PHENOMENOLOGICAL PHILOSOPHY AND THE CONTEMPORARY PROBLEM OF THE INTENTIONALITY OF THE MIND
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Course Master:
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F. Vassileiou
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Duration:
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1 semester
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ECTS (credit hours):
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6 (30)
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Course outline:
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1. Basic elements of Philosophy of Science
The problem of induction and the first principles: Application in the case of Cognitive Science.
2. Descriptive Psychology and Franz Brentano’s peculiar empiricism
Inner perception, stratification of mental life, presentation, judgment.
3. Brentano’s legacy
Psychical phenomena, physical phenomena, and intentionality as criterion for the delineation of the region of psychical acts.
4. Edmund Husserl’s criticism against Brentano’s theory (Ι)
First form of psychologism and its overcoming: eidetic method and Husserl’s critique of naturalism.
5. Edmund Husserl’s criticism against Brentano’s theory (ΙΙ)
Second form of psychologism and its overcoming: synthetic categorial intuition. Simple perception, judgment, scientific idealization.
6. The issue of perceptual intentionality
Gestalt Psychology and Husserlian Mereology.
7. Different levels of the phenomenological constitution of perceptual objects
Sensory fields, primordial extentionality, and primordial materiality-causality.
8. Reception of the problem of intentionality in the analytic tradition
The way Roderick Chisholm construed Brentano’s theory. The formation of contemporary representational theory of mind.
9. The current discussion about the distinction between conceptual and non-conceptual perceptual content in analytic philosophy
The “myth of the given,” John McDowell’s intellectualism, and recent tendencies of acceptance of non-conceptual content.
10. The attempt at a dialogue between phenomenology and analytic philosophy regarding the distinction between conceptual and non-conceptual content
Tendencies for convergence and criticism.
11. Overall reconstructive discussion |
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Instructors:
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F.Vassiliou
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Prerequisites:
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Course Master:
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Panagiotis Simos, Ph.D
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Duration:
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1 semester
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ECTS (credit hours):
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9 (45)
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Course outline:
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The course covers the basic principles of psychometric theory involved in the application of standardized tests for the assessment of language and cognitive functions. Specific applications of these tests in the evaluation of patients with neurological disorders are discussed.
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Instructors:
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Panagiotis Simos, Ph.D.
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Prerequisites:
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Clinical Neuroanatomy, Brain and cognitive functions
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MOLECULAR CELLULAR NEUROENDOCRINOLOGY
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Course Master:
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R. Dermitzaki
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Duration:
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1 semester
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ECTS (credit hours):
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6 (30)
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Course outline:
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Neuroendocrinology of Stress Physiology of the stress axis CRH receptors and their antagonists
Opioid receptors and their antagonists
ACTH receptors
Animal models in the study of stress KO animals in the study of stress
Glucocorticoid receptors
Neuroendocrinology of the Immune Response HPA axis-immune system interaction
Neuroendocrine control of inflammation
Neuroendocrinology of Sleep Neuroendocrinology of the Energy Balance
General introduction:
Appetite regulating neuropeptides
Neuroendocrinology of the Reproduction Neuroendocrine regulation of GnRH
Neuroendocrine regulation of gonadal function
Neuroendocrine regulation of the fetal-maternal unit Neuroendocrinology of the Central Nervous System Neuropeptides:
Neurosteroids
Neurotrophic factors
Membrane receptors of neuroactive steroids
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Instructors:
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R. Dermitzaki, M. Venyhaki
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SYNAPTIC INTERACTIONS IN THE CORTEX
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Course Master:
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Yannis Dalezios
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Duration:
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One semester
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ECTS (credit hours):
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6 (30)
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Course outline:
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1. Intoduction. Methods.
