BCSC 247: Schedule
Only students who are enrolled in the course may access materials online. You must be logged into Blackboard to access these files.
Updated slides for this year will be posted on Blackboard after each lecture.
If any lectures or exams are overlapping with your religious holidays, please contact the instructor or teaching assistants in advance.
Date
Topic/Materials
08/26
Introduction
08/28
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 __ Information Processing in Neural Networks.pdf (untill the Encoding and Decoding section)
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 __ Information Processing in Neural Networks.pdf (untill the Encoding and Decoding section)
09/02
Neuron models I: Building a Mathematical Model of a Neuron
Video: Euler method for numerical simulation
Reading: 1. Neuronal Dynamics book
2. Ion channels and equilibrium membrane potential
Simulator Playground
Above the heading, Primer Biology of the neuron, select integrate and fire model from the dropdown and play around with the parameters)
Optional readings: Neuronal Dynamics book (Section 2.1.1)
Assignment 1 is given
Video: Euler method for numerical simulation
Reading: 1. Neuronal Dynamics book
2. Ion channels and equilibrium membrane potential
Simulator Playground
Above the heading, Primer Biology of the neuron, select integrate and fire model from the dropdown and play around with the parameters)
Optional readings: Neuronal Dynamics book (Section 2.1.1)
Assignment 1 is given
09/04
Neuron models II: Biophysical neuron models and chemical synapses
Reading:
1. Izhikevich model
2. Neural Dynamics book (Section 3.1 Synapses)
Simulator Playground (Above the heading, Primer Biology of the neuron, select ‘simple model’ from the dropdown and play around with the parameters)
Optional: Hodgkin-Huxley model
Izhikevich’s MATLAB Code
Reading:
1. Izhikevich model
2. Neural Dynamics book (Section 3.1 Synapses)
Simulator Playground (Above the heading, Primer Biology of the neuron, select ‘simple model’ from the dropdown and play around with the parameters)
Optional: Hodgkin-Huxley model
Izhikevich’s MATLAB Code
09/05
Recitation 1
09/09
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
2. Deriving Poisson from Binomial Distribution with low success probability
3. Poisson and Exponential distribution
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
2. Deriving Poisson from Binomial Distribution with low success probability
3. Poisson and Exponential distribution
09/11
Encoding II: RFs, simple & complex cells, reverse correlation
No required readings
Optional Reading:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf , (Section 2.4 - 2.8)
2. Schwartz et al, Spike-triggered neural characterization
3. PCA tutorial
No required readings
Optional Reading:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf , (Section 2.4 - 2.8)
2. Schwartz et al, Spike-triggered neural characterization
3. PCA tutorial
09/12
Recitation 2
09/13
Assignment 1 due at 9AM
09/16
Encoding III: Reverse correlation, GLM
Readings: 1. GLM basics
Optional: 1. Pillow et. al 2008, Nature
2. Rust et. al 2005
ASSIGNMENT 2
Readings: 1. GLM basics
Optional: 1. Pillow et. al 2008, Nature
2. Rust et. al 2005
ASSIGNMENT 2
09/18
Information Theory
Readings:
1. Kenji Doya, Shin Ishii, Alexandre Pouget, Rajesh P. N. Rao - Bayesian Brain: Probabilistic Approaches to Neural Coding.pdf
2. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Chapter 3.1 - 3.2)
Optional:
1. Cover and Thomas(Chapter 2.1 - 2.5)
2. Tutorial on Fisher Information (Section 1)
Readings:
1. Kenji Doya, Shin Ishii, Alexandre Pouget, Rajesh P. N. Rao - Bayesian Brain: Probabilistic Approaches to Neural Coding.pdf
2. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Chapter 3.1 - 3.2)
Optional:
1. Cover and Thomas(Chapter 2.1 - 2.5)
2. Tutorial on Fisher Information (Section 1)
09/19
Recitation 3
09/22
Deadline for dropping the class
09/23
Decoding I: Signal Detection Theory
09/25
Decoding II: Population decoding, Fisher Information
[video from previous year]
No readings
Optional:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Chapter 3.3, 3.4+)
2. Neural correlations, population coding, and computation (Averbeck et. al 2008)
[video from previous year]
No readings
Optional:
1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf (Chapter 3.3, 3.4+)
2. Neural correlations, population coding, and computation (Averbeck et. al 2008)
09/26
Recitation 4
09/27
ASSIGNMENT 2 due at 9AM
09/30
Decoding III: Noise correlations, Choice Probabilities
[video from previous year]
Readings:
1. Parker & Newsome 1998: p. 227-231
2. Zohary et. al 1994, Correlated neuronal discharge rate and its implications for psychophysical performance
Optional:
