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Ph.D. Program in Brain Sciences: Computation and Information Processing (597)
1. REQUIRED COURSES
76900/76901 Physiology of the Nervous system A/B
4 credits for each course
76901 – 1st Semester, Dr. Mickey London
76900 – 2nd Semester, Dr. Mati Joshua
The two parts of the course will deal with basic principles and mechanisms of neuronal sensory systems. The focus of the course will be from sensory transduction, through sensory processing to perception. The course will provide basic as well as more advanced knowledge about several neuronal systems focusing on physiology and sensory coding.
The second half of the course will focus on the motor system, learning and memory, spatial navigation and various diseases of the nervous system.
The course presents the very basics of the topics, but additionally it touched upon the cutting edge of research.
The course is comprised of regular lectures, advanced lectures covering state-of-the-art research by scientists in each field, and presentations of current research papers by students.
Modern neuroscience research incorporates a variety of research methods and approaches, in order to develop a full and coherent picture of the studied topic. At ELSC, the expertise of the researched covers the whole range from theoretical neuroscience, through molecular, physiological and behavioral animal research, to human imaging and recording. This course will cover some of the prominent basic experimental approaches used today in neuroscience, in both human (fMRI introduction, experiments and analysis by Prof. Amir Amedi) and animal studies. Students will learn both the theory and the practice of important traditional physiological methods (slice and in-vivo recording), stereotactic brain surgery, basic molecular biology approaches (gene expression profiling, cloning, immunohistochemistry), imaging (confocal, CLARITY) and mouse behavior. Optogenetic tools will be applied at all levels (from cloning, through electrophysiology to behavior). All of the above methods will be used for the study of fear behavior, and will merge together into a multilayered picture of the state-of the art of scientific knowledge in this field.
76908 Theory of Neural Networks I
Prof. Yonatan Loewenstein
The aim of this course is to provide students with the basic concepts of storage and processing of information in neural networks, and to equip them with analytical and numerical methods in the study and application of neural network models. In particular, the course focuses on concepts and methods of dynamics and their application to neural networks. The course syllabus includes:
1. Basic concepts: electrical properties of neurons, binary and analog neurons, deterministic and stochastic dynamics of the binary neuron, physical analogy – a spin in a magnetic field, networks of binary neurons.
2. Concepts from dynamics and statistical mechanics: master equation, dynamics of averages, detailed balance principle, thermodynamic equilibrium, energy, free energy and entropy.
3. Hopfield network: associative memory model, Hebb's learning rules, Hopfield model, signal to noise ratio analysis, memory capacity, networks with asymmetric connections, the perceptron algorithm.
4. Networks of analog neurons: linear networks, firing rate models, nonlinear dynamics, bifurcation transitions, the Fitzhugh-Nagumo model, oscillations in excitatory-inhibitory networks, model of the olfactory system, neural integration.
76909 Theory of Neural Networks II
Dr. Yoram Burak
The course is concerned with computation and learning in neural network models. The course is divided into four parts:
1. Supervised learning in feed-forward neural networks: Perceptron, support vector machine, boosting, gradient-based learning, online learning.
2. Unsupervised learning: principal component analysis, self-organizing feature maps.
3. Reinforcement learning: learning by random exploration, the REINFORCE algorithm.
4. Computation in recurrent networks: learning of temporal sequences, winner-take-all networks, neural integration, memory in dynamical systems.
2 credits points, 2nd Semester
One of the main goals, if not the ultimate goal of neuroscience is to understand human function, in health and disease. While some of the information can be gleaned from studies in vitro or in laboratory animals, eventually humans have to be studied. The complexity of the human system, the unique questions that can be asked to their full capacity only in humans, as well as ethical issues, require special methods to be applied in this domain, starting from psychophysics and experimental (neuro) psychology methods through functional and structural neuroimaging and stimulation methods.
This course will present students with a selection of the main methods used in human neuroscience. In each, we will discuss the rational, the signals measured, the technological bases, the questions that can be addressed, the main analytic techniques, and the potential pitfalls and non-trivial limitations of each method.
At the end of the course students will be able to critically evaluate evidence obtained by the main tools of human neuroscience, to appreciate the advantages and challenges of human cognitive neuroscience methods, as well as to design and analyze basic experiments in the tools covered.
76989 Introduction to Cogniation
Prof. Yosef Grodzinsky
The course introduces some of the fundamental topics in cognitive psychology: visual perception, attention, memory, organization of semantic knowledge and problem solving. Students will learn basic concepts and approaches, such as short-term vs. long-term memory, facilitation vs. inhibition, automatic vs. controlled processes and high vs. low levels of processing. In addition, students will become familiar with the basic methodologies in human performance research and will learn how they are applied in the study of cognition. A special emphasis will be placed on network models of high cognitive processes.
76913 Advanced Cognitive Processes
Prof. Merav Ahissar
Current topics of research—in language, reading, attention, decision making, perception and perceptual learning—will be presented by teachers affiliated with cognitive research in the Department of Psychology. Teachers will present their novel findings in the broader context of their respective fields. At the end of the course, students will submit a paper that addresses several of the topics presented.
76915 Introduction to Information and Learning Processes
Prof. Naftali Tishby
The course will provide formal tools for understanding phenomena such as learning decision making and information processing. Specifically, we will cover:
1. Statistical decision theory - Bayesian approaches and Hypothesis testing
2. Parameter Estimation - Bayesian and maximum likelihood approaches. Bias variance
tradeoffs. Minimum variance unbiased estimators. Cramer Rao inequality. Conjugate priors.