2. Neuronal types.
3. GABA receptors and their localization.
4. Ionotropic glutamate receptors and their localization.
5. Metabotropic Glutamate Receptors: from structure to function. I. (F. Ferraguti)
6. Metabotropic Glutamate Receptors: from structure to function. II. (F. Ferraguti)
7. Synaptic organisation of the cerebral cortex: Neuronal types, Connections, Receptors and their Physiological Roles. I (P. Somogyi)
8. Synaptic organisation of the cerebral cortex: Neuronal types, Connections, Receptors and their Physiological Roles. II (P. Somogyi)
9. The functional organisation of the basal ganglia I. (J.P. Bolam)
10. The functional organisation of the basal ganglia II. (J.P. Bolam)
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Instructors:
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Yannis Dalezios, Peter Somogyi, J. Paul Bolam, Francesco Ferraguti.
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DESIRES, VALUES, MOTIVES: A NEUROPHENOMENOLOGICAL POINT OF VIEW
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Course Master:
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Panos Theodorou
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Duration:
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1 semester
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ECTS (credit hours):
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6 (30)
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Course outline:
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1. The problem: How does an intelligent comportment towards beings in a world come about?
2. Fundamental genealogy of the problem: recourse to Aristotle's De Anima (appetite, goods, movement).
3. The theory of motives/motivation in recent philosophical thought: Hume and Kant.
4. Modern Anglo-American treatment of motives/motivation.
5. Traditional Phenomenology and Neurophenomenology: from Husserl, Heidegger and Merleau-Ponty to Varela, Maturana and Thompson.
6. Un unexpected source of inspiration for the further development of Neurophenomenology: appetite, values and motives/motivation in Max Scheler's Formalism in Ethics and Non-Formal Ethics of Values (1913, 1916) and The Human's Place in the Cosmos (1928).
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Instructors:
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Panos Theodorou
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Prerequisites:
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Course Master:
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A. Moschovakis
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Duration:
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One semester (Two hours per week)
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ECTS (credit hours):
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9 (45)
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Course outline:
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1. Biomechanics of moving limbs
2. Motoneurons
3. Premotoneurons: The neural integrator
4. Feedback control of movement parameters: The burst generator
5. The Superior Colliculus: Metrics and sensorimotor interface.
6. Vestibular control of eye movements
7. Cerebellar control of eye movements
8. Basal ganglionic control of eye movements
9. Cortical control of eye movements
10. Models of the oculomotor system
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Instructors:
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A. Moschovakis
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Prerequisites:
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Systems and signals
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Course Master:
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A. Argyros
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Duration:
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1 semester
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ECTS (credit hours):
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4 (60)
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Course Outline:
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The goal of this course is the detailed study of computer vision methods that aim at a realistic interpretation of a scene based on the analysis of one or more images or videos. The course covers part of the material under the specialization area “Computational Vision and Robotics”. More specifically, the course focuses on the study of methods for image content description and representation, estimation of parametric models, estimation of 3D structure and motion, as well as topics on the detection, tracking and recognition of objects and activities. Emphasis is given on the study and the analysis of 3D information regarding the structure and the dynamic aspects of a scene. After attending the course, the student should be acquainted with relevant techniques, should be in a position to implement them as well as use them as components of broader computer vision systems, and able to study the computer vision literature.
List of topics 1. Introduction to computer vision 2. Overview of topics on image acquisition and processing (sampling quantization, color perception, smoothing and differentiation filters) 3. Overview of topics on image analysis (edge detection, image segmentation) 4. Texture representation, analysis and synthesis 5. Interest point detection (Harris corner detector) 6. Blob detection 7. The scale Invariant Feature Transform – SIFT 8. The Hough transform 9. Model fitting (least squares) 10. Robust model fitting (LMedS, RANSAC) 11. Image alignment 12. Camera models, lenses, projective geometry 13. Camera calibration 14. Epipolar geometry 15. Stereo vision: point correspondence and 3D reconstruction 16. Volumetric 3D reconstruction from multiple views 17. 12D motion (normal flow, optical flow) 18. 3D motion modeling (motion field, egomotion) 19. Tracking linear dynamical systems 20. Tracking with particle filtering 21. Object detection 22. Object recognition 23. Object category recognition 24. Activity recognition
Suggested Books Rick Szeliski, “Computer Vision: Algorithms and Applications” David Forsyth, Jean Ponce, “Computer Vision: a modern approach” Gonzalez and Woods, Digital Image Processing, 4th Ed., Pearson, 2018 Relevant Scientific Journals International Journal of Computer Vision Computer Vision and Image Understanding Image and Vision Computing IEEE Transactions on Pattern Analysis and Machine Intelligence Relevant Scientific Conferences ΙΕΕΕ Computer Vision and Pattern Recognition (CVPR) ΙΕΕΕ International Conference on Computer Vision (ICCV) European Conference on Computer Vision (ECCV) British Machine Vision Conference (BMVC) • Asian Conference on Computer Vision (ACCV) |
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Instructors:
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A. Argyros
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Course Master:
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Georgia Gregoriou, Vassilis Raos, Helen Savaki
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Duration:
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1 semester
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ECTS (credit hours):
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9 (45)
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Course outline:
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1. Primary and Secondary Visual Cortices. Neuronal properties and topographic organization.
