Credits: 4 Didactic: Y Academic Year: 2017 Term: 1 Room:

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Page 1 of 5 Theoretical Neuroscience: From Cells to Learning Systems Course: GS-NE-473 Credits: 4 Didactic: Y Academic Year: 2017 Term: 1 Room: Rice TBA Class: TTH, 2:30 PM 3:45 PM COURSE DIRECTOR

1 Page 1 of 5 Theoretical Neuroscience: From Cells to Learning Systems Course: GS-NE-473 Credits: 4 Didactic: Y Academic Year: 2017 Term: 1 Room: Rice TBA Class: TTH, 2:30 PM 3:45 PM COURSE DIRECTOR CONTACT INFORMATION Name: Fabrizio, Ph.D. Office: BCMS-S557 gabbiani@bcm.edu Office Hours: Tuesday and Thursdays 1-2 pm. COURSE DESCRIPTION AND OBJECTIVES: Goal of the course is to introduce the most salient features of neural systems at the biophysical, cellular and systems level, as well as to develop the ability to construct and test mathematical models from basic principles of biophysics. Upon completion of the course, students should be able to: 1. Formulate and solve algebraic equations for the resting state of cells, 2. Formulate and solve differential equations for the dynamic state of cells and their interactions, 3. Use Fourier transforms to describe the response properties of visual neurons, 4. Apply probabilistic models to describe synaptic transmission and behavior, 5. Analyze the responses of networks of neurons and study learning in such networks. Assigned readings for the first 14 lectures are specified in the schedule. There will be an online quiz over each reading prior to the class period where it is needed. Exercises will be pursued in groups of 2 to 4 during class. During the second part of the course, there will be no required readings and quizzes. REQUIRED TEXTS AND MATERIALS: Mathematics for Neuroscientists [MN] (1 st or 2 nd ed.). MATLAB for $99, or Octave, free, or alternatively, Python, free. PREREQUISITE(S) or EXCLUSIONS: None ATTENDANCE REQUIREMENTS: There will be considerable group work and so attendance is crucial. Those with no unexcused absences may drop their 3 lowest quiz scores. HOMEWORK AND EXAMS 27 online quizzes/assignments, two exams and a final project. FINAL PROJECT Graduate students will work on a final project during the second part of the semester. The student will select the project, which can be related to his/her own research or designed to deepen understanding of a specific topic related to neural modeling. Goal of the project is for the student to carry out modeling and/or data analysis using techniques studied in the course and thus show

2 Page 2 of 5 mastery of the course material. The student will submit a brief description of the project (300 words or ½ page) by the middle of October (10/12/17). The student will meet and discuss his/her project with the instructors about 1 month later (11/14/17). Around the end of the semester (12/13/17), the student will turn in his/her project, including a report and the code used to generate the results. The final project report will be typed on US Letter paper, in Arial font, 11 points, 1 inch margins on all sides, single spaced. It will include the following sections: Abstract: a summary of the project and its results, words. Introduction and Background: a review of the relevant literature and/or previous studies placing the project into context. Around words (1-1.5 pages) Methods: a description of the experimental methods used (if the project is based on such data), and the programming code generated by the student for the project. Around words (1-1.5 pages). Results: including graphs generated from the programming code and a narrative explaining them, as well as legends for each of the figures including graphs. Around words (1.5-3 pages). Conclusions/Discussion: summarizing the results and future directions, about words or 1 page. Appendix: Matlab code used to generate the figures. GRADING: Your quiz/assignment average is 50% of your grade. Over the first 14 lectures, weekly assignments will be given that will contribute 50% of each quiz/assignment total. Over the second part of the course, there will be only assignments and no quizzes. 2 exams, each covering 14 lectures. The exams will be pledged 3-hour and take-home. Each exam is worth 20% of your grade. The final project is worth 10% of the final grade. PROFESSIONAL CONDUCT: Students are expected to conduct themselves in a professional manner and abide by all policies of Baylor College of Medicine, the Graduate School of Biomedical Sciences and their Programs. Any conduct not in keeping with the ethical or professional standards of BCM is defined as professional misconduct. Academic misconduct is defined as dishonesty (e.g. cheating, plagiarism, etc.) that occurs in conjunction with academic requirements such as coursework including homework and examinations. GRADE VERIFICATION: Due process involves providing students with a clear description of course expectations, including grading requirements. Students may have questions about their final grade or the grading process. If students want to verify their final grade, they are first encouraged to meet with the course director informally to discuss those questions. After grade verification and discussion, the student may choose to proceed with a formal grade appeal if they believe they have received a grade unjustly. Grievances are not the same as disagreements. A student cannot file a grievance merely because s/he disagrees with the grade. A student can file a grievance if they believe the grade was unfair, for example, if it is felt to be an act of discrimination. Formal grievances can be filed via the Integrity Hotline portal. INTEGRITY HOTLINE: or STUDENT GRIEVANCES POLICY:

