IDS 5741 Modeling neuronal systems, fall 2021
schedule: Synchronous Zoom Wednesdays 2:00 – 4:50
brief description: A study in cognitive computational neuroscience, deep learning, and how the felds are ripe for convergence.
This course will meet synchronously at 2pm via Zoom on Wednesdays, unless otherwise noted. Lectures will be recorded, and uploaded to webcourses for students a few days after class. Please see covid-19 statement below.
**** note: this syllabus represents a tentative schedule of course topics, assignments, and due dates. All scheduling is subject to change. Please refer to webcourses for updates and announcements****
contact information
instructor: PK Douglas, PhD email: [email protected] office: partnership 2 email is the most reliable way to contact me.
student learning objectives
• Appreciate how neurons represent stimuli and communicate amongst neural ensembles • Learn methods for modeling neuronal dynamics including deep learning • Evaluate recent literature at the intersection of neuroscience and machine learning • Examine parallels between artificial and biological computation
course description
This course first provides an overview of key concepts in neuroscience taking a bottom up approach starting with microscopic and moving through mesoscopic and systems level computing in the human brain. We will cover key methods for measuring functional and dynamic activity in the human brain noninvasively (e.g., EEG, fMRI). Recently, deep learning neural networks have become an indispensable tool for brain computational modeling. We will then cover the foundations of deep learning and active inference models while examining the key parallels between artificial & biological neural networks.
required texts & software
• There is no official required text. I will post readings on webcourses. Many background readings will come from the Deep Learning Book, which is freely available online. • See Webcourses for Open Source software resources and tutorial links that may be useful for the final project. The calendar is color coded according to the following scheme:
Unit 1: Neuroscience (single neuron level) Unit 2: Neural Ensembles and representational codes Unit 3: Intro to Deep Learning & Parallels with Neuroscience Unit 4: Autoencoders and Bayesian Models
Date Topics Reading / Assignments Mode (*subject to change)
Week 1 • Course overview • Syllabus / Math Assessment Quiz Synchronous • The importance for cross pollination • Post introduction of yourself between neuroscience and deep learning • Examples of Final Projects
Week 2 • Ion Channels & Synapses • Read Posted Neuroscience course Synchronous • Hodgkin Huxley gating material • Action Potentials • Select Paper to Present
Week 3 • The Neuron as an electric circuit • Quiz: Ion Channels & Plasticity Synchronous • Cable Theory • Read Posted Neuroscience Material • Plasticity & Hebbian Learning
Week 4 • Neural Codes (in Ensembles) • Read article & add response in online Synchronous • Origins of EEG / MEG signals discussion • Read Buszaki & DeNeve articles • Read Deep Learning Book Chapter 15.1
Week 5 • Intro to Functional Neuroanatomy • Quiz - Neuroanatomy & MRI Synchronous • MRI, fMRI, DTI • Read one of the posted articles & add • Functional & Efective Connectivity summary/response in online discussion
• PCA, ICA & Brain Networks • See reading list for papers & Post in Synchronous Week 6 • Graph theory discussion forum
Week 7 • Take Home Midterm • No Class Meeting
Week 8 • Intro to Deep Learning • Read Deep Learning Book Ch. 2-5 Synchronous • Key concepts from machine learning • ICA Quiz that are still relevant for modern algorithms
Week 9 • Deep Learning Continued • Read Deep Learning Book -Chapter 6 Synchronous • Is Backprop biologically plausible? What about Dynamic Routing?
