Introduction to Theoretical Neuroscience, Spring 2020

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Spring 2020

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 1neuron 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 ModelWhite NoiseSynapses-Networks)
11      (Larry) Types of Neuron Models and Networks (Assignment 3Poisson SpikingNetworks)
12      Assignment 2 Due
13      (Ashok) Linear Algebra I (Notes)
18      (Ashok) Linear Algebra II (NotesAssignment 4Solutions)
19      Assignment 3 Due
20      (Ashok) Introduction to Probability, Encoding, Decoding (Notes)
25      (Ashok) GLMs (NotesAssignment 5)
26      Assignment 4 Due
27      COSYNE

March
3       COSYNE
5       (Ashok) Decoding, Fisher Information I (Notes)
10     (Ashok) Canceled
12     (Ashok) Information Theory (NotesAssignment 6google-1000-english.txtRecitation 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 (NotesAssignment 7Codes)

April
2       (Ken) – Rate Networks/E-I networks II (Notes)
7       (Ken) – Unsupervised/Hebbian Learning, Developmental Models (NotesAssignment 8Ring-Model)     
8       Assignment 7 Due
9       (Ken) – Optimization (Notes)
14     (Ken) – Optimization II (NotesAssignment 9Assignment 9 AddendumCodeGaussian ProblemNote on Lyapunov functionsReview of simple developmental modelsNews & Views on feature-map approachMacKay and Miller, 1990Miller and MacKay, 1994)
15     Assignment 8 Due
16     (Ashok) Optimization (Notes)
21     (Stefano) Perceptron (NotesAssignment 10)
23     (Stefano) Multilayer Perceptrons and Mixed Selectivity (Notes)
28     (Stefano) – Deep Learning I (backpropagation) (Assignment 11NotesCodes - please note more codes are available on courseworks)
30     (Stefano) – Deep Learning II (convolutional networks) (NotesVisualizing and Understanding Convolutional NetworksYaminsDiCarlo)

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