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VKN: Computational and Cognitive Neuroscience

Institute of computer science, Faculty of Science
UPJŠ Košice
Winter semester 2019

Lecturer: Norbert KopčoInstitute of Computer Sciencenorbert.kopco@upjs.sk, Jesenná 5, room T2.16, tel. 234 2450Teaching assistants: Keerthi Doreswamy, kk.creare@gmail.com, Jesenná 5, room 33, Peter Lokša, peterloksa@gmail.com, Jesenná 5, room 33.

Lectures:     Thursday 13:30-15:00 in classroom SA1C05 (Jesenná 5) – common with UNV

Class contents

Description of the central neural system and cognitive processes of human, with an emphasis placed on computational aspects of study in cognitive and neural sciences. Chosen topics from the cognitive sciences (in continuation of UNV). Overview of the methods of the teoretical study in cognitive and neural sciences including connectionist, statistic and system-teoretical access to cognitive processes and neural structures modeling. Overview of the chosen models of human visual and hearing system, learning, reasoning, resolving the problems, decision making and develop of the brain structures.

Prerequisites

– UNV: Intro to neuroscience
– basics of biology, chemistry, physics, psychology
– linear algebra and differential equations
– knowledge of MATLAB or willingness to learn

Class philosophy and student’s work

The emphasis will be on individual work during the semester. Attendance at lectures is very important (and will be monitored using an attendance sheet). Students’ work in the labs will involve several assignments which the students will work out individually or in small groups. Submitted assignments will be graded and will constitute a part of the final grade. During the semester there will be two compulsory written exams, one in the middle (midterm exam) and one at the end of the semester (final exam). Another requirement is to work on a essay. Based on the points obtained by a student for the assignments and written exams, a final overall grade will be proposed after the final exam on the final week of classes. Those who want to improve their grade will have an opportunity to sign up for an additional exam.
Important: Your work during the semester will be watching specific time plan. For violation of this plan (submit the assignment later, absence on the exam etc.) are definated penalties. If you want to avoid them from the objective occasion and you can not abide some date, inform me soon. (e.g. if you inform me before the exam, that you can not write it, we can find another date for exam).

Grading

Contribution of the individual tasks to the final grade:
– active participation at the lectures 6%
– assignments 12% (4 assignments, 3% each) – the number of points is reduced by 20% of the final grade for the plagiarism.
– essay 12%
– midterm exam 35%
– final exam 35%
– extra: 5 – 10% of the grade can be earned by the participation on the experiments in the Laboratory of perception and cognition. We will keep you informed about the options.

Penalty in grading and ethics

The number of points earned for assignments and projects is reduced by 20% of the maximum value for the assignment/project with each week of delay. Each assignment, exam, and essay should reflect the work and knowledge of the person who is stated as the author. You can work in groups (up to 4 people) for the assignments, but I expect that everyone who is listed in the assignment as the author actually participated on it. At random, I will review how the assignment was done for all who are stated as the authors. And if I find that an author does not know how something was done in the assignment, it could mean losing up to 20% of the overall grade for the whole group (i.e., including those who actually did the assignment and can explain how it was done). Once again, I emphasize that the goal is that everyone, not just one person from the group, should do the work on the assignment.

Literature  (Some books are available in the electronic form from domain TU tuke.sk):

Primary source of information are the PDF slides of lectures, which you can find in section Lectures on this website before the lecture.

Additional sources:

Stillings et al.: Cognitive Science: An Introduction, 2nd ed., MIT Press, 1995 (referred to as CSAI, available electronically)

Posner, M: Foundations of Cognitive Science. MIT Press, 1989 (the copy is in my office)

Gazzaniga M. (ed.): The New Cognitive Neurosciences. 2nd ed. MIT Press. 1999 (available electronically here – upon request. Alternatively, for $15 you can get 5-days access and download whichever of more than 400 books available on CogNete. Printed version of the book is in my office.)
Hertz J, Krogh A and Palmer RG: Introduction to the theory of neural computation. Addison-Wesley 1991 (HKP, the copy is in my office)

Levine DS: Introduction to neural and cognitive modeling. Lawrence Erlbaum 1991 (the copy is in my office)

Dayan P and LF Abbott: Theoretical Neuroscience – Computational and Mathematical Modeling of Neural Systems. MIT Press, 2001 (further referred to as TN, available electronically)

