THEORETICAL NEUROSCIENCE

(Cogmaster, Course CA6(a))

http://www.lps.ens.fr/~risc/CA6/

(French version here )

(last update of this site : 16th of January 2012)

This course introduces quantitative approaches to three central questions in neuroscience: How is the brain made up? What functions and computations does it accomplish? By which mechanisms? The brain is a highly complex object; its function is also highly complex and at the same time extremely refined. Because of this, it is often impossible to establish direct links between the biochemistry, on the one hand, and brain function, on the other. Theoretical or computational neuroscience attempts to provide bridges between the two, by suggesting possible mechanisms that the brain may use in perception, learning, memory, motor control,… Furthermore, in recent years an enormous amount of neurobiological data has been collected with high precision. The sheer volume of data begs for computational principles that may help organize and understand it, and the unprecedented precision of the data is now allowing for detailed comparisons with mathematical theories.

The scope of the course is threefold. First, to present a number of questions for which a quantitative approach is relevant. Second, to introduce quantitative or mathematical tools necessary to the study of these questions, as well as to the study of similar questions in related fields (psychophysics, computer science, biophysics, bioengineering,...). Third, and maybe most importantly, to discuss concrete examples relevant to brain function in which one can make progress through quantitative thinking. Questions and examples that will be discussed include: How do neurons code inputs to the brain? Is ‘function’ carried out by single neurons or by groups of neurons? How can vision be so precise? Where do the brain rhythms come from? How can one model the learning and storage of memories? How does the brain generate outputs such as motor outputs?

Outline of the course Attendance, listeners Time & place Problem sets, exam Useful references Course material Tutoring

NEW:
Exam: instructions here - Oral part: schedule here (updated Dec. 19 )
Course material updated Jan. 16, 2012
Problem sets: see here (posted Oct. 12, 2010).

TEACHING FACULTY

Romain Brette (01 44 32 26 72, romain.brette [at] ens.fr)

Nicolas Brunel (01 42 86 20 58, nicolas.brunel [at] biomedicale.univ-paris5.fr )

Gianluigi Mongillo (01 42 86 38 13, gianluigi.mongillo [at] univ-paris5.fr)

Jean-Pierre Nadal (01 44 32 32 75, nadal [at] lps.ens.fr)

Tutoring/TDs: Alexis Dubreuil (alexis.dubreuil [at] gmail.com)

ATTENDANCE FOR CREDIT & LISTENERS

This course belongs to the Master in Cognitive Studies ('cogmaster', ENS, EHESS, University Paris Descartes), and hence appears in the curriculum of students in Cognitive Studies (“Advanced Course CA6(a)”, 6 ECTS)

The course is also open to L3, M1, and M2 physics students; the course can be taken for credit as a non-specialty (non-physics) course by physics students at ENS, but may be taken for credit as well as a specialty (physics) course upon agreement by the Head of Teaching in the Department of Physics.

L3, M1, and M2 biology students at ENS can also take the course for credit as a non-specialty (non-biology) course.

We more specifically recommend the course for credit for M2 cognitive science and biology students, and M1 math, physics, and computer science students.

PhD students from Ecole doctorale de physique de la région parisienne (ED107) can take the course as one of the optionnal courses. Other Ecoles doctorales allow to take this course among those which have to be taken during the thesis (do not hesitate to ask)

All L3, M1, and M2 students, from any department, are welcome as (non-credit) listeners.

All graduate students, postdocs, visitors, and researchers are welcome as listeners.

TIME & PLACE OF CLASSES

All classes of the Course "Theoretical Neuroscience"
will take place every Thursday (not including holidays)
from 1:00PM to 4:30PM, with a 30mn break around 2:30PM.
Class 1 will be held on on Thursday, 29th of September 2011, and the last class of the term on Thursday, the 12th of January 2012.

Location: Room L367 (previously called T4/5) (except on the 5th of Jan.: room L359 / T2 ), third floor of the Physics Department of ENS (24, rue Lhomond, Paris 5).

PROBLEM SETS

Problem sets will be distributed and discussed on Wednesdays, 1:30pm-3pm, room L367 (T4/5) - except on the 4th of Jan.: room L359 / T2.
(1st session on Wednesday October 12)
Problem sets for the current year are posted here - access with password.

FINAL EXAM

For students taking the course for credit, the exam will be partly written, and partly an oral based on an article given in advance. The written part will take place on Thursday, the 19th of January 2012.
Detailed instructions are given here.

PREREQUISITES

The course has few prerequisites. An elementary mathematical background (analysis, algebra, probability) is useful and will be reviewed in the course if/when necessary. Some knowledge of neurobiology, and of dynamical systems and statistical mechanics, will be helpful but not necessary.

TUTORING

If necessary, specific tutoring sessions will be organized: For students with little background in maths/physics, tutoring sessions will cover some basic mathematical tools useful for the understanding of the lectures. Similarly, tutoring sessions will cover some notions in biology and experimental techniques.
Contact us if interested.

WEBSITE

Informative and reference material for the course will be posted on this web page,

http://www.lps.ens.fr/~risc/CA6/ .

OUTLINE OF THE COURSE

Course CA6 – Academic year 2011/2012

Preliminary outline of the Course:

1. Introduction
Lecture 1 (Sept 29)     - General overview 
2. Basic tools
Lecture 2 (Oct 6)       - 2.1 Neurons
Lecture 3 (Oct 13)      - 2.2 Synapses 
Lecture 4 (Oct 20)      - 2.3a Learning I (intro; feedforward networks)
Lecture 5 (Oct 27)      - 2.3b Learning II (recurrent networks of binary neurons)
Lecture 6 (Nov 10)      - 2.4 Coding
Lecture 7 (Nov 17)      - 2.5a Networks: architectrures, rate models.
Lecture 8 (Nov 24)      - 2.5b Networks of spiking neurons
3 Models of specific systems
Lecture 9 (Dec 1)       - 3.2 Primary visual cortex
Lecture 10 (Dec 8)      - 3.3 Auditory system
Lecture 11 (Dec 15)     - 3.4 Hippocampus
Lecture 12 (Jan 5)      - 3.5 Association cortex
Lecture 13 (Jan 12)     - 3.6 Cerebellum