1. Information-limiting correlations, Moreno-Bote 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, Ecker 2011
4. Notter Lecture, Anne Churchland
[video from previous year]
Readings:
1. Parker & Newsome 1998: p. 227-231
2. Zohary et. al 1994, Correlated neuronal discharge rate and its implications for psychophysical performance
Optional:
1. Information-limiting correlations, Moreno-Bote 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, Ecker 2011
4. Notter Lecture, Anne Churchland
10/02
Deep Learning & Brain I: General introduction, Performance optimized
networks
[video from previous year]
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
Optional:
1. Performance-optimized hierarchical models predict neural responses in higher visual cortex, Yamins 2014 PNAS
[video from previous year]
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
Optional:
1. Performance-optimized hierarchical models predict neural responses in higher visual cortex, Yamins 2014 PNAS
10/03
Recitation 5
10/07
Deep Learning & Brain II: RSA
[video from previous year]
No required readings
Optional:
1. Kriegeskorte 2009, Relating population-code representations between man, monkey, and computational models
2. A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
3. Cadena 2019
4. Walker 2019 Nature
Goal-Driven Recurrent Neural Network Models of the Ventral Visual Stream
[video from previous year]
No required readings
Optional:
1. Kriegeskorte 2009, Relating population-code representations between man, monkey, and computational models
2. A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
3. Cadena 2019
4. Walker 2019 Nature
Goal-Driven Recurrent Neural Network Models of the Ventral Visual Stream
10/09
Deep learning and the brain III: Controversial images, topographic ANNs
10/10
Recitation 7
10/11
ASSIGNMENT 3 due at 9AM
10/14
FALL BREAK
10/16
Deep Learning models of auditory processing 1: Guest lecture by Prof. Sam
Norman-Haignere
Deep Learning models of auditory processing 2: Guest lecture by Prof. Sam Norman-Haignere
Deep Learning models of auditory processing 2: Guest lecture by Prof. Sam Norman-Haignere
10/21
Midterm Exam
10/23
Decision-making I
[video from previous year]
Video: 1. 1st Lecture from 2021 class by Ariel Zylberberg (video from previous year)
Readings:
1. Gold and Shadlen 2007:The Neural Basis of Decision Making (Pages 535-541)
[video from previous year]
Video: 1. 1st Lecture from 2021 class by Ariel Zylberberg (video from previous year)
Readings:
1. Gold and Shadlen 2007:The Neural Basis of Decision Making (Pages 535-541)
10/24
Recitation 8
10/27
Decision-making II
[video from previous year]
Readings:
2. Gold and Shadlen 2007:The Neural Basis of Decision Making (Pages 542-end)
Optional video: 1. 2nd Lecture from 2021 class by Ariel Zylberberg (video from previous year)
ASSIGNMENT 4 given
[video from previous year]
Readings:
2. Gold and Shadlen 2007:The Neural Basis of Decision Making (Pages 542-end)
Optional video: 1. 2nd Lecture from 2021 class by Ariel Zylberberg (video from previous year)
ASSIGNMENT 4 given
10/29
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
Readings:
1. Park et. al 2014 Encoding and decoding in parietal cortex during sensorimotor decision-making
10/30
Recitation 9
11/04
Probabilistic inference I: Intro, Behavior, Bayesian Networks
[video from previous year]
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
[video from previous year]
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
11/06
Probabilistic inference II: Neural sampling
[video from previous year]
Readings: 1. Hoyer & Hyvärinen 2003
Optional: 1. Buesing et. al 2011
[video from previous year]
Readings: 1. Hoyer & Hyvärinen 2003
Optional: 1. Buesing et. al 2011
11/13
Probabilistic inference IV: Learning + JC (Olshausen & Field 1996,
Hoyer)
[video from previous year]
Readings: 1. Emergence of simple-cell receptive field properties by learning a sparse code for natural images
[video from previous year]
Readings: 1. Emergence of simple-cell receptive field properties by learning a sparse code for natural images
11/14
Recitation 11
11/18
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
Readings: 1. Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment, Berkes 2011
11/18
ASSIGNMENT 5
11/20
Bayesian Decision Theory, Action Selection
Optional: 1. Bayesian Decision models: A primer, Wei Ji Ma 2019
Optional: 1. Bayesian Decision models: A primer, Wei Ji Ma 2019
11/21
Recitation 12
11/25
Active Sensing
Readings: Yang, Wolpert, Lengyel (2016) “Theoretical perspectives on active sensing”
Readings: Yang, Wolpert, Lengyel (2016) “Theoretical perspectives on active sensing”
11/27
Thanksgiving
12/02
Reinforcement Learning
12/04
Recap & NeuroAI
12/05
Recitation 13
12/7
ASSIGNMENT 5 DUE at 9AM
TBA
Final Exam