The fields of biologically inspired artificial intelligence, neuroscience, and psychology have had exciting influences on each other over the past decades. Especially recently, with the increased popularity and success of artificial neural networks (ANNs), ANNs have enjoyed frequent use as models for brain function. However, there are still many disparities between the implementation, algorithms, and learning environment used for deep learning and those employed by the brain, which is reflected in their differing abilities. I first briefly introduce ANNs and survey the differences and similarities between them and the brain. I then make a case for designing the learning environment of ANNs to be more similar to that in which brains learn, namely by allowing them to actively interact with the world and decreasing the amount of external supervision. To implement this sensorimotor learning in an artificial agent, I use deep reinforcement learning, which I will also briefly introduce and compare to learning in the brain. In the research presented in this dissertation, I focus on testing the hypothesis that the learning environment matters and that learning in an embodied way leads to acquiring different representations of the world. We first tested this on human subjects, comparing spatial knowledge acquisition in virtual reality to learning from an interactive map. The corresponding two publications are complemented by a methods paper describing eye tracking in virtual reality as a helpful tool in this type of research. After demonstrating that subjects do indeed learn different spatial knowledge in the two conditions, we test whether this transfers to artificial agents. Two further publications show that an ANN learning through interaction learns significantly different representations of the sensory input than ANNs that learn without interaction. We also demonstrate that through end-to-end sensorimotor learning, an ANN can learn visually-guided motor control and navigation behavior in a complex 3D maze environment without any external supervision using curiosity as an intrinsic reward signal. The learned representations are sparse, encode meaningful, action-oriented information about the environment, and can perform few-shot object recognition despite not knowing any labeled data beforehand. Overall, I make a case for increasing the realism of the computational tasks ANNs need to solve (largely self-supervised, sensorimotor learning) to improve some of their shortcomings and make them better models of the brain.

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The rhetorical core of adaptation studies is a comparison between two texts, and the type of comparison that has sparked the most reactions, whether in its use or in speaking out against it, is fidelity criticism. As David Johnson and Simone Murray point out, fidelity criticism has long been rejected as an unscholarly mode of interpretative analysis because it is caught up in subjective value judgments and imprecise conjectures of a text’s “essence.” I contend, however, that the understanding of essences is critical to understanding both fidelity and the adaptation experience because something like essence is fixed in the human consciousness. Recent research in neuro-studies suggests that the mind creates “essences” by recognizing networks of structural elements in objects (namely texts for the purposes of this paper). The essence then becomes an experienced-based abstraction that can be recalled whenever useful. The individual is able to use the abstraction the mind creates to interpret the world, including the object itself, other objects, and the relationship all those objects have with him/herself, the individual. That relationship, in turn, influences and changes both the object and the individual interpreting the object. Thus the concept of a text’s essence, though often disregarded, becomes a useful interpretative tool when understood through a combination of overlapping theoretical traditions. Combining a reception-based structural and Heideggerian utilitarianism with recent neuroscientific findings grants productive insights clarifying our understandings and definitions of essence, especially in regard to adaptations in particular.

The purpose of the dissertation is to investigate the degree of compatibility of two fields: phenomenology and computational cognitive science. The former field proposes to explicate all structures of conscious experience in terms of conscious experience. The latter proposes to explicate all structures of consciousness partly in terms of unconscious causal factors. These endeavors have been seen as mutually exclusive. I put forward the thesis that the original formulation of phenomenology may be seen to have a computational theory of mind in the background. To this end, I show in the first chapter that the founder of phenomenology articulated, prior to founding phenomenology, a computational theory of mind in terms of its two modern theses: (1) syntactic representations, and (2) their causal generation and interaction. Insofar as I am able to provide sufficient evidence for this thesis, I am theoretically licensed to proceed to trace its influence on the founding of phenomenology proper. On the above textual basis, I proceed in the second chapter to discuss Husserl's methodology in the founding work of phenomenology - the Logical Investigations. I there show how my compatibility thesis may be true; indeed, I demonstrate that formal evidence is the causal product of what Husserl calls “unsere Denkmaschine” – a thought-machine that manipulates syntactic symbols. The third chapter discusses several arguments against (Humean) associationism, and by extension against (Churchlandian) connectionism, and show that they demand in their stead computationalism, both on account of the nature of the explananda as well as for the sake of theoretical completeness. In the fourth chapter, I discuss, with a view to deepening my interpretation, the much-celebrated property (since Chomsky) of productivity. This leads to a discussion of the methodological relation between “universal grammar,” as it appears directly in the 4th Logical Investigation, and the computational theory of mind. In the fifth chapter, I discuss how Husserl’s descriptive treatment of the propositional attitudes (as act-matters & act-qualities), nominalization, and categorial intuition may be supplemented on the explanatory side by a language of thought.

This qualitative thesis work investigates in which way an immersive,embodied and interactive computer-based simulation can lead to an easierunderstanding of programming concepts. It also presents a concept for VRprogramming learning tool that could turn learning into a fun and engagingexperience for the students. The target group is Interaction Design bachelorstudents – programming is an important tool to create innovative andinteractive artefacts and interfaces, and yet, the students have hard time tounderstand many of the programming concepts. The current research relieson concepts such as embodiment and tangibility, which reflects on theprototype developed – a highly immersive, embodied VR platform with astrong illusion of tangibility. Additionally, the prototype was inspired byfeedback from students and teachers, result of a participatory approach.Finally, the research showed through usability tests that the studentsexperienced that the application made the concepts more graspable and morefun to learn.

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This thesis presents a design to route the spikes in a cognitive computing project called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE). SyNAPSE is a DARPA-funded program to develop electronic neuromorphic ma- chine technology that scales to biological levels. The basic computational block in the SyNAPSE system is the asynchronous spike processor (ASP) chip. This analog core contains the neurons and synapses in a neural fabric and performs the neural and synaptic computations.An ASP takes asynchronous pulses (spikes) as inputs and after some small delay produces asyn- chronous pulses as outputs.The ASP chips are organized in a nxn (where n [approximately equal to] 10) 2-dimensional grid with a dedicated node for each chip. This interconnected network is called Digital Fabric(DF) and the node is called Digital Fabric Node (DFN). The DF is a packet network that routes pulse (AER - Address event rep- resentation) packets between ASP's. This thesis also presents a technique for design implementation on a FPGA, perfor- mance testing of the network and validation of the network using various tools.

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