3 Page 3 of 5 STUDENT DISABILITY SERVICES: Students with documented disabilities can seek accommodations from Student Disability Services at or to the Student Disability Coordinator, Ms. Mikiba Morehead at mikiba.morehead@bcm.edu. Information about a student s disability will be kept private. The student is responsible for informing the course director of approved accommodations prior to the first examination. COURSE SCHEDULE*: *course starts Term 1 and ends after Term 2 Week/Lecture Topics (references to [MN] 2 nd edition) Instructor Week 1 The Passive Isopotential Cell Numerical Solution of Differential Equations 8/22/17 Nernst Potential, Membrane Conductance, Capacitance, Synaptic Conductance. [MN Chapter 2]. 8/24/17 Differential Equations, Exact and Numerical Solutions. [MN Chapter 3, sections 1, 4, 5]. Week 2 The Active Isopotential Cell 8/29/17 Delayed Rectifier Potassium Channel, Sodium Channel. [MN Chapter 4, sections 1 & 2]. 8/31/17 Hodgkin-Huxley Equations. [MN Chapter 4, section 3]. Week 3 The Quasi-Active Isopotential Cell and the Passive Cable 9/5/17 The Quasi-Active Isopotential Cell, Numerical Methods and Exact Solution. [MN Chapter 5, sections 1-3]. 9/7/17 The Discrete and the Continuous Passive Cable Equation, Numerical and Exact Solutions. [MN Chapter 6, sections 1-4]. Week 4 The Passive and Active Tree 9/12/17 The Passive Dendritic Tree, Differential Equation and Numerical Methods. [MN Chapter 8, section 1-4]. 9/14/17 The Active Cable and Dendritic Tree. [MN Chapter 9, section 1, 3, & 5]. Week 5 Fourier Transforms Early Vision 9/19/17 Fourier Transforms, Discrete and Continuous. [MN Chapter 7]. 9/21/17 Receptive Fields of Retinal Ganglion Cells and Simple Cells in Visual Cortex. [MN Chapter 22, section 2-5, Chapter 23, section 1]. Week 6 Visual Cortex Probabilities 9/26/17 Non-Separable Receptive Fields, Motion Energy Model. [MN Chapter 23, sections 2-5].

4 Page 4 of 5 9/28/17 Basic Probability, Binomial, Poisson and Gaussian Random Variables. [MN Chapter 12, sections 1-7 and 11]. Week 7 Introduction to Signal Detection Theory and Psychophysics 10/3/17 Hypothesis Testing, Ideal Decision Rules, Poisson and Gaussian Models. [MN Chapter 27, sections 1-3]. 10/5/17 Single Photon Detection, Psychophysics, Motion Detection. [Chapter 28, sections 1-3]. Week 8 Recess Synaptic Transmission 10/10/17 Midterm Recess: No Class 10/12/17 Quantal Release Model. [MN Chapter 13, sections 1-5]. ½ page description of Final Project due. Week 9 Synaptic Transmission Synaptic Plasticity and Learning 10/17/17 Short Term Synaptic Plasticity, Receptor Dynamics (Markov models). [MN Chapter 13, sections 6, 7]. 10/19/17 Long Term Synaptic Plasticity, the Basis of Learning and Memory Introduction. Week 10 Synaptic Plasticity Unsupervised Learning 10/24/17 Hebb s and Oja s Rules, Principal Component Analysis, Links to Receptive Field Development. [MN Chapter 16, sections 1-3]. 10/26/17 The Bienenstock-Cooper-Munroe (BCM) Rule, Application to Receptive Field Development. Week 11 Unsupervised Learning Supervised Learning 10/31/17 Objective Function Formulation and Independent Component Analysis. 11/2/17 Perceptron Rule for Linear Neurons. [MN Chapter 30, section 1] Week 12 Supervised Learning Biophysics of Synaptic Plasticity 11/7/17 Multilayered Networks, Backpropagation Learning Rule. 11/9/17 Biophysical Mechanisms of Synaptic Plasticity. Week 13 Biophysics of Synaptic Plasticity Molecular Mechanisms of Synaptic Plasticity 11/14/17 Linear Pair-Based Superposition Models. Discussion of final project with instructors after class. 11/16/17 Modeling Molecular Interactions Underlying Plasticity Models.

5 Page 5 of 5 Week 14 Molecular Mechanisms of Synaptic Plasticity 11/21/17 Calcium Dependent Plasticity Models. [MN Chapter 14, section 5] 11/23/17 Thanksgiving Recess: No Class Week 15 Receptive Fields and Reinforcement Learning 11/20/17 Receptive fields with Calcium Based Models. 11/30/17 Reinforcement Learning. 12/13/17 Final project due at 5 pm.

Neuroscience I. BIOS/PHIL/PSCH 484 MWF 1:00-1:50 Lecture Center F6. Fall credit hours

Neuroscience I. BIOS/PHIL/PSCH 484 MWF 1:00-1:50 Lecture Center F6. Fall credit hours INSTRUCTOR INFORMATION Dr. John Leonard (course coordinator) Neuroscience I BIOS/PHIL/PSCH 484 MWF 1:00-1:50 Lecture Center F6 Fall 2016 3 credit hours leonard@uic.edu Biological Sciences 3055 SEL 312-996-4261