2. Middle temporal area (MT). Experimental approaches to assess causal relationships between neuronal function and behavior.
3. Visual area V4.
4. Inferior Temporal cortex (IT). How complex visual stimuli are encoded in the brain.
5. From basic properties of visual neurons to visual awareness.
6. Frontal Eye Fields (FEF) and Lateral Intraparietal area (LIP).
7. Neural mechanisms of visual attention.
8. Cerebral circuits underlying visuomotor behavior in non human primates: ventral premotor area F5 - anterior intraparietal area (AIP).
9. Cerebral circuits underlying visuomotor behavior in non human primates: ventral premotor area F5 - primary motor cortex (MI).
10. Mirror neurons.
11. Cerebral circuits underlying visuomotor behavior in non human primates: ventral premotor area F4 - ventral intraparietal area (VIP).
12. Cerebral circuits underlying visuomotor behavior in non human primates: dorsal premotor area F2 - Area V6A.
13. Cerebral circuits underlying visuomotor behavior in non human primates
14. (i) Anatomy and physiology of prefrontal cortex. (ii) Consciousness
15. Embodied cognition: how we understand others' actions.
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Instructors:
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Georgia Gregoriou, Vassilis Raos, Helen Savaki
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Prerequisites:
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Neuroanatomy.
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Course Master:
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Constantine Stephanidis
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Duration:
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1 semester
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ECTS (credit hours):
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12 (60)
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Course outline:
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The aim of the course "Human-Computer Interaction" is to provide the theoretical base and the practical knowledge required for the design, development and evaluation of user interfaces.
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Instructors:
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Constantine Stephanidis
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Prerequisites:
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CELLULAR MECHANISMS OF LEARNING AND MEMORY
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Course Master:
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Kiki Sidiropoulou
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Duration:
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1 semester
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ECTS (credit hours):
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6 (30)
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Course outline:
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1. Behavioral tests for learning and memory in rodents.
2. Cellular correlates of memory: long-term potentiation and persistent activity
3. Synaptic mechanisms of long-term potentiation: NMDA, AMPA and metabotropic glutamate receptors.
4. Other biophysical mechanisms involved in long-term potentiation and memory.
5. Biophysical mechanisms of persistent activity: NMDA receptor and CAN channels.
6. Cell-type specific control of neuronal activity in vivo: Effects on learning and memory processes.
7. Effect of learning experience on biophysical mechanisms.
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Instructors:
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Kyriaki Sidiropoulou
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Course Master:
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Panos Trahanias
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Duration:
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1 semester
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ECTS (credit hours):
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6 (32)
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Course Outline:
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The course aims at the presentation and study of the mechanisms that facilitate a robotic system to perceive its environment and use the relevant information to autonomously navigate in it. This involves topics such as sensory types and their principles of operation, mapping, localization, path planning and tracking, obstacle detection and avoidance, landmarks and topological navigation. The course includes a project based on a recent scientific paper.
