Senior Lecturer, School of Computer Science,
   University of Lincoln, Lincoln, U.K.

Faculty, Athens International Masters Program in Neuroscience,
   Dept of Biology, University of Athens, Greece

Faculty, Budapest Semester in Cognitive Science,
   Eotvos Lorand University, Budapest, Hungary

Guest Lecturer, Institute of Automation,
   Chinese Academy of Sciences, Beijing, China

Associate Editor, Cognitive Computation


Guest Associate Editor, Frontiers in Systems Neuroscience


Associate Editor, Scholarpedia


Review Editor, Frontiers in Cognitive Science
email: vcutsuridisgmail.com
Books

Hippocampal Microcircuits:
A Computational Modeler's
Resource Book, 1st ed



Springer 2010
Perception-Action Cycle:
Models, Architectures
and Hardware



Springer 2011
Hippocampal Microcircuits:
A Computational Modeler's
Resource Book, 2nd ed



Springer, in 2017
Edited Proceedings

Brain Inspired
Cognitive Systems
(BICS) 2008



Springer 2008
Book Series

Springer Series in Cognitive
& Neural Systems



Springer
Trends in Augmentation
of Human Performance



Springer


Home Page
Research
Teaching
   • Eotvos Lorand University
   • Chinese Academy of Sciences
   • University of Patras
   • University of Crete
   • University of Athens
   • University of Stirling
   • Teaching competencies & interests
Publications
Activities, Grants
Talks, Seminars
Industry
Education, Career
Software
Full CV   (July 20, 2016)


Eotvos Lorand University, Budapest, Hungary
Budapest Semester in Cognitive Science
Core Faculty & Guest Lecturer, 2010-now
  • Cognitive Informatics

    An undergraduate course taught at Eotvos University (Hungary) every year. The course is part of the curriculum of the “Budapest Semester in Cognitive Sciences” at Eotvos University at which I am a core faculty member. Topics include: introduction to cognitive modelling, types of cognitive modelling, usefulness of cognitive modelling, levels of cognitive modelling, successes and pitfalls of cognitive modelling; Symbolic modelling (ACT-R, Soar, CLARION, etc.); Connectionist modelling (what is an artificial neuron and how it transmits information, activation functions, connection weights, output computation, McCulloch-Pitts neuronal type, learning rules, network behaviour, worked examples); Computational models of concept formation, concept learning and knowledge representation (Semantic nets, PDP approach to semantic cognition, Adaptive Resonance Theory, Linear Associative Memory)

Chinese Academy of Sciences, Beijing, China
Institute of Automation
Guest Lecturer, Jun 15 – Jul 5, 2014
  • Cognitive Science for Brain-Mind Research

    An undergraduate course taught in the Institute of Automation of the Chinese Academy of Sciences (China). It is a survey of the psychological subjects, with an emphasis on cognitive science. Principles of learning and memory, including habituation, sensitization, classical conditioning, operant conditioning, episodic and semantic memory, skills and habits, working memory, cognitive control, executive function, emotions, observational learning, development and aging.

University of Patras, Greece
Medical School
Guest Lecturer, 2008-2009
  • Neuroinformatics

    A graduate level course in Neuroinformatics including computational neuroscience tailored to medical students with very little math. The course was taught in the Medical School at the University of Patras (Greece). Topics included tools and databases for management and sharing of neuroscience data at all levels of analysis (e.g. BrainML, NeuroML, etc) and computational models of the nervous system and neural processes including notions of electrical and biochemical properties of single neurons, the electrical and chemical communication between neurons, the anatomy, physiology and function of each of the major brain structures and systems and how behavior emerges from their actions. Hodgkin-Huxley models. Integrate and fire models, various types of neural networks, central pattern generators. Synaptic plasticity. MATLAB, Neuron, etc.

University of Crete, Greece
Brain and Mind Graduate Program
Adjunct Faculty, Summer 2015
  • Computational Neuroscience

    A graduate level course taught in the Brain and Mind Graduate Program at the University of Crete. The course is tailored to students from various disciplines including math, physics, psychology, philosophy, neuroscience, etc. Topics will include introduction to math (derivatives, integrals, linear algebra), elements on neurophysiology, computational models of single neurons, networks, calcium dynamics, synaptic plasticity as well as computer vision, bio-inspired navigation and robotics.

University of Athens, Greece
Information Technologies in Medicine and Biology, Dept of Informatics & Telecommunications
Faculty, 2008–2009
  • Computational Neuroscience

    A graduate level course in computational neuroscience tailored to engineering and informatics students with very heavy math. The course is part of the “Information technologies in medicine and biology” master’s programme in the University of Athens (Greece) at which I am a faculty member. Topics included notions of electrical and biochemical properties of single neurons, the electrical and chemical communication between neurons, the anatomy, physiology and function of each of the major brain structures and systems and how behavior emerges from their actions. Emphasis is given on mathematical descriptions and computational techniques used to study and understand neurons and network of neurons such as: Hodgkin-Huxley models, cable theory, integrate-and-fire neurons, multicompartmental modeling, riring rate models, various types of neural networks (feedforward, associative, linear recurrent, stochastic, etc.), central pattern generators, topographic maps, receptive fields, elements of information theory (entropy and mutual information, etc.) spike-train statistics, reverse-correlation methods, rate vs temporal processing, population vector coding, adaptation and learning (Hebbian learning, LTP/LTD, STDP, supervised, unsupervised learning), classical conditioning, reinforcement learning (Markov decision processes, actor-critic model, etc.). MATLAB, Neuron, etc.

Neuropsychology and Neuroscience Graduate Program, Medical School
Faculty, March, 2016
  • Computational Neuroscience I & II

    A two-week intensive graduate level course tailored to students from various disciplines including math, physics, psychology, philosophy, neuroscience, etc. Topics include introduction to elements on neurophysiology, computational models of single neurons, networks, calcium dynamics, synaptic plasticity.

University of Stirling, U.K.
Dept of Computing Science & Mathematics
Lecturer, 2007–2008
  • Object-oriented software design (Fall 2007, Spring 2008)

    A graduate level course taught at the University of Stirling (U.K) in 2007-2008. Topics included software development process. Object concepts. Unified Modelling Language (UML): class diagrams, use case diagrams, interaction diagrams, state diagrams. Use of a CASE tool. Analysis and design models. Case studies in use-case modelling, object-oriented analysis and object-oriented design. Components and re-use in software engineering.

  • Computing and the brain (Spring 2008)

    An undergraguate course taught at the University of Stirling (U.K.) in 2008. Topics included notions of electrical and biochemical properties of single neurons, the electrical and chemical communication between neurons, the anatomy, physiology and function of each of the major brain structures and systems, Hodgkin Huxley models, integrate and fire models, synaptic plasticity models, artificial neural networks: perceptron, backpropagation, Hopfield, etc. Synaptic plasticity models (Hebbian, STDP, etc). MATLAB, Neuron, etc.

Teaching Competencies & Interests
Open-ended summary list of domains I have taught or can teach:

Computer science (see also Industry)

  • Core topics: theory and practice of programming languages (object-oriented, procedural, declarative; Java, C/C++, etc.), GUIs, etc.

  • Software engineering: object-oriented methodology, design patterns, software architecture

Research & seminar topics (see also Research Topics)

  • Computational cognitive science: computational neuroscience, artificial & spiking neural networks, neurobiological modeling, pattern recognition, machine learning, computer vision

Undergraduate mathematics & physics