09/01

Introduction

09/06

Neuroscience Basics
Readings:
1. Basic components of a neuron
2. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Section 1.1)
Optional Readings:
1. How does a neuron maintain its membrane potential, type of channels, Na+, K+ pumps, etc.
2. Chapter 19 - Information Processing in Neural Networks: From Molecules to Networks (until the Encoding and Decoding section)

09/08

Neuron models I: Integrate & Fire model:
Video:
1. Math Tools for Neuro: Video 5.2: Dynamical Systems & Differential Equations (From 0:00 - 18:35)
Readings:
1. Neuronal Dynamics book (Until section 1.3.3)
Simulator Playground (Above the heading, Primer Biology of the neuron, select integrate and fire model from the dropdown and play around with the parameters)

09/13

Neuron models II: Izhikevich model: Guest lecture by Dr. Zhen Chen
1. Video 5.1: Intro to Dynamical Systems
2. Math Tools for Neuro: Video 5.2: Dynamical Systems & Differential Equations (From 18:35 - 25:30)
3. Simple Model of Spiking Neurons (PAPER)
4. Simulator Playground (Above the heading, Primer Biology of the neuron, select 'simple model' from the dropdown and play around with the parameters)
Optional:
1. Izhikevich’s MATLAB Code

09/15

Encoding I: Tuning curves
Readings:
1. 2016_SfN_Short_Course.pdf (Book Page numbers: 11-19) Optional:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf , Section 1.2

09/20

Encoding II: Turning curve, Reverse correlation
Readings:
1. Schwartz et al, Spike-triggered neural characterization

09/22

Encoding III: RGCs, Simple & Complex cells, Reverse correlation
Optional:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf , (Section 2.4 - 2.8)
Specific to Assignment 2:
1. Deriving Poisson from Binomial Distribution with low success probability
2. Poisson and Exponential distribution

09/27

Encoding IV: GLM
Readings:
1. Rust et. al 2005
Optional:
1. Pillow et. al 2008, Nature
2. PCA tutorial

09/28

09/29

Information Theory
Readings:
1. Kenji Doya, Shin Ishii, Alexandre Pouget, Rajesh P. N. Rao - Bayesian Brain: Probabilistic Approaches to Neural Coding.pdf Read Chapter 1 (focus on 1.3-1.5).
Optional:
1. Cover and Thomas (Chapter 2.1 - 2.5)

10/04

Decoding I: Signal Detection Theory
Readings:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Chapter 3.1 - 3.2)
Optional:
1. Tutorial on Fisher Information (Section 1)

10/06

Decoding II: Population decoding, Fisher Information
Readings:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Chapter 3.3)
Optional:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Chapter 3.4+)
2. Neural correlations, population coding, and computation (Averbeck et. al 2008)

10/11

10/13

Decoding III: Noise correlations, Choice Probabilities
Video:
1. Notter Lecture, Anne Churchland
Readings:
1. Zohary et. al 1994, Correlated neuronal discharge rate and its implications for psychophysical performance
Optional:
1. Information-limiting correlations, Moreno Beto 2014
2. The Effect of Correlated Variability on the Accuracy of a Population Code, Dayan Abbott 1999
3. The Effect of Noise Correlations in Populations of Diversely Tuned Neurons

10/18

Decision-making I
Readings:
1. Gold and Shadlen 2007: The Neural Basis of Decision Making

10/20

Decision-making I

10/25

Decision-Making III: Recap & Journal Club (Park et al. 2014 in Reading Material)
Readings:
1. Park et. al 2014 Encoding and decoding in parietal cortex during sensorimotor decision-making

10/27

Deep Learning & Brain I: General introduction, Performance optimized networks
Video: But what is a neural network? (3Blue1Brown)
1. Chapter 1, Deep learning
2. Chapter 2, gradient descent
Readings:
1. LeCun et.al 2015, Deep learning
2. Performance-optimized hierarchical models predict neural responses in higher visual cortex, Yamins 2014 PNAS
Optional:
1. Cadena 2019 2. Walker 2019 Nature

11/01

Deep Learning & Brain II: Misc + JC (Lindsey et. al 2019)
Readings:
1. A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs:
Optional:
1. Goal-Driven Recurrent Neural Network Models of the Ventral Visual Stream
2. Kriegeskorte 2009, Relating population-code representations between man, monkey, and computational models

11/03

Deep Learning models of auditory processing: Guest lecture by Prof. Sam Norman-Haignere

11/08

Probabilistic inference I: Intro, Behavior, Baysean Networks
Readings:
1. A Brief Introduction to Graphical Models and Bayesian Networks by Kevin Murphy
Optional:
1. Ernst and Banks 2002
2. Multisensory integration: psychophysics, neurophysiology, and computation, Angelaki, Gu, DeAngelis 2009

11/10

Probabilistic inference II: Neural implementation, Sampling
Optional:
1. Kording et al 2007, Causal Inference in cue integration 2. Fiser et. al 2010 TICS

11/15

Probabilistic inference III: Neural sampling
Readings:
1. Hoyer & Hyvärinen 2003
Optional:
1. Buesing et. al 2011

11/17

Probabilistic inference IV: Learning + JC (Olshausen & Field 1996, Hoyer)
Readings:
1. Emergence of simple-cell receptive field properties by learning a sparse code for natural images

11/22

Probabilistic inference V: Learning +JC (Berkes et al. 2011)
Readings:
1. Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment, Berkes 2011

11/24

11/29

Bayesian Decision Theory, Action Selection
Optional:
1. Bayesian Decision models: A primer, Wei Ji Ma 2019

12/01

Reinforcement Learning I: Guest lecture by Prof. Ruben Moreno-Bote
Readings:
1. Introduction to Reinforcement Learning
Optional:
1. Model-based RL
2. Model Free RL

12/06

Reinforcement Learning II: Guest lecture by Prof. Ruben Moreno-Bote
Readings:
1. Gershman et. al

12/08

Levels of abstraction/attractors/simulations
Readings:
1. Gerstner et. al 2012

12/13

TBA