Week 10 • Convolutional NNs & the Human visual • Vision Quiz Synchronous system • Chapter 9, Deep Learning book & • Transfer learning: how similar are Post in discussion forum representations across sensorial modalities? Week 11 • Explainable AI • Quiz - Intro to deep learning and what Synchronous • What is the credit assignment is biologically plausible? problem? • Read remainder of Ch. 15 Deep Learning Book
• The Hippocampus: Memory, • See reading list for papers & Post in Synchronous Week 12 Navigation, Dreams (replay) discussion forum • The importance of noise in avoiding catastrophic inference
Week 13 • Neuromodulators: Dopamine, • See reading list for papers & Post in Synchronous Norepinephrine & representing discussion forum uncertainty • Reinforcement Learning and the basal ganglia
Week 14 Thanksgiving Holiday!! • NO CLASS Happy thanksgiving
Week 15 • Autoencoders, Variatitonal Bayes, and • See reading list for papers & Post in Synchronous Active Inference discussion forum
Week 16 • EXAM WEEK Winter Break! • Final Project Presentations (same day as class according to UCF Final Exam schedule) paper presentation Each person is asked to present one paper from the reading list. Each year I update the reading list to represent a sampling of the latest advancements in neuroscience and machine learning. You will sign up to present this paper on the date that this paper complements the lecture topic. For example, the purely neuroscience papers tend to be assigned to the first few lectures in the semester. For these presentations, you are expected to guide the class through the published paper by describing the introduction, methods, figures, and discussion using slides. You can choose to present one of the papers that I have chosen, or you may select your own paper relevant to the topic, pending instructor approval. Sign ups for dates & paper topics will take place in Weeks 2-3. midterm - (written, take home) The midterm exam will be a take home exam that covers the neuroscience and modeling topics up until that point in the class. There are multiple exam forms and students are expected to do their own unique work. quizzes, discussion forum All discussion forums and quizzes are worth 5 points each. If you are not presenting a specific paper, you are asked to post a brief summary or comment on the paper for that week on webcourses. I will drop the lowest grade from these categories. final exam or final project You have the freedom to select either a final take home exam, or a final project. Final projects are encouraged and are somewhat freeform. These can be computational based (e.g., a deep learning project), neuroscience related (e.g., modeling neuronal data), or a combination of both (e.g., transfer learning applied to neural representations). Examples of successful final projects will be presented on the first day of class. grading Each category gets the following weight:
• Quizzes & Discussion Forum (20%) • Paper Presentation (20%) • Take Home Midterm (25%) • Final Exam or Final Project (25%) • Participation (10%)
Letter grading follows the traditional scale:
Letter grade Points
A 93-100 A- 90-92
B+ 87-89
B 83-86
B- 80-82
C+ 77-79
C 73-76
C- 70-72
D+ 67-69
D 63-66
D- 60-62
F 59 and below
changes & announcements It is possible that adjustments to the schedule may be made during the course. In the event that anything in this syllabus changes (e.g. classroom moves, changes in due dates, contact information), I will use a broad announcement so that all students will be informed immediately. It is critically important that you set your webcourse announcements so that you receive all notifications and be sure to check the class website regularly. attendance policy I do not officially take attendance. However, in class discussions will contribute towards your participation score, and I will randomly assign creative problem sets in class that will be collected during class, and may count towards attendance and/or extra credit.
The Undergraduate Catalog states, “Reasons for acceptable absences may include illness, serious family emergencies, special curricular requirements (e.g., judging trips, field trips, professional conferences), military obligations, severe weather conditions, and religious holidays.” make-up exams and assignments Per university policy, you are allowed to submit make-up work (or an equivalent, alternate assignment) for authorized university-sponsored activities, religious observances, or legal obligations (such as jury duty). If this participation conflicts with your course assignments, I will offer a reasonable opportunity for you to complete missed assignments and/or exams. The make-up assignment and grading scale will be equivalent to the missed assignment and its grading scale. In the case of an authorized university activity, it is your responsibility to show me a signed copy of the Program Verification Form for which you will be absent, prior to the class in which the absence occurs. In any of these cases, please contact me ahead of time to notify me of upcoming needs. university services & resources academic services and resources A list of available academic support and learning services is available at UCF Student Services. Click on "Academic Support and Learning Services" on the right-hand side to filter. non-academic services and resources A list of non-academic support and services is also available at UCF Student Services. Click on "Support" on the right-hand side to filter. If you are a UCF Online student, please consult the UCF Online Student Guidelines for more information about your access to non-academic services. policy statements academic integrity Students should familiarize themselves with UCF’s Rules of Conduct. According to Section 1, "Academic Misconduct," students are prohibited from engaging in: • Unauthorized assistance: Using or attempting to use unauthorized materials, information or study aids in any academic exercise unless specifically authorized by the instructor of record. The unauthorized possession of examination or course-related material also constitutes cheating. • Communication to another through written, visual, electronic, or oral means: The presentation of material which has not been studied or learned, but rather was obtained through someone else’s efforts and used as part of an examination, course assignment, or project. • Commercial Use of Academic Material: Selling of course material to another person, student, and/or uploading course material to a third-party vendor without authorization or without the express written permission of the university and the instructor. Course materials include but are not limited to class notes, Instructor’s PowerPoints, course syllabi, tests, quizzes, labs, instruction sheets, homework, study guides, handouts, etc. • Falsifying or misrepresenting the student’s own academic work. • Plagiarism: Using or appropriating another’s work without any indication of the source, thereby attempting to convey the impression that such work is the student’s own. • Multiple Submissions: Submitting the same academic work for credit more than once without the express written permission of the instructor. • Helping another violate academic behavior standards.
