Instructors: Dr. Nina Miolane & Dr. Fatih Dinc
Affiliation: Geometric Intelligence Lab - UC Santa Barbara (https://gi.ece.ucsb.edu)
Quarter: Winter 2025
Format: 2 Lectures weekly + office hours
Prerequisites: Mathematical maturity with linear algebra, differential equations, and probability, and ability to code with Python.
Class webpage: You are there!
Office hours: TBD.
Welcome to ECE 594N, Winter 2025! This is a 10-week-long introductory course for researchers from various backgrounds (EE, CS, physics, applied mathematics, neuroscience, and biology) who wish to gain hands-on experience in computational neuroscience. The main premise is to learn about the latest computational tools from geometry and dynamical systems that allow studying computations performed by artificial and biological neural networks. There are three modules:
Module I provides a historical view of population codes, starting from static decoding approaches of earlier decades to the modeling of dynamics and geometry of the neural code.
Module II motivates and introduces the theoretical basis of the latent computation framework (LCF), a theory of neural computation that explains several empirically observed phenomena.
Module III discusses further applications to a broader set of open research directions in artificial and biological neural networks.
There will be one take-home midterm (30%), which will cover the basic topics taught in module I and potentially some fundamental topics from module II.
The main assessment component of the course is the final project (40% for the poster, 20% for the reviews; see the section ‘Our mutual agreements’ below), which can be either
(preferred) a research project on a novel topic broadly relevant to the machine learning community and involves some form of dynamics, geometry, or neural computation, or
(if desired) a review article of a particular subtopic in line with the topics of the class.
Active participation in class discussions (10%).
We expect everyone to have a final project assignment before the end of week 4 (January 30th, 5pm PT), preferably in collaborative groups of 3 or 4 students. You are welcome to discuss your project ideas with the instructors and involve them in the project, or completely leave them out and collaborate with your favorite group. The only rule is that the collaboration has to be newly formed and be in an exploratory spirit. We will also ask labs around the campus to participate and discuss potential project proposals as part of the course. There will be candidate project ideas posted. More on this later, see the website for regular updates.
Class participation will be computed following the formula: 15 - max(# of days missed, 5). Basically, you get 5 free days, no questions asked. Please use them wisely and do not ask for exceptions. What is being taught in class is not available in a textbook, so try to be actively present! You will self-report your participation grade, we will not keep track of your presence.
The midterm exam will be assigned by week 2 and due until the end of week 5 (February 6th, 5pm PT). It will be open-ended, and allow you to practice asking and answering research questions. You are welcome to work on your own for the exam, but our strong preference is you work on the exam together with your research group. You can submit one exam per group if preferred, but then a contribution statement has to be present and all members should write they understand and agree with the solutions (no effects on the grade, just an honor code requirement).
Assessments of take-home midterm will be done collaboratively (in week 6, date TBD). The midterm will be assigned weeks in advance and asynchronously, please plan accordingly and do not ask for extensions, as the asynchronous nature should allow accommodating any emergency lasting less than half the quarter. The date assigned for assessment is mandatory for everyone, think of it as the day of the midterm exam.
The final project has four components.
Zeroth component should be a single page description (title, authors, and problem statement + tentative methods) of the proposal (0%, due January 23rd 5pm PT).
In the first component, you are asked to provide a write-up of your results in NeurIPS format (https://neurips.cc/Conferences/2025/CallForPapers). This is due by the end of week 9 (March 6th, 5pm PT) and will be graded based on a rubric by the instructors (20%).
The second component is poster presentation, which will take place in the week of final exams in a designated spot. The poster PDFs are due the morning of the presentation. The grading will be done by instructors using a rubric (20%).
The final component is the reviewer feedback, due by the end of week 10 (March 13th, 5pm PT). Each student will provide feedback to at least two other writeups. The quality of the feedback will be graded by the recipients of the feedback and the instructors jointly, also based on a rubric (20%).
All rubrics, midterm or final, will be shared with you beforehand. The goal standard for the midterm exam is the demonstration of the independent and collaborative critical thinking ability, whereas for the final project, it is a submission to the NeurIPS 2026 conference
AI usage policy: No AI assistance is allowed in the midterm exam or when providing reviews to your fellow classmate’s final project, but it is allowed for any other component of the class. For instance, you can use AI to understand a paper written by your classmate or assist in writing your own paper, but you cannot use AI to suggest how a paper you are assigned to should be reviewed (e.g., summary should be your own words, and similarly for weaknesses or strengths etc.). Any work turned in as part of the class, apart from the reviews and the midterm where it is prohibited, should have a single comprehensive paragraph describing how AI was used.
Collaboration and intellectual exchange are integral parts of this course. Students are encouraged to study and discuss course materials together. However, all submitted work must accurately reflect the individual or collective effort of those credited. When submitting any assignment, students must explicitly acknowledge all forms of assistance or collaboration received from classmates, instructors, or external sources. There is no restriction on the length of the acknowledgment section, and it will not influence grading in any manner. Failure to acknowledge assistance constitutes a violation of the Honor Code. Academic integrity requires full transparency in all submitted work. Authorship on a final course project does not automatically confer authorship on any subsequent publication derived from that work. Authorship decisions for publications shall be made collectively by all contributors, in accordance with established academic standards for contribution and responsibility. Disputes regarding authorship are outside the scope of this course. Instructors will not intervene in such matters unless they are themselves coauthors, in which case the same contribution-based principles apply. If instructors or external advisors provide supervision or intellectual input on a project, students bear the responsibility of appropriately involving or notifying them in any subsequent dissemination or submission of the work.
Artificial intelligence tools were used in the preparation of this document to assist with language editing, refinement of paragraph flow, and organization of course topics. The substantive content, structure, and intellectual contributions of the course materials were created by the instructors and represent their original work.