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Instructors:
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Panos Trahanias
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Course Master:
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D. Tsakiris
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Duration:
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1 semester
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ECTS (credit hours):
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12 (60)
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Course Outline:
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This course will consider the mechanical behavior and the motion control of complex robotic systems inspired from biology. Some examples of such systems are robotic systems emulating the reptile undulatory locomotion, active perception systems inspired by the human oculomotor system and robotic systems inspired by the visual motion-based insect flight control system.
The course will draw on studies from computational neuroethology and the computational neurosciences, in order to address biomimetic robot control and the modeling of biological motion control systems. More precisely, it will address topics related to:
the kinematics and dynamics of robotic systems,
the control of robotic systems, with emphasis on linear and non-linear control, on sensor-based control, on the use of learning in control and on behavior-based control,
the neural mechanisms for control of motion, such as the central pattern generator neural networks responsible for rhythmogenesis in biological systems.
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Instructors:
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D. Tsakiris
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INTRODUCTION TO THE PHILOSOPHY OF PERCEPTION
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Course Master:
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Maria Venieri
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Duration:
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1 semester
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ECTS (credit hours):
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6 (30)
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Course outline:
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1. Main issues in the philosophy of perception.
2. History of the field from Aristotle to the 20th century.
3. Perception and the problem of the external world. The arguments from illusion and hallucination.
4. Arguments for and against indirect realist theories of perception
5. Arguments for and against direct realism
6. Adverbial theory of perception
Active perception
7. The problem of perceptual content
8. The philosophical impact of psychological theories of perception (Bruner, Gibson, Marr)
9. Final assessment of the examined theories. Conclusions
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Instructors:
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M. Venieri
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INTRODUCTION TO SOCIAL ANTHROPOLOGY
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Course Master:
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George Nikolakakis
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Duration:
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1 semester
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ECTS (credit hours):
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6 (30)
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Course Outline:
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History of Social Anthropology
Branches and research objects
Anthropology and Social Sciences
Primitive and modern societiesBasic concepts in science and methodology
Qualitative and quantitative methods in Anthropology
Research and investigation on the spot
Interpretative approaches of institutions and fields
Selected analytical examples
Approaches of the symbolic systems
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Instructors:
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George Nikolakakis
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Prerequisites:
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None
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ANTHROPOLOGY OF THE SYMBOLIC SYSTEMS
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Course Master:
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George Nikolakakis
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Duration:
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1 semester
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ECTS (credit hours):
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9 (45)
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Course Outline:
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The concept of the symbolic system
Object, picture, symbol, sign
The symbolic exchange
Concept and approaches of the gift in "traditional" and modern societies
Language, thought and symbolic systems in primitive systems of categorization
Analytical examples of symbolic systemsApproaches of the political
Approaches of the religious
Approaches of the economic
Connections and correlations of the symbolic systems
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Instructors:
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George Nikolakakis
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Prerequisites:
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Introduction in Social Anthropology
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PRINCIPLES OF FUNCTIONAL IMAGING OF BRAIN MECHANISMS APPLIED TO fMRI DATA
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Course Master: |
P. Simos |