For more information about Academic Integrity, students may consult The Center for Academic Integrity. For more information about plagiarism and misuse of sources, see “Defining and Avoiding Plagiarism: The WPA Statement on Best Practices”. responses to academic dishonesty, plagiarism, or cheating Students should also familiarize themselves with the procedures for academic misconduct in UCF’s student handbook, The Golden Rule. UCF faculty members have a responsibility for students’ education and the value of a UCF degree, and so seek to prevent unethical behavior and when necessary respond to academic misconduct. Penalties can include a failing grade in an assignment or in the course, suspension or expulsion from the university, and/or a "Z Designation" on a student’s official transcript indicating academic dishonesty, where the final grade for this course will be preceded by the letter Z. For more information about the Z Designation, see http://goldenrule.sdes.ucf.edu/zgrade. course accessibility statement The University is committed to providing reasonable accommodations for all persons with disabilities. This syllabus is available in alternate formats upon request. Students with disabilities who need accommodations in this course must contact the professor at the beginning of the semester to discuss needed accommodations. No accommodations will be provided until the student has met with the professor to request accommodations. Students who need accommodations must be registered with Student Accessibility Services, Ferrell Commons, 7F, Room 185, phone (407) 823-2371, TTY/TDD only phone (407) 823-2116, before requesting accommodations from the professor.
campus safety statement Emergencies on campus are rare, but if one should arise during class, everyone needs to work together. Students should be aware of their surroundings and familiar with some basic safety and security concepts. • In case of an emergency, dial 911 for assistance. • Every UCF classroom contains an emergency procedure guide posted on a wall near the door. Students should make a note of the guide’s physical location and review the online version at
Covid-19 Policy Updates The University continues to update its policy as the pandemic situation evolves. Please refer to the university announcements periodically for updates (https://www.ucf.edu/coronavirus/an-update-on-fall-plans-and- precautions/).
Notifications in Case of Changes to Course Modality Depending on the course of the pandemic during the semester, the university may make changes to the way classes are offered. If that happens, please look for announcements or messages in Webcourses@UCF or Knights email about changes specific to this course.
COVID-19 and Illness Notification Students who believe they may have a COVID-19 diagnosis should contact UCF Student Health Services (407-823-2509) so proper contact tracing procedures can take place. Students should not come to campus if they are ill, are experiencing any symptoms of COVID-19, have tested positive for COVID, or if anyone living in their residence has tested positive or is sick with COVID-19 symptoms. CDC guidance for COVID-19 symptoms is located here: (https://www.cdc.gov/coronavirus/2019-ncov/symptoms- testing/symptoms.html) Students should contact their instructor(s) as soon as possible if they miss class for any illness reason to discuss reasonable adjustments that might need to be made. When possible, students should contact their instructor(s) before missing class.
In Case of Faculty Illness If the instructor falls ill during the semester, there may be changes to this course, including having a backup instructor take over the course. Please look for announcements or mail in Webcourses@UCF or Knights email for any alterations to this course.
Course Accessibility and Disability COVID-19 Supplemental Statement Accommodations may need to be added or adjusted should this course shift from an on-campus to a remote format. Students with disabilities should speak with their instructor and should contact [email protected] to discuss specific accommodations for this or other courses.