Novák M, Faber J a O Kufudaki: Neuronové sítě a informační systémy živých organismů. Grada, 1993 (referred to as NS, available electronically)

Kandel ER, Schwartz JH a TM Jessell: Principles of Neural Science. McGraw-Hill, 2000 (the copy is in my office)

Wilson RA a FC Keil: The MIT Encyclopedia of the Cognitive Sciences. MIT Press, 1999 (MITECS available electronically)

Rybár J, Beňušková Ľ a Kvasnička V: Kognitívne vedy. Kalligram, 2002 (referred to as RKV, the copy is in my office)

Marek Dobeš: Neuropsychológia.(available electronically)
Purves et al.: Neuroscience. 2nd ed., Sinauer Associates, 1991 (available in fulltext electronically. It is not possible to orient in the book from the contents but you can view the contents and then search for the title of the chapter.)
Novák M, Faber J a O Kufudaki: Neuronové sítě a informační systémy živých organismů. Grada, 1993 (referred to as NS, available electronically)
Hertz J, Krogh A and Palmer RG: Introduction to the theory of neural computation. Addison-Wesley 1991 (HKP, the copy is in my office)

Essay

Detailed information regarding essays can be found here.
Submit date of the essay is 28.11. (Week 11.).

Schedule of lectures

Week 1:        19.9.    Introduction (slides in English, Slovak)

Literature: CSAI chapter 1 (possibly also chapters 2 and 3), Preface to RKV, chapter by M. Dobeš about Cognitive psychology

Topic 1: Chosen topics in cognitive and neural sciences

Week 2:        26.9.    Vision 1: Neural base of vision. (slides in PDF EN, SK)

Literature: TN chapters 2.3-2.8, NS chapter 4.3.4., CSAI chapters 12.1, 12.3, 12.5    Andoga R., Dobeš M. (2003) Object-oriented attention and modeling with neural network. The third slovak-czech seminar: Cognition, artificial life and computer intelligence.    Schmolensky (2000) The Primary Visual Cortex

Summary from the previous year (I expect, that you look at it – you can have a questions from it on the exam): Perception of brightness, contours, color. BCS/FCS model. Perception of size and distance. Lecture from the previous year you can find here.

Week 3:        3.10.    Vision 2: Object Recognition. Visual scene analysis. (slides in PDF)Literature: TN chapters 2.3-2.8, NS chapter 4.3.4., CSAI chapter 12
                Selfridge O. G. (1959) Pandemonium: A Paradigm for Learning. In D. V. Blake & A. M. Uttley (Eds.) Proceedings of the Symposium on Mechanization of Thought Processes, 511-529.
                 Riesenhuber, M., and Poggio, T. Models of Object Recognition. Nature Neuroscience 3 supp., 1199-1204 (2000)
                 Biederman, I. (1987) Recognition-by-components: A theory of human image understanding. Psychological Review. 1987 Apr Vol 94(2) 115-117
Web: Daniel Simons Visual cognition demos, Serendip: Tricks of the eye, wisdom of the brain

Week 4:        10.10.  (lectures form weeks 2. and 3.) Auditory cognition: How we supress echoes. Auditory scene analysis (computer scene too). (slides in PDF)

Summary from the previous year (I expect, that you look at it – you can have a questions from it on the exam): What is noice and hearing. List of topics of the hearing study. Frequency selectivity. Binaural and dimensional hearing. Lecture from the previous year you can find here. Also the lecture 6. from the previous year contains informations about plasticity of the dimensional hearing system – I can give you a question from this topic.
Literature:         Demonstration of the hearing efects and illusions (DeutschPrecedence, Clifton, Fransen, Gestalt principles, list of the demonstrations on CD)
Darwin, C. J. and R. P. Carlyon (1995). Auditory grouping. Hearing. B. C. J. Moore. San Diego, CA, Academic Press: 387-424
Method Independent Component Analysis used on computer analysis of the hearing scene (demo of the Cocktail Party efect).
Bregman, A. S. (1990). Auditory Scene Analysis: The Perceptual Organization of Sound. Cambridge, MA: MIT Press.