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Duration: |
1 semester |
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ECTS (credit hours): |
8 (40) |
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Course Outline: |
1. Principles of functional brain imaging 2. Principles of radiation physics applied to functional magnetic resonance imaging 3. Techniques used in the analysis of fMRI data 4. Use of fMRI in the study of linguistic brain mechanisms - Principles - Experimental design 5. Memory processes - Brain mechanisms - Experimental design 6. Use of fMRI in the study of linguistic brain mechanisms - A critical appraisal 7. Laboratory of fMRI data analysis - data conditioning (single-subject) 8. Laboratory of fMRI data analysis - statistics (single-subject) 9. Laboratory of fMRI data analysis - hypothesis testing (group-level) 10. Laboratory of fMRI data analysis - functional connectivity (CONN) 11. Use of functional brain imaging in the study of memory 12. Use of functional brain imaging in the study of emotions |
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Instructors: |
E. Orfanidou, P. Simos, Th. Maris, E. Kavroulakis |
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Prerequisites: |
EVOLUTIONARY COGNITIVE SYSTEMS
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Course Master:
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M. Maniadakis
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Duration
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1 semester
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ECTS (credit hours):
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6 (30)
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Course outline:
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The course aims to provide basic knowledge in using evolutionary algorithms for the design and implementation of artificial cognitive systems. The course will particularly focus on (i) the exploitation of neuroscience observations for implementing brain-like cognitive dynamics, and (ii) the study of the internal mechanisms self-organized in the model as a means for extracting valuable hypothesis about brain functioning. The following topics will be covered: • Interpreting cognitive systems design as an optimization problem. • How evolutionary algorithms compare to other optimization methods. • The encoding of the cognitive system. • Solution evaluation and reproduction. • Domain specific reproduction operators. • Multi-criteria optimization approaches. • Evolutionary developmental systems. • Parallel evolutionary systems. • Competitive co-evolution. • Cooperative co-evolution. |
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Instructors:
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M. Maniadakis
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Prerequisites:
|
Basic programming skills
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|
Course Masters
|
I. Tollis, I. Tsamardinos
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Duration
|
1 semester
|
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ECTS (credit hours):
|
10 (50)
|
|
Course outline:
|
Objectives of the course (preferably expressed in terms of learning outcomes and competences):
• Provide an introduction to the type of algorithmic problems and techniques that are widely used in the field of bioinformatics
• Familirize the students with the analysis of biological data and measurements in order to discovery new biological knowledge
Course contents:
The course starts with an introduction to the basic molecular biology concepts and types of biological data, such as gene expression micro-arrays, Single Nucleotide Polymorphism assays, etc. The introduction is oriented towards computer science students with no prior background in molecular biology.
A selection of algorithms used in bioinformatics from the list below is then presented:
• Basic statistical methods (hypothesis testing, multiple hypothesis testing and False Discovery Rate control) and methods for biomarker discovery. Applications to the discovery of differentially expressed genes.
• Basic methods of optimization in biomedical networks and graphs (metabolic networks, gene interaction networks, RNA/DNA interaction networks, gene interaction networks)
• Local and global string matching using dynamic programming and applications to the optimization of DNA structure.
• Modern methods for classification using Support Vector Machines and applications to the diagnosis and prediction of pathology from micro-array gene expression data.
• Modern and basic clustering algorithms and applications to the analysis of biological data for the identification of disease subtypes.
• Biomarker and molecular signature discovery using modern feature selection methods (Markov Blanket-based variable selection).
• Analysis of sequences based on Hidden Markov Models and application to biological sequences.
Recommended reading:
Reading material is selected among and provided by different books and papers in the scientific literature. The book "An Introduction to Bioinformatics Algorithms" by Neil C. Jones and Pavel A. Pevzner is followed loosely.
Teaching methods:
There are two weekly lectures that teach the main material of the course. These are supported by recitations taught at most once a week that delve into selected details of each week's material and go through the solutions to a selected list of exercises. In the course, there are weekly or biweekly exercises that contain a theoretical and a practical (programming) part. Programming is performed mostly in the Matlab environment.
Assessment methods:
The final grade is a linear function of the grades of the final exam and the assignments with weights 70, 30. Language of instruction: The language is Greek or English depending on the audience. English speaking students are welcome.
