Seminar in Quantitative Neuroscience


coordinators:  Srdjan Ostojic


To do by Monday evening:
- Read paper from current topic and submit a question to srdjan.ostojic@ens.fr

Outline of the Course

In this course, the students will go through several, mostly recent articles within the broader area of computational neuroscience. Each week will be dedicated to one research article (see choice of articles below). This article will be presented by a student in a short talk (about 30 minutes), followed by a discussion among the students. To ensure a lively discussion, every student has to read the article and submit a question about it one week in advance. The topics cover computational models from the single neuron level to behavior. Most of the articles adress modern problems in the brain sciences and use a combination of experiments and mathematical analysis to solve them. Students will be free to choose an article to their liking, and will be assigned one of the faculty member as advisor. Every session is closed with a 5-10 min review talk by the responsible professor.

Intended Audience

  • M1/M2 students with a quantitative background (physics, computer science, mathematics, engineering etc.) that are interested in neuroscience
  • M1/M2 students with a biology/neuroscience background that are interested in quantitative (mathematical) approaches to neuroscience

Validation

Students in the course will be judged by the talk they give (50%) and their participation in the discussions of all the other talks (50%). The course will be worth 4 ECTS credits.

Course Language

The course languages will be French and English. Talks can be held in either language.

Space-Time Coordinates

Wednesdays 14.30-16.30
29 rue d'Ulm, salle Ribot (ground floor)
First meeting and introduction: October 4 2017
Next meetings:  October 11, October 18, ...

Contact

Coordinator: Srdjan Ostojic (01 44 32 26 44, srdjan.ostojic@ens.fr)
Matthew Chalk (matthewjchalk@gmail.com)
Boris Gutkin (01 44 32 27 93, boris.gutkin@ens.fr)
Vincent Hakim (01 44 32 37 68, hakim@lps.ens.fr)
Kishore Kuchibhotla (kuchibh@gmail.com)

Schedule

To be determined after first class.
Date Speaker Topic Presentation Responsible Faculty
October 4    
Introduction
General Instructions Srdjan Ostojic
October 11
Srdjan Ostojic
17
Example presentation Srdjan Ostojic
October 18 Alex Auvolat 5
Reading Population Codes - Theory
Srdjan Ostojic
October 25
Kexin Ren
3What Does a Fly See?
Matthew Chalk
November 1
NO CLASS



November 8
Kevin Berlemont10
How Do Neurons Decide?
Srdjan Ostojic
November 15
Laya Ghodrati16
How Do We Navigate? The Curious Case of Grid Cells.
Vincent Hakim
 November 22 Theo Desbordes 7 How Are Complex Cognitive Tasks Implemented? Srdjan Ostojic
November 29 Charlotte Piette14
How robust are our brains?
Kishore Kuchibhotla
December 6
Ava Kiai
1
What Determines a Neuron's Tuning?
Matthew Chalk
December 13
NO CLASS


December 20
Lea Boyer21
Neural constraints on learning.
Kishore Kuchibhotla
January 10
Arnaud Poublan4
What Does a Human Being See?
Srdjan Ostojic

Articles/ Talks

Before You Start

How To give a talk. pdf
General instructions for the presentation. pdf

Topic 1: What Determines a Neuron's Tuning? Efficient Coding of Sensory Information

Smith EC, Lewicki MS (2006) Efficient auditory coding. Nature 398:334-338. pdf
Responsible faculty: Matthew Chalk

Topic 2: Is the brain optimized for natural stimuli? Coding Natural Signals

Schwartz O. and Simoncelli EP (2001) Natural signal statistics and sensory gain control Nature Neuroscience 4:819-825. pdf
Responsible faculty: Matthew Chalk

Topic 3: What Does a Fly See? Reading a Neural Code

Bialek W, Rieke F, de Ruyter van Steveninck RR, Warland D (1991) Reading a neural code. Science 252(5014):1854-1857. pdf
Responsible faculty: Matthew Chalk

Topic 4: What Does a Human Being See? Reading the Human Mind

Kay KN, Naselaris T, Prenger RJ, Gallant JL (2008) Identifying natural images from human brain activity. Nature 452(7185):352-355. pdf
Responsible faculty: Matthew Chalk

Topic 5: What Do Neurons Know? Reading Population Codes - Theory

Jazayeri M, Movshon JA (2006) Optimal representation of sensory information by neural populations. Nature Neuro 9 (5), 690. pdf
Responsible faculty: Srdjan Ostojic

Topic 6: What Do Neurons Know? Reading Population Codes - Experiment

Cohen MR, Newsome WT (2009) Estimates of the contribution of single neurons to perception depend on timescale and noise correlation. J Neurosci 29(20):6635-6648. pdf
Responsible faculty: Srdjan Ostojic

Topic 7: How Are Complex Cognitive Tasks Implemented? The Importance of Mixed Selectivity Neurons

Rigotti M, Barak O, Warden MR, Wang XJ, Daw ND, Miller EK, Fusi S (2013) The importance of mixed selectivity in complex cognitive tasks. Nature 497:585–590. pdf
Responsible faculty: Srdjan Ostojic

Topic 8: Do Neurons Know Probabilities? Probabilistic Population Codes.

