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Computational Neuroscience

Computational Neuroscience

BIO4502e.01, University of Science and Technology of China (Fall 2026)

time: 2(3,4), Week 1-18

location: Room 2307

instructor: Prof. Quan Wen

TA: Qilin Zhang
(office hours: Sat 7-8pm & by appt, LSB 205, West Campus)

four designs

prerequisites: High school knowledge of neuroscience. A good working knowledge of multivariable calculus, probability theory, linear algebra and differential equations. Familiarity of a programming language.

brief description: In this course, we will explore how physics, engineering, and mathematics have shaped our understanding of the brain. In particular, we will investigate the relationship between structure, dynamics, representation, and behavior. Special topics may include wiring optimization in neural circuit, attractor and chaotic dynamics in neural network, sensory and motor representation, biological learning rules, Hopfield network, and hierarchical control of behaviors. We will also discuss connections with modern machine learning methods.

contact:

discussion: Students are encouraged to post discussion on the GitHub issue page.

View older announcements
  • Sep 23, 2026 Recitation: time 2pm room 2307.
  • Sep 4, 2026 Syllabus updated.
  • Aug 29, 2026 Welcome! Course homepage is live.
Lecture Schedule
Week Date Topic Readings Slides Notes Homework
W1 Tue
9/1 		An introduction to computational neuroscience Wang-Gu Model Introduction
W2 Tue
9/8 		Single neuron dynamics and computation Single Neuron Dynamics
W3 Tue
9/15 		Connecting neurons for computation Lec 3.1 Problem Set 1 due 9.29
W4 Tue
9/22 		Perceptron model: capacity and algorithm Lec 3.2
Sun
9/27 		Recitation: time 7(6,7) room 2307 Rec 0
W5 Tue
9/29 		Recurrent neural network I: from weights to dynamics Lec 4.1
W6 Tue
10/6 		No class
Sat
10/10 		Recurrent neural network II: attractor and Hopfield network Attractors in RNN Lec 4.2/3
W7 Tue
10/13 		Recurrent neural network III: KIMI-K3, an introduction to RMT KIMI-K3 Problem Set 2 due 10.19
Sat
10/17 		Recitation: time 6(6,7) room 2307 Review B-P Model MADE Rec 1
W8 Tue
10/20 		Recurrent neural network IV: dynamical mean-field theory Lec 4.4
W9 Tue
10/27 		Recurrent neural network V: low-rank network Lec 4.5
W10 Tue
11/3 		Population Geometry Dimensionality Population Geometry Problem Set 3 due 11.10
W11 Tue
11/10
W12 Tue
11/17
W13 Tue
11/24
W14 Tue
12/1
W15 Tue
12/8
W16 Tue
12/15
W17 Tue
12/22
W18 Tue
12/29
page maintained by Qilin Zhang