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Instructors:
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I. Tollis, I. Tsamardinos
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Prerequisites:
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CS-380, CS-217, MΑΘ-105
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Course Master
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I. Tsamardinos
|
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Duration
|
1 semester
|
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ECTS (credit hours)
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10 (50)
|
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Course outline:
|
Objectives of the course (preferably expressed in terms of learning outcomes and competences):
• To provide an introduction to the type of problems addressed in the field of Machine Learning, the relation with other related fields, and familiarize the students with the major ideas, theoretical results, techniques, algorithms, and methods of the field
• To teach the programming of simple Machine Learning algorithms
• To enable the student to perform simple, yet effective and methodologically sound data analysis, provide prognostic models, perform variable selection, tune the algorithm parameters, and provide reliable prediction performance estimates
• To enable the student to assess the basic theoretical and practical advantages and disadvantages of a learning algorithm
• To familiarize the student with general methods for casting problems as Machine Learning problems and general methods for representing the data appropriately
Course contents:
a) Basic statistical methods employed in Machine Learning, such as simple hypothesis testing b) Basic learning algorithms for supervised learning, such as Bayesian Classifiers, Decision Trees, Feed-Forward Neural Networks, k-Nearest Neighbors, and Support Vector Machines. c) Metrics of performance, such as accuracy and the Area Under the Receiving Operating Characteristic curve d) Methods of estimating performance and optimization of parameter values, such as cross-validation and nested cross-validation e) Issues of data cleaning and representation f) More advanced methods : variable selection techniques (e.g., Univariate Association Filtering, Recursive Feature Elimination, Wrapping search, Bayesian Network-based variable selection), and Causal Discovery methods g) Selected examples of application of Machine Learning methods h) Overview of other learning problems and paradigms (unsupervised methods, clustering, relational learning, learning with timed data, active learning, etc.)
Recommended reading: Reading material is selected among and provided by different books and papers in the scientific literature. The books mostly influencing the class are: a) Machine Learning, by Tom Mitchell and b) The Elements of Statistical Learning: Data Mining, Inference, and Prediction , by Trevor Hastie, Robert Tibshirani, Jerome Friedman
Teaching methods: There are two weekly lectures that teach the main material of the course. These are supported by recitations taught weekly that delve into selected details of each week's material and go through the solutions to a selected list of exercises. In the course, there are weekly or biweekly exercises that contain a theoretical and a practical (programming) part. Programming is performed in the Matlab programming environment. During the third part of the course, the students are requested to complete a project; the topic is selected and agreed with the instructor.
Assessment methods:
The final grade is a linear function of the grades of the final exam, the midterm exam, the assignments, and the project with weights 30, 20, 20, 30.
Language of instruction:
The language is Greek or English depending on the audience. English speaking students are welcome.
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Instructors:
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I. Tsamardinos
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Prerequisites:
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CS-150, CS-217, CS-380
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INTRODUCTION TO ARTIFICIAL INTELLIGENCE
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Course Master
|
I. Tsamardinos
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Duration
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1 semester
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ECTS (credit hours):
|
10 (50)
|
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Course outline:
|
Objectives of the course (preferably expressed in terms of learning outcomes and competences):
• to provide an introduction to the theory, practice and philosophy of Artificial Intelligence (AI) as a field
• to study in depth several basic and fundamental algorithmic techniques of AI, particularly techniques that are widely used in other fields too
• to familiarize the students with some of the basic programming tools of the field and other programming paradigms, such as functional programming in Lisp and symbolic programming
Course outline:
• Introduction to the problems and history of Artificial Intelligence
• Problem solving techniques using searching, including informed and uniformed search, as well as constraint satisfaction techniques
• Logic-based agents
• Representing problems and reasoning in Propositional and First Order Logics
• Planning
• An introduction to reasoning with uncertainty, making simple decisions under uncertainty
Recommended reading:
Artificial Intelligence: A Modern Approach, by S. Russell and P. Norvig
Teaching methods:
There are two weekly lectures that teach the main material of the course. These are supported by recitations taught at most once a week that delve into selected details of each week's material and go through the solutions to a selected list of exercises. In the course, there are weekly or biweekly exercises that contain a theoretical and a practical (programming) part. Programming is performed in the Allegro Lisp programming environment.
Assessment methods:
The final grade is a linear function of the grades of the final exam, the midterm exam, and the assignments with weights 40, 25, 35.