Fischer B and Peña JL (2011) Owl’s behavior and neural representation predicted by Bayesian inference. Nature Neuroscience 14: 1061-1066 pdf
Responsible faculty: Matthew Chalk

Topic 9: How Do Neurons Decide? Perceptual Decision Making.

Gold JI and Shadlen MN (2000) Representation of a perceptual decision in developing oculomotor commands. Nature 404:390-394. pdf
Responsible faculty: Matthew Chalk

Topic 10: How Do Neurons Decide? A computational model.

Wang XJ (2002) Probabilistic Decision Making by Slow Reverberation in Cortical Circuits. Neuron 36:955-968. pdf
Responsible faculty: Srdjan Ostojic

Topic 11: How Do We Deal With Ambiguity? A biophysical model of binocular rivalry.

Seely J, Chow CC (2002) Role of mutual inhibition in binocular rivalry. J Neurophysiol 106: 2136–2150. pdf
Responsible faculty: Vincent Hakim

Topic 12: How is confidence encoded? Decision-making in uncertain conditions.

Kiani R, Shadlen MN (2009) Representation of confidence associated with a decision by neurons in the parietal cortex. Science 8;324:759-64. pdf
Responsible faculty: Kishore Kuchibhotla

Topic 13: How Do We Deal With Uncertainty? Encoding confidence in the brain.

Sanders JI, Hangya B, Kepecs A (2016) Signatures of a Statistical Computation in the Human Sense of ConfidenceNeuron 90(3):499-506. pdf
Responsible faculty: Vincent Hakim

Topic 14: How robust are our brains? Redundancy as a principle in neuronal networks.

Li N, Daie K, Svoboda K, Druckmann S (2016) Robust neuronal dynamics in premotor cortex during motor planning. Nature 532(7600):459-64. pdf
Responsible faculty: Kishore Kuchibhotla

Topic 15: How Do Neurons Communicate? The Conundrum of the Neural Code.

Mehta MR, Lee AK, Wilson MA (2002) Role of experience and oscillations in transforming a rate code into a temporal code. Nature 487:741-746 pdf
Responsible faculty: Boris Gutkin

Topic 16: How Do We Navigate? The Curious Case of Grid Cells.

Burak Y and Fiete I (2009) Accurate Path Integration in Continuous Attractor Network Models of Grid Cells. Plos Comput Biol 5(2): e1000291. pdf
Responsible faculty: Vincent Hakim

Topic 17: How is working memory stored? Modelling persistent activity

Compte A, Brunel N, Goldman-Rakic PS, Wang XJ (2000) Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model. Cerebral Cortex 10:910-923. pdf
Responsible faculty: Srdjan Ostojic

Topic 18: How Do We Recall Memories? Scaling laws of memory retrieval.

Katkov M,  Romani S and Tsodyks M (2015) Effects of long-term representations on free recall of unrelated words. Learn. Mem. 22: 101-108. pdf
Responsible faculty: Vincent Hakim

Topic 19: Can You Drink Yourself to Death? Addiction as a Computational Process Gone Awry.

Redish AD (2004) Addiction as a computational process gone awry. Science 306(5703):1944-1947. pdf + debate (see pubmed)
Responsible faculty: Boris Gutkin

Topic 20: How Should You Act in Unknown Environments? The Exploration-Exploitation Dilemma Revisited.

Daw ND, O'Doherty JP, Dayan P, Seymour B, Dolan RJ (2006) Cortical substrates for exploratory decisions in humans. Nature 441:876-879. pdf
Responsible faculty: Srdjan Ostojic

Topic 21: Are some things easier to learn than others? Neural constraints on learning.

Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI, Tyler-Kabara EC, Yu BM, Batista AP. (2014) Neural constraints on learning. Nature 28;512(7515):423-6. pdf
Responsible faculty: Kishore Kuchibhotla

Topic 22: How Energy-Efficient Is the Brain? Optimizing Computations with a 20 Watt-Limit.

Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cerebral Blood Flow Metabolism:475-80. pdf
Responsible faculty: Vincent Hakim

Topic 23: Can a Brain Be Reconstructed in the Near Future? The Science of Connectomics

Seung SH (2009) Reading the Book of Memory: Sparse Sampling versus Dense Mapping of Connectomes. Neuron 62:17. pdf
Responsible faculty: Vincent Hakim