Week 5:        17.10.  Cortical auditory processing. (slides in PDF)

Literature:         Rauschecker, JP and Tian B (2002) Mechanisms and streams for processing of „what“ and „where“ in auditory cortex. PNAS 97:11800-11806
Guenther FH, et al. (2002) ‘Holes’ in the Brain Help Us Sort Out Sounds. 143 meeting of the Acoustical Society of America.

Week 6:        24.10.  Crossmodal interactions (hearing, vision, touch). Other topics of study of brain and mind (consciousness, emotions, motivation, decision making, reasoning). (slides in PDF)

Literature: Keyword Emotions from Purves et al.: Neuroscience.
PICARD, R. W. (1997) Affective Computing. The MIT Press.
Chapter by M. Dobeš about Cognitive psychology
Web Seeing through Sound.

Topic 2: Modeling in cognitive and neural sciences

Week 7:        31.10.  (lectures form weeks 6. and 7.) Introduction to cognitive and neural modeling, historical view. (slides in PDF)

Literature:HKP: Introduction MITECS chapters Connectionismus, Cognitive Modeling Extra literature:Elman: Rethinking Innatenes. Chapter 1

Week 8:        7.11.    Connectionist modeling 1 – Interactions between STM and LTM in simple neural model of the classical conditioning. (slides in PDF)
Literature:
Carpenter, G.A. (1989). Neural network models for pattern recognition and associative memory. Neural Networks, 2, 243-257

Week 9:        14.11.  Connectionist modeling 2 – Additive and shunting neural networks. (slides in PDF)
Literature:
Grossberg, S. (1982). Why do cells compete? UMAP Unit 484, The UMAP Journal, Vol. III, No. 1.
Grossberg, S. (1980). How does a brain build a cognitive code? Psychological Review, 87, 1-51. – read mainly Appendix C.

Week 10:      21.11.  two lectures Connectionist modeling 3 – Learning rule Outstar. (slides in PDF)

Grossberg, S. (1980). How does a brain build a cognitive code? Psychological Review, 87, 1-51. – read mainly Appendix B.
Extra literature:
Levy, W.B. and Desmond, N.L. (1985). The rules of elemental synaptic plasticity. In W.B. Levy, J.A. Anderson, and S. Lehmkuhle (Eds.), Synaptic modification, neuron selectivity, and nervous system organization. Hillsdale, NJ: Erlbaum.
Grossberg, S. (1974) Classical and instrumental learning by neural networks. Progress in Theoretical Biology, 3, pp. 51-141.
Guenther, F.H. (1995). Speech sound acquisition, coarticulation, and rate effects in a neural network model of speech production. Psychological Review, 102, pp. 594-621.

Connectionist modeling 4 – Recurrent competitive fields. (slides in PDF)

Grossberg, S. (1976). Adaptive pattern classification and universal recoding, I: Parallel development and coding of neural feature detectors. Biological Cybernetics, 23, 121-134.
von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striate cortex. Kybernetik, 14, pp. 85-100. [NFR Chapter 17.]
Appendix A of Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, pp. 1-51.

Week 11:      28.11. Connectionist modeling 5 – Adaptive resonance theory. (slides in PDF)
Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, pp. 1-51.Carpenter, G.A. (2001). Neural network models of learning and memory: leading questions and an emerging framework. Trends in Cognitive Sciences, 5, 114-118

Week 12:      Statistical and decision-theory modeling.   

Durlach (1968) A Decision Model for Psychophysics.
Website about Luis Leon Thurstone – he developed many psychological measuring techniques.
Definition of the correlation coefficient on MathWorld.

Topic 3: Topics of the current research in cognitive and neural science on institute of computer science and (outline plan)

Week 13:      12.12.  Guest lectures

Schedule of labs (preliminary)

Week 1:         introduction to assignments and essays, description of the electronic sources

Week 2:        free day

Week 3:       work on essays

Week 4:        assignment 1 (to be submitted by the beginning of the labs in week 6.)

Week 5:        assignment 2 (to be submitted by the beginning of the labs in week 7.)

Week 6:        free day

Week 7:        assignment 3 (to be submitted by the beginning of the labs in week 9.)

Week 8:        midterm exam in the time of the lecture

Week 9:        assignment 4 (to be submitted by the beginning of the labs in week 12.)

Week 10:      work on the essays

Week 11:      submit the essay

Week 12:      submit the assignment 4

Week 13:      final exam

Useful links

Look at the useful links for Intro to neuroscience