Intro to Theory

Computational Neuroscience, Fall 2021

Larry Abbott, Ken Miller, Ashok Litwin Kumar, Stefano Fusi, Sean Escola

TAs: Elom Amematsro, Ramin Khajeh, Minni Sun, Denis Turcu

Meetings: Tuesdays & Thursdays 2:00-3:30

Text - Theoretical Neuroscience by P. Dayan and L.F. Abbott (MIT Press)

Webpage -


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9         (Larry) Introduction to Course and to Computational Neuroscience   

14       (Larry)  Electrical Properties of Neurons, Integrate-and-Fire Model

16       (Stefano) Adaptation, Synapses, Synaptic Plasticity

21       (Larry) The Hodgkin-Huxley Model (Assignment 1)

23       (Larry) Types of Neuron Models and Networks

28       (Larry) Numerical Methods, Filtering (Assignment 2)       

29        Assignment 1 Due

30       (Sean) Generalized Linear Models


5         (Ken) Linear Algebra (Assignment 3)

6          Assignment 2 Due

7         (Ken) PCA and Dimensionality Reduction

12       (Ken) Rate Networks/E-I networks I (Assignment 4)

13        Assignment 3 Due

14       (Ken) Rate Networks/E-I networks II

19       (Ken) Unsupervised/Hebbian Learning, Developmental Models (Assignment 5)

20        Assignment 4 Due

21       (Ashok) Introduction to Probability, Encoding, Decoding

26       (Ashok) Decoding, Fisher Information I

27        Assignment 5 Due

28        (Ashok) Decoding, Fisher Information II (Assignment 6)


2          Holiday 

4         (Ashok) Information Theory

9         (Ashok) Optimization I (Assignment 7)

10       Assignment 6 Due

11       (Ashok) Optimization II

16       (Stefano) The Perceptron (Assignment 8)

17       Assignment 7 Due

18       (Stefano) Multilayer Perceptrons and Mixed Selectivity

23       Holiday

25       Holiday

30      (Stefano) Deep Learning (Assignment 8)        


1         Assignment 8 Due

2        (Sean) Learning in Recurrent Networks

7        (Stefano) Continual Learning and Catastrophic Forgetting (Assignment 10)

8        Assignment 9 Due

9        (Stefano) Reinforcement Learning

15      Assignment 10 Due


Introduction to Theoretical Neuroscience (Spring 2021)

Introduction to Theoretical Neuroscience (Spring 2020)

Introduction to Theoretical Neuroscience (Spring 2019)

Introduction to Theoretical Neuroscience (Spring 2018)

Statistical analysis of neural data

Statistical analysis of neural data (GR8201), Fall 2021

This is a Ph.D.-level topics course in statistical analysis of neural data. Students from statistics, neuroscience, and engineering are all welcome to attend. A link to the last iteration of this course is here.

Time: F 10-11:30
Place: JLG L8-084
ProfessorLiam Paninski; Office: Zoom. Email: [email protected]. Hours by appointment.

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Prerequisite: A good working knowledge of basic statistical concepts (likelihood, Bayes' rule, Poisson processes, Markov chains, Gaussian random vectors), including especially linear-algebraic concepts related to regression and principal components analysis, is necessary. No previous experience with neural data is required.

Evaluation: Final grades will be based on class participation and a student project. Additional informal exercises will be suggested, but not required. The project can involve either the implementation and justification of a novel analysis technique, or a standard analysis applied to a novel data set. Students can work in pairs or alone (if you work in pairs, of course, the project has to be twice as impressive). See this page for some links to available datasets; or talk to other students in the class, many of whom have collected their own datasets.

Course goals: We will introduce a number of advanced statistical techniques relevant in neuroscience. Each technique will be illustrated via application to problems in neuroscience. The focus will be on the analysis of single and multiple spike train and calcium imaging data, with a few applications to analyzing intracellular voltage and dendritic imaging data. Note that this class will not focus on MRI or EEG data. A brief list of statistical concepts and corresponding neuroscience applications is below.

Mathematical Tools

Mathematical Tools for Theoretical Neuroscience (NBHV GU4359)

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Spring 2021

Lecturer: Dan Tyulmankov ([email protected])
Faculty contact: Prof. Ken Miller ([email protected])*

Time: Tuesdays, Thursdays 11:40a-12:55p
Place: Zoom
Webpage: CourseWorks (announcements, assignments, readings) and Piazza (Q&A, discussion)
Credits: 3 *(Please contact Prof. Miller to sign add/drop forms and other items which require faculty permission)

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. 


  • Undergraduate and graduate students: Must register** on SSOL and on Piazza
  • All others: Please fill out this form and register on Piazza

(**If you’re only interested in attending a subset of lectures, register anyways and contact Dan) 

Prerequisites: Basic prior exposure to trigonometry, calculus, and vector/matrix operations at the high school level


Mathematical Tools for Theoretical Neuroscience (Spring 2020)

Advanced Theory

Special Virtual Edition, Summer/Fall 2020

Meetings: Wednesdays, 10.00 am 

Location: Zoom, contact [email protected] for details


7/15/2020     Time-dependent mean-field theory for mathematical streetfighters (Rainer Engelken)

7/22/2020     Predictive coding in balanced neural networks with noise, chaos, and delays, Article (Everyone) 

7/29/2020     A solution to the learning dilemma for recurrent networks of spiking neurons, Article (Everyone) 

8/5/2020       Canceled             

8/12/2020     Artificial neural networks for neuroscientists: A primer, Article (Robert Yang)

8/19/2020     A mechanism for generating modular activity in the cerebral cortex (Bettina Hein)

8/26/2020     Dynamic representations in networked neural systems, Article (Kaushik Lakshminarasimhan) 

9/2/2020       Network principles predict motor cortex population activity across movement speeds (Shreya Saxena)

9/9/2020       Modeling neurophysiological mechanisms of language production (Serena Di Santo)

9/16/2020     How single rate unit properties shape chaotic dynamics and signal transmission in random neural networks, Article (Samuel Muscinelli)

9/23/2020     Shaping dynamics with multiple populations in low-rank recurrent networks, Article (Laureline Logiaco)

9/30/2020     Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking, Article (Anqi Wu)

10/7/2020     Decoding and mixed selectivity, Article, Article, Article (Fabio Stefanini)

10/14/2020    Theory of gating in recurrent neural networks, Article, Article (Kamesh Krishnamurthy)

10/21/2020    Decentralized motion inference and registration of neuropixel data (Erdem Varol)

10/28/2020    Decision, interrupted (NaYoung So)

11/4/2020      Abstract rules implemented via neural dynamics (Kenny Kay)

11/11/2020     Canceled for Holiday

11/18/2020     "Rodent paradigms for the study of volition (free will)" (Cat Mitelut)

11/25/2020      Gaussian process inference (Geoff Pleiss)

12/2/2020        Canceled

12/9/2020        Structure and variability of optogenetic responses in multiple cell-type cortical circuits (Agostina Palmigiano)

12/16/2020       Manifold GPLVMs for discovering non-Euclidean latent structure in neural data, Article (Josh Glaser)


Advanced Theory Seminar (Spring 2020) website
Advanced Theory Seminar (Spring 2019) website

Page Last Updated: 9/16/2021