The module focuses on basic conceptualizations and processes of memory and learning. The students will acquire knowledge of theoretical accounts of memory structures and processes and their relevance and implications for primarily cognitive learning. Topics include types of memory (short-term, long-term, procedural, episodic, semantic), types of knowledge acquired (conceptual, declarative, procedural) and kinds of learning (association, generalization, implicit, explicit, transfer). In addition, principles of memory (assimilation, structuring, restructuring, encoding specificity, and levels of processing) and memory processes (encoding, activation, and retrieval) are discussed in relation to learning mechanisms and outcomes. The students will (a) apply this knowledge to comprehend, analyze, and evaluate the theoretical implications of recent psychological research in memory and learning; (b) synthesize and evaluate the potential of this knowledge in relation to computational problems and capabilities; (c) consider and propose methods of computationally testing theoretical claims and predictions.

This course presents basic frameworks of learning, offering the theoretical underpinning for the development of machine learning algorithms, with an emphasis on the development of naturalistic solutions for the acquisition of symbolically-represented cognitive knowledge. It  examines learning in the limit, the mistake-bounded model of online learning, active learning with queries, and the probably approximately correct model of batch learning. It then discusses learnability in the presence of missing or corrupted information. An effort is made to connect the formal properties of these models to real world situations, and examine the extent to which these properties capture or reflect some aspects of human learning. The relation of learning to the processes of perception and reasoning is also discussed, as well as the relation of learning to other natural processes, including the process of evolution.

The thematic unit COS514 will provide tools and methods for characterizing what nervous systems do, determine how they function, and understand why they operate in particular ways. In the introduction, the main biophysical aspects of neurons will be covered and the mechanism behind the creation of the action potential will be described. Also, the biophysics of excitatory and inhibitory synapses will be covered. In the next part of the course two simple mathematical models for neurons will be covered: the passive membrane model and the leaky integrate-and-fire model. The equivalent electrical circuits for these models are presented, the corresponding equations are derived, and their solutions are found analytically. Subsequently, the Hodgkin-Huxley neuron model is addressed, along with the equivalent electrical circuit and the full model equations for the membrane potential and the gating variables are derived.  Taking into consideration the spatial extension of neurons, we will study the topic of dendritic function and how dendrites can be modeled as cables. The cable equation for the passive dendrite is derived and corresponding stationary solutions are calculated analytically. Dendritic theory is completed by presenting the famous Rall cable theory and compartmental models. The topic of neuron plasticity will be addressed next, and the differences between structural and functional plasticity, in particular. The Hebb’s postulate will be formulated and the biophysical mechanism behind plasticity is explained. Spike Timing-Dependent Plasticity is described mathematically. Three rules and their corresponding models are presented: Hebb’s rule, covariance rule and Oja’s rule. Comparisons between these rules are made. Finally, the course  will deal with the subject of neural encoding, that is, how stimuli are reflected on the neural responses. Various techniques for neural recordings are presented and the neural code is explained through an example experiment. Concepts such as neural response function, firing rate, tuning curves, reverse correlation function, and spike-train statistics are addressed.

The COS513 Computational Intelligent Systems module provides a global overview of Computational Intelligent Systems, and their applications in understanding various aspects of cognition and the operation of the mind. The study of the brain from the computer scientists’ perspective will be emphasized by providing an in depth analysis of the methodology and basic concepts on how to model cognition and how to develop intelligent computer systems that try to mimic the way a human brain works. The theoretical framework of Computational Intelligence algorithms and techniques, such as Artificial Neural Networks, Genetic Algorithms and Fuzzy Logic, is covered together with hands on experience in the development and implementation of Computational Intelligent Systems. The main aim of the course is to provide students with the knowledge and skills required to design and implement effective and efficient Computational Intelligence solutions to problems for which a direct solution is impractical or unknown. Moreover, the course studies the area of cognitive systems and big-data analytics.

The thematic unit COS511 Introduction to Cognitive Psychology aims at providing a comprehensive overview of the basic areas of research in the main sub-fields of Cognitive Psychology. Specifically, through the course students acquire knowledge about the main theories and the most important empirical findings related to perception, attention, memory, mental imagery and knowledge representation, problem solving, and decision-making. Emphasis is placed on discussing the links between Cognitive Psychology and related fields such as Cognitive and Computational Neuroscience. The unit also provides an introduction to the methods and procedures that are commonly used to study the mechanisms of cognition.