BCSC 247: Schedule
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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
08/27
Introduction
08/29
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)
09/03
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
09/05
Neuron models II: Biophysical neuron models and chemical synapses
Reading:
1. Hodgkin-Huxley model
2. Izhikevich model
3. 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: Izhikevich’s MATLAB Code
Reading:
1. Hodgkin-Huxley model
2. Izhikevich model
3. 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: Izhikevich’s MATLAB Code
09/06
Recitation 1
09/10
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
09/12
Encoding II: Turning curve, Reverse correlation
No required readings
Optional Reading:
1. Schwartz et al, Spike-triggered neural characterization
2. PCA tutorial
No required readings
Optional Reading:
1. Schwartz et al, Spike-triggered neural characterization
2. PCA tutorial
09/13
Recitation 2
Assignment 1 due at 9AM
Assignment 1 due at 9AM
09/17
Encoding III: RGCs, Simple & Complex cells, Reverse correlation
Readings: none
Optional: 1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf , (Section 2.4 - 2.8)
Assignment 2
Readings: none
Optional: 1. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott.pdf , (Section 2.4 - 2.8)
Assignment 2
09/19
Encoding IV: GLM
Readings:
1. GLM basics
Optional:
1. Pillow et. al 2008, Nature
2. Rust et. al 2005
Readings:
1. GLM basics
Optional:
1. Pillow et. al 2008, Nature
2. Rust et. al 2005
09/20
Recitation 3
09/23
Deadline for dropping the class
09/24
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)
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)
09/26
Decoding I: Signal Detection Theory
[video from previous year]
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)
[video from previous year]
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)
09/27
Recitation 4
ASSIGNMENT 2 due at 9AM
ASSIGNMENT 2 due at 9AM
10/01
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)
10/03
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
ASSIGNMENT 3
[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
ASSIGNMENT 3
10/04
Recitation 5
10/08
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)
10/10
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)
[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)
10/11
Recitation 6
10/14
ASSIGNMENT 3 due at 9AM
10/15
FALL BREAK
10/17
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
10/18
Recitation 7
10/22
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
10/24
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
10/25
Recitation 8
10/28
ASSIGNMENT 4
10/29
Deep Learning models of auditory processing 1: Guest lecture by Prof. Sam
Norman-Haignere
10/31
Deep Learning models of auditory processing 2: Guest lecture by Prof. Sam
Norman-Haignere
11/01
Recitation 9
11/05
Computational models in cognitive neuroscience: guest lecture by Prof. Cora
Iordan
11/07
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
11/08
Recitation 10
11/12
Probabilistic inference II: Neural implementation, Sampling
[video from previous year]
Optional: 1. Kording et al 2007, Causal Inference in cue integration
2. Fiser et. al 2010 TICS
[video from previous year]
Optional: 1. Kording et al 2007, Causal Inference in cue integration
2. Fiser et. al 2010 TICS
11/14
Probabilistic inference III: Neural sampling
[video from previous year]
Readings: 1. Hoyer & Hyvärinen 2003
Optional: 1. Buesing et. al 2011
ASSIGNMENT 4 due at 9AM
[video from previous year]
Readings: 1. Hoyer & Hyvärinen 2003
Optional: 1. Buesing et. al 2011
ASSIGNMENT 4 due at 9AM
11/15
Recitation 11
11/19
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
11/21
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
11/22
Recitation 12
ASSIGNMENT 5
ASSIGNMENT 5
11/26
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
11/28
Thanksgiving
12/03
Active Sensing
Readings: Yang, Wolpert, Lengyel (2016) “Theoretical perspectives on active sensing”
Readings: Yang, Wolpert, Lengyel (2016) “Theoretical perspectives on active sensing”
12/5
NeuroAI
12/6
Recitation 13
12/9
ASSIGNMENT 5 DUE at 9AM
TBA
Final Exam