Virtual classes and recitations
Faculty: Larry Abbott, Stefano Fusi, Ashok Litwin Kumar, Ken Miller
TAs: Matteo Alleman, Dan Biderman, Salomon Muller, Amin Nejatbakhsh, Marjorie Xie
Meetings: Tuesdays & Thursdays, JLGSC L5-084, Lecture 2.00 - 3.30pm
Text: Theoretical Neuroscience by P. Dayan and L.F. Abbott (MIT Press)
January
21 (Larry) Introduction to the Course and to Theoretical Neuroscience
23 (Larry) Mathematics Review: Notes
28 (Larry) Electrical Properties of Neurons, Integrate-and-Fire Model (Assignment 1, neuron models)
30 (Larry) Adaptation, Synapses, Spiking Networks (Numerical methods)
February
4 (Larry) Numerical Methods, Filtering (Assignment 2)
5 Assignment 1 Due
6 (Larry) The Hodgkin-Huxley Model (I&F Model, White Noise, Synapses-Networks)
11 (Larry) Types of Neuron Models and Networks (Assignment 3, Poisson Spiking, Networks)
12 Assignment 2 Due
13 (Ashok) Linear Algebra I (Notes)
18 (Ashok) Linear Algebra II (Notes, Assignment 4, Solutions)
19 Assignment 3 Due
20 (Ashok) Introduction to Probability, Encoding, Decoding (Notes)
25 (Ashok) GLMs (Notes, Assignment 5)
26 Assignment 4 Due
27 COSYNE
March
3 COSYNE
5 (Ashok) Decoding, Fisher Information I (Notes)
10 (Ashok) Canceled
12 (Ashok) Information Theory (Notes, Assignment 6, google-1000-english.txt, Recitation Notes)
14 Assignment 5 Due
17 Spring Break
19 Spring Break
24 (Ken) Canceled – PCA and Dimensionality Reduction I
26 (Ken) – PCA and Dimensionality Reduction II (Notes)
27 Assignment 6 Due
31 (Ken) – Rate Networks/E-I networks I (Notes, Assignment 7, Codes)
April
2 (Ken) – Rate Networks/E-I networks II (Notes)
7 (Ken) – Unsupervised/Hebbian Learning, Developmental Models (Notes, Assignment 8, Ring-Model)
8 Assignment 7 Due
9 (Ken) – Optimization (Notes)
14 (Ken) – Optimization II (Notes, Assignment 9, Assignment 9 Addendum, Code, Gaussian Problem, Note on Lyapunov functions, Review of simple developmental models, News & Views on feature-map approach, MacKay and Miller, 1990, Miller and MacKay, 1994)
15 Assignment 8 Due
16 (Ashok) Optimization (Notes)
21 (Stefano) Perceptron (Notes, Assignment 10)
23 (Stefano) Multilayer Perceptrons and Mixed Selectivity (Notes)
28 (Stefano) – Deep Learning I (backpropagation) (Assignment 11, Notes, Codes - please note more codes are available on courseworks)
30 (Stefano) – Deep Learning II (convolutional networks) (Notes, Visualizing and Understanding Convolutional Networks, YaminsDiCarlo)
May
1 Assignment 9 & 10 Due
5 (Stefano) Learning in Recurrent Networks (Notes)
7 (Stefano) Continual Learning and Catastrophic Forgetting (Notes)
12 (Stefano) Reinforcement Learning (Notes)14 Research Topic
15 Assignment 11 Due
Meetings: Wednesdays, 10.15 - 11.45am
Location – Jerome L Greene Science Center, L6-087
See course website for updated schedule
Control Theory and Reinforcement Learning
Jan 29 Examples of control theory in neuroscience (Bettina Hein, Laureline Logiaco)
Feb 5 Methods in control theory (Bettina Hein, Laureline Logiaco)
Feb 12 Reinforcement learning (James Murray)
Feb 19 From optimal control theory to reinforcement learning (Samuel Muscinelli)
Feb 26 Cosyne break
Mar 4 Cosyne break
Latent Variable Models
Mar 11 Hidden Markov Models (Matt Whiteway)
Mar 18 Latent Dynamical Systems (Josh Glaser)
Mar 25 Gaussian Processes (Rainer Engelken)
Apr 1 Modeling of Behavior (Juri Minxha)
Apr 8 Hackathon on Latent Variable Models
Miscellaneous
Apr 15 Phenomenological Renormalization Group (Serena Di Santo)
Apr 22 Additional and diverse perspectives in neuroscience (Laureline Logiaco, Matt Whiteway, Juri Minxha)
Apr 29 Introduction to replica theory (SueYeon Chung)
May 6 Classification and Geometry of General Perceptual Manifolds (SueYeon Chung)
Mathematical Tools for Theoretical Neuroscience (NBHV GU4359)
Faculty: Ken Miller (kdm2103@columbia.edu)
Instructor: Dan Tyulmankov (dt2586@columbia.edu)
Teaching Assistant: Amin Nejatbakhsh (mn2822@columbia.edu)
Time: Tuesdays, Thursdays 8:40a-10:00a
Place: JLGSC L5-084
Webpage: https://ctn.zuckermaninstitute.columbia.edu/courses
Credits: 3 credits, pass/fail only
Description: An introduction to mathematical concepts used in theoretical neuroscience aimed to give a minimal requisite background for NBHV G4360, Introduction to Theoretical Neuroscience. The target audience is students with limited mathematical background who are interested in rapidly acquiring the vocabulary and basic mathematical skills for studying theoretical neuroscience, or who wish to gain a deeper exposure to mathematical concepts than offered by NBHV G4360. Topics include single- and multivariable calculus, linear algebra, differential equations, dynamical systems, and probability. Examples and applications are drawn primarily from theoretical and computational neuroscience.
Prerequisites: Basic prior exposure to trigonometry, calculus, and vector/matrix operations at the high school level
Grading: 100% attendance-based
Readings and exercises: Lecture notes and optional practice exercises will be provided for each lecture, and supplementary readings will be assigned from various textbooks.