FOUNDATIONS OF INFORMATION THEORY AND COMPUTATIONAL NEUROSCIENCES - MOD. 2

The course is one of the mandatory educational activities of the Master of Science in Engineering Physics degree program, Physics of the Brain curriculum, and enables the student to gain knowledge and understanding of the fundamental and applied concepts of systems and computational neuroscience.

The course is divided into four submodules:
1. Tactile Perception (teacher: Mathew Diamond, diamond@sissa.it)
2. Physiology and functions of the mammalian visual system (teacher: Davide Zoccolan, zoccolan@sissa.it)
3. Evolution of Neural Computation (teacher: Alessandro Treves, ale@sissa.it)
4. Bayesian modeling and information theory for neuroscience (teacher: Eugenio Piasini, epiasini@sissa.it)

1. Knowledge and understanding skills
“Tactile Perception": learn about the major historical advances in understanding the organization and processing of information in the cerebral cortex; principles of localization of function; mechanisms of sensory transduction, particularly in the somatosensory system; basic principles of psychophysical methods and psychometric functions; methods for studying the neuronal basis of perceptual functions, including measures of brain activity in behavioral experimental subjects.

“Physiology and functions of the mammalian visual system": introduction to the anatomy and neurophysiology of the visual system, with special emphasis on the functions of the so-called ”ventral pathway.” Methods for recording neuronal activity and for graphing and performing preliminary analysis of recorded data. Three different types of computational approaches (descriptive, mechanistic and functional) to model neural data will be presented.

“Evolution of Neural Computation": arriving at an overview of the evolution of the nervous system, particularly of our and related species, with focused attention on the computational mechanisms that evolved hundreds of millions of years ago in its different components. This overview should be related to recent advances in the field of artificial neural computing systems.

“Bayesian modeling and information theory for neuroscience": be aware of how normative approaches based on Bayesian statistics and information theory can contribute to the investigation of sensory perception and the design and interpretation of psychophysics experiments.


2. Ability to apply knowledge and understanding
“Tactile Perception": familiar with the interpretation of psychophysical data and must be able to evaluate the relationships between neuronal activity and simultaneous behavioral measures.

“Physiology and functions of the mammalian visual system": Critically read and understand a scientific article in the area of systems neuroscience. Be able to understand what kinds of analyses and models can be applied to the responses obtained by visual neurons following the presentation of images and movies.

“Evolution of Neural Computation”: compare the fundamental assumptions and methodologies with a view to their application to neural network analysis.

“Bayesian modeling and information theory for neuroscience": be able to construct simple Bayesian models for sensory tasks, starting with a small set of hypotheses about the key cognitive factors involved and deriving concrete, testable predictions on experimental data in a systematic way. Develop an intuitive understanding of the efficient coding principle and the kinds of arguments that can be developed based on it.


3. Autonomy of judgment
Be able to apply critical analysis to published work in behavioral neuroscience. Development of insight into experimental design.


4. Communication skills.
Be able to ask clear and precise questions about the material presented and discussed.
Be able to communicate the knowledge learned using appropriate terminology, both orally and in writing.
Know how to interact with the instructor and course colleagues in a respectful and constructive manner, particularly during work done in groups.


5. Learning skills.
Know how to take notes, selecting and collecting information according to its importance and priority.
Know how to be sufficiently autonomous in collecting data and information relevant to the problem investigated.

Useful a familiarity with concepts covered in Mathematical Analysis I and Mathematical Analysis II courses (derivatives and integrals to one and more variables), Linear Algebra (vector spaces and operations between vectors, equations to eigenvalues). Familiarity with probability and statistics and the logistic function helpful.

1. Physiology and functions of the mammalian visual system (an introduction to systems/computational neuroscience)
2. Sensory Systems: Tactile Perception
3. Evolution of Neural Computation
4. Bayesian modeling and information theory for neuroscience

Martin, A. R., Brown, D. A., Diamond, M. E., Cattaneo, A. & De-Miguel, F. F. From Neuron to Brain, Sixth Edition. (Oxford University Press, 2021).
Wichmann, F. A. & Hill, N. J. The psychometric function: I. Fitting, sampling, and goodness of fit. Perception & Psychophysics 63, 1293-1313, doi:10.3758/BF03194544 (2001).
Hernández, A. et al. Decoding a perceptual decision process across cortex. Neuron 66, 300-314 (2010)
Eric Kandel, John D. Koester, Sarah H. Mack. Principles of Neural Science, Sixth Edition
Peter Dayan, Laurence F. Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press
Rolls, E., & Treves, A. (1997). Neural networks and brain function. Oxford university press.
Ma, Koerding and Goldreich (2022). Bayesian Models of Perception and Action (http://www.cns.nyu.edu/malab/bayesianbook.html ).
Richard McElreath (2nd ed 2020). Statistical Rethinking.
David MacKay (2003). Information Theory, Inference, and Learning Algorithms (http://www.inference.org.uk/mackay/itila/book.html ).

Achievement of learning objectives is assessed through the final written exam, consisting of topics to be developed.

In-person or zoom lectures. Students are encouraged to actively participate with questions and queries.

English

written and oral

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development