Courses

Download Schedule

Larry Abbott, Stefano Fusi, Ashok Litwin-Kumar, Ken Miller

Meetings: Tuesdays, Lectures 12.30 - 2.00pm, Recitations 2.00 - 3.30pm
                  Thursdays, Math Supplements 12.30 - 2.00pm, Lectures 2.00 - 3.30pm

Location: Jerome L Greene Science Center, 5th floor, conference room L5-084

TAs: Ella Batty, Ramin Khajeh, Salomon Muller, Danil Tyulmankov, Marjorie Xie

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

January
22     Math Intro: Differential Equations (Ashok)
24     Electrical Properties of Neurons, Integrate-and-Fire (Larry) - Assign 1 download
          Calculus Review (Dan)
29     Adaptation, Izhikevich Model, Phase-Planes, Stability Analysis (Larry)
          Recitation (Ramin)
30     Assign 1 Due
31     The Hodgkin-Huxley Model (Larry)
          Taylor Series, Linearization (Dan)

February
5      Synapses, Short-Term Plasticity, Release Probability (Stefano) - Assign 2 download
         Recitation (Ramin)
7      Probability / Poisson spiking, Central Limit Theorem/Gaussian (Larry)
         Combinatorics (Dan)
12    Probability / Encoding I (Ken, lecture notes) - Assign 3 download
         Recitation (Ramin)
13    Assign 2 Due
14    Linear Algebra I (Ken)
         Linear Algebra 0 (Dan)
19    Linear Algebra II (Ken) Assign 4
         Recitation (Marjorie)
20    Assign 3 Due
21    Dimensional Reduction I (Ken)
         Linear systems (Dan)
26    Dimensional Reduction II (Ashok) - Assign 5 download
         Recitation (Marjorie)
27    Assign 4 Due
28    COSYNE

March
5      COSYNE
         Recitation (Marjorie)
7      Receptive Fields, GLMs, and Maximum Likelihood (Ashok) 
         Convolution (Dan)      
12    Spike and Rate Networks I (Larry)
         Recitation (Marjorie)
13    Assign 5 Due
14    Spike and Rate Networks II (Stefano) - Assign 6 download
         Fourier transforms (Dan)      
19    Spring Break
21    Spring Break
26    E/I Networks I (Ken)
         Recitation (Salomon)
28    E/I Networks II (Ken) - Assign 7 download
         Multivariable calculus (Dan)

April
2      Decision-Making Networks (Stefano)
        Recitation (Salomon)
3      Assign 6 Due
4      Recurrent Networks - Hopfield and Random (Larry) - Assign 8 download
        Multivariable calculus continued (Dan) 
9     Optimization I (Ashok)
        Recitation (Salomon)
10   Assign 7 due
11   Optimization II (Ashok) - Assign 9 download
        Lagrange multipliers (Dan) 
16    Perceptron and Decoding (Stefano)
         Recitation (Ella)
17    Assign 8 Due
18    Learning in Recurrent Networks (Larry)
        Probability (Dan)
23    Multilayer Perceptrons (Ashok) - Assign 10 download
         Recitation (Ella)
24    Assign 9 Due
25    Deep Learning I (Stefano)
        Probability continued (Dan)
30    Deep Learning II (Stefano)
        Recitation (Ella)

May
1      Assign 10 Due
2     Reinforcement Learning (Stefano) - Assign 11
        Information Theory (Dan)
7      Recitation (Ella)
8      Assign 11 Due

Meetings: Wednesdays, 10.15 - 11.45am

LocationJerome L Greene Science Center, 6th floor room L6-087

Encoding and Decoding
Jan 23     Intro (Fabio Stefanini)
Jan 30     Linear & Logistic Regression (Ramon Nogueira, Josh Glaser)
Feb  6     GLM & Nonlinear Regression (Ramon Nogueira, Josh Glaser)
Feb 13     Static Models for Dimensionality Reduction (Matt Whiteway)
Feb 20     Dynamic Models for Dimensionality Reduction (Shreya Saxena)
Feb 27     Cosyne
Mar  6     Cosyne
Mar 13     Hackathon Encoding & Decoding

Mechanistic Models of Neural Circuits
Mar 20     Intro. From Spiking Models to Population Rate Part I (Laureline Logiaco)
Mar 27     From Spiking Models to Population Rate Part II (Laureline Logiaco)
Apr  3     Inhibition Stabilized Networks & Supralinear Stabilized Networks (Mario Dipoppa)
Apr 10     Neural Variability in Rate Models, Appropriateness Spiking vs. Rate Models (Mario Dipoppa)
Apr 17     Equilibrium Theory for Hopfield Model (Alessandro Ingrosso)
Apr 24     Dynamic Mean Field Theory of Chaotic Rate Networks (Alessandro Ingrosso)

Learning in Recurrent Networks
May  1     Intro. Learning in Recurrent Networks, Backprop Through Time (Rainer Engelken)
May  8     Least-Squares, RLS, FORCE, Kalman Filters (James Murray)
May 15     Dynamic Mean Field Theory for Low-Rank Static Solutions (Rainer Engelken)
May 22     Control Theory for Low-Rank Static Solutions (James Murray)
May 29     Bonus