten sensorischen Topographien beschrieben. Die anschließende Modellierung von auditorisch-visuellen Integrationsmechanismen in Kapitel 4 dient ausdrücklich nicht der Klassifikation oder dem Tracking von Objekten sondern einer frühen räumlichen Steuerung der Aufmerksamkeit, die im biologischen Vorbild unbewusst und auf subkortikalem Niveau stattfindet. Nach einer Erörterung der wenigen bekannten Modellkonzepte werden zwei eigene multisensorische Simulationssysteme auf Basis künstlicher neuronaler Netze und probabilistischer Methoden entwickelt. Kapitel 5 widmet sich der systematischen experimentellen Untersuchung und Optimierung der Modelle und zeigt, wie unbewusste Wahrnehmungsleistungen und deren Simulation unter Bezugnahme auf qualitative und quantitative Befunde über multisensorische Effekte im Mittelhirn evaluiert werden können. Die Diskussion des Modellverhaltens in realen audio-visuellen Szenarien soll unterstreichen, dass die frühe Steuerung der Aufmerksamkeit noch vor der Objekterkennung einen wichtigen Beitrag zur räumlichen Orientierung leistet