The Expectation Maximization (EM) algorithm
3. Information theory - Source and channel coding theorems. Mutual information and
information processing principles.
4. Theory of classiﬁcation algorithms - The PAC framework, VC dimension, generalization
bounds, model selection.
The course will provide formal tools for understanding phenomena such as learning, decision making and information processing.
76911 calculus and probability for neuroscience + 76992 linear algebra for neuroscience*
Ms. Lior Tiroshi and Ms. Rotem Maoz
6 credits - 3 credits for each course
This introductory math courses consist of three sections, one in linear algebra, another in differential and integral calculus, and the third in probability. The course will also provide firsthand experience in MATLAB programming.
1. Linear algebra: vector and matrix arithmetic, determinants, systems of linear equations, linear spaces, linear transformations, matrix digitalization, Inner product spaces, orthogonal and orthonormal bases.
2. Differential and integral calculus: complex numbers, differential equations, RC circuits, Euler’s method for numerical integration, special functions, Taylor series and Fourier analysis, functions of multiple variables and partial derivatives, optimization under constraints (Lagrange multipliers), double and triple integration.
3. Probability: basic concepts in probability, discrete and continuous distributions, experimental distributions, combination theory.
* For those students who did not take an equivalent course.
76932 The molecular basis of neuronal processing
(Supervised by Prof. Hermona Soreq)
This class will familiarize students with the current views on the molecular and cellular pathways involved in neurodegenerative diseases and on their impact and relevance in clinical diagnosis and treatment. Because of the extended life expectancy during the last century, neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis (ALS) are becoming more and more common. Although very different from one another, these diseases share common mechanisms and pathways that ultimately lead to neuronal death. These include aberrant gene expression and protein folding, decrease of the proteasome function, synaptic failure and oxidative stress. The course will include lectures given by international guest experts, and will draw heavily from recent research in everything from basic science performed in model organisms, to clinical trials that aim to find drugs and/or treatments to alleviate these diseases. Students must attend the weekly class, and will be required to submit homework based on in-class discussions.
This course is designed to expose first year students to state-of-the-art research. Students will work in small groups led by one of the teachers of the course, and each group will meet once a week. At the end of the semester, each group will present its work to the whole class.
76980 M.Sc. Seminar
Prof. Israel Nelken
This seminar is usually held at the week before the beginning of the academic year. Students report on a project that they have worked on under the supervision of an advisor. The project may be a theoretical study, an experimental project, or a literature review. This seminar is a required course for ELSC students who do not hold a M.Sc. degree.
2. ELECTIVE COURSES
76921 Human Vision: A Computational Approach
Prof. Yair Weiss
Even the most sophisticated computer vision algorithms are still far from the performance of a two-year-old child. In this course we will study the human visual system from a computational approach. We will focus on three main topics: motion, color and form. For each topic we will review the psychophysical data (including some amazing illusions, see http://web.mit.edu/persci/), describe computer vision algorithms (see http://www.cs.huji.ac.il/%7Ehvca/algorithms.html) and discuss computational theories.
76922 Lab Rotation
Prof. Yonatan Loewenstein
Students are given the opportunity to do practical work at Hebrew University laboratories. In the framework of this course, students and teachers define a project that can be completed in one semester. Students may choose to present the project at their M.Sc. seminar. Students must work in two different labs to receive course credit.
76925 Computational Approaches in Clinical Neuropsychiatry
Dr. Shahar Arzy
Neuroscience teaches us computational, theoretical and experimental approaches to better understand physiological and pathological aspects of the human brain. But how are these expressed in the “real” clinical world? And what can the clinic teach us about the human brain? This practical course enables students to personally experience clinical practices related to their research (or possible future research). Students may join clinicians in specific practicum, such as the operation room theater, treatments, patient-data analyses, grand rounds, discussions and more. The program will be fitted individually to each student according to his or her own preferences and interests.
76931: Basic Concepts in Dynamics and Stochastic Processes
Prof. Yonatan Loewenstein
An introduction to the field of dynamics and important concepts such as stability, limit cycles and bifurcations.
A basic introduction to stochastic processes, with an emphasis on Poisson and Markov processes. The course aims to introduce and teach methods of analyzing dynamical systems to extract properties such as the existence of fixed points, stability of fixed points, oscillations etc. Introducing stochastic processes and their basic types and properties.
76937 The Biological Basis of Neurodegenerative Diseases
Prof. Hermona Soreq & Dr. Sebastian Kadener
The course is concerned with molecular processes in the brain, and stresses their involvement in neuronal and cognitive processes. Students will learn methods and approaches that can be applied to a broad spectrum of neuroscience topics. Topics include: basic concepts in biochemistry, gene expression and gene expression control by neural activity, mRNA post-transcriptional modifications and their involvement in neural processes, structure and functioning of ion channels and receptors, intracellular signaling pathways, research methods in molecular neurobiology, genetic manipulations on cells and organisms, and the use of mutant animals in neuroscience research.
The field of the study of the cortex is gaining momentum in recent years and is at the forefront of brain research; therefore it is important to provide students with up-to-date knowledge on the state of research in this area.
The course will focus on an in-depth study of and discussions and criticism pertaining to selected articles published in the field, both experimental and theoretical.
The participants will each present and discuss at least one presentation.
76961 Brain, neurons and everything in between*
Dr. Inbal Goshen
The course will deal with the following topics:
1) Intro to cell biology
2);Cell types in the nervous system and their roles
3);Neuronal membrane potential
4) The action potential
5) Single-cell computations
9) Intro to Neuroanatomy
10) Functional neuroanatomy
* For those students who did not take an equivalent course.
Last updated: 19/9/16