Language of instruction:
The language is Greek or English depending on the audience. English speaking students are welcome. |
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Instructors:
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I. Tsamardinos
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Prerequisites:
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CS-240, CS-180
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FOUNDATIONS OF COGNITIVE SCIENCE AND UNIFIED THEORIES OF COGNITIVE SYSTEMS
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Course Master:
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Petros A. M. Gelepithis
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Duration
|
1 semester
|
|
ECTS (credit hours):
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12 (60)
|
|
Course outline:
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To offer a concise, interdisciplinary review of the foundations on which the study of cognitive systems is based. On successfully finishing the course, the students will have delved into the main research issues in the foundations of the sciences of brain and mind. Foundations of Cognitive Science and Unified Theories of Cognitive Systems: Basic notions and tools. Computational systems of knowledge representation: Philosophical, neurophysiological, and computational issues. The Human Language as tool and knowledge system. Human and Robotic emotions: Theories, problems, and mechanisms. Interactions between logic and emotion. Comparative review of theories of consciousness: philosophical, neurophysiological, computational, psychological. Comparative presentation of unified theories of cognition: Psychological, computational, neurophysiological. A novel, axiomatic theory of cognitive systems: Consequences for the sciences of brain and mind. Students' assessment will be entirely based on a written research essay. Literature: Anderson, J. R., & Lebiere, C. (2003). The Newell Test for a theory of cognition. Behavioral and Brain Sciences. 26: 587-640 (including commentary and response by the authors). Bennett, M., Dennett, D., Hacker, P., & Searle, J. R. (2007). Neuroscience and Philosophy: Brain, Mind, and Language. Columbia University Press. Boden, M. A. ed. (1990). The philosophy of artificial intelligence. Oxford University Press. Branquinho, J. ed. (2001). The Foundations of Cognitive Science. Clarendon Press, Oxford. Dautenhahn, K. (2007). Socially intelligent robots: dimensions of human–robot interaction. Phil. Trans. R. Soc. B, 362: 679–704. Merker, B. (2007). Consciousness without a cerebral cortex: A challenge for neuroscience and medicine. Behavioral and Brain Sciences, 30: 63-134 Newell, A. (1990). Unified theories of cognition. Harvard University Press. Newen, A., & Bartels, A. (2007). Animal Minds and the Possession of Concepts Philosophical Psychology, 20, 3, 283-308. Seth, A. K., Baars B. J., & Edelman, D. B. (2005). Criteria for consciousness in humans and other mammals. Consciousness and Cognition, 14: 119-139 Sirois, S., Spratling, M., Thomas, M. S. C., Westermann G., Mareschal, D., & Johnson M., H. (2007). Précis of: Neuroconstructivism: How the Brain Constructs Cognition. Behavioral and Brain Sciences, (in press). |
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Instructors:
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Petros A. M. Gelepithis
|
BRAIN CONNECTIVITY ANALYSIS USING EEG/ MEG
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Course Master:
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V. Sakkalis
|
|
Duration
|
1 semester
|
|
ECTS (credit hours):
|
6 (30)
|
|
Course outline:
|
Functional Imaging Modalities, EEG/ MEG signals and facts, Cross-correlation & Coherence, Short time Fourier Transform Coherence, Wavelet Coherence, Phase Synchronization, Deterministic Chaos, Nonlinear interdependence measures, Surrogate time series analysis, Topographic Brain Mapping, Brain Network Visualization and Quantitative analysis using Graphs, Source localization techniques |
|
Instructors:
|
V. Sakkalis
|
INTRODUCTION IN MOLECULAR NEUROBIOLOGY
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Course Master:
|
I. Charalampopoulos
|
|
Duration:
|
1 semester
|
|
ECTS (credit hours):
|
6 (30)
|
|
Course outline:
|
Introduction to Molecular Neurobiology: from structure to therapeutics.
Neuronal interactions at the molecular level.
Mechanisms of neuroprotection and neurorepair.
Neuroregeneration in the CNS and PNS: molecular mechanisms.
Adhesion molecules and ECM in neural tissue.
Glial cells and regeneration/remyelination.� (Use of the population model of muscle in the study of muscle electrical activity via mathematical modeling and computer simulations
Neural stem cells biology-Adult neurogenesis.
Neurosteroids, neurotransmitters and neuropeptides in Nervous System.
Interactions of Nervous and Immune systems.
Animal models of neurodegeneration.
Neurodegenerative diseases: pathophysiology and therapeutics.
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Instructors:
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I. Charalampopoulos
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Prerequisites:
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Basic Neurosciences
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