This class will focus on large-scale neural data analysis and dynamics of large networks, in particular mean-field methods and manifolds, and is designed for doctoral students from EPFL with a strong background in physics or math and previous exposure to computational neuroscience. It is also open for PhD students in theoretical neuroscience from other universities in Europe (application form below).
Prerequisites: The class is taught at a level where teachers assume that standard material in computational and theoretical neuroscience is known. Standard material is available from text books on Theoretical Neuroscience and Neuronal Dynamics or from video lecture. In this class we go beyond and study important research papers.
For physicists and mathematician who would like to participate but are who are not certain about their background in (computational) neuroscience, we recommend to take a look at the textbook on neuronal dynamics and prepare for the class by reading all of Chapter 1 as well as the first few sections of each of the following chapters:
- Chapter 5, 6, 7, 8, 9, (models of single neurons and stochasticity)
- Chapter 12,13,14,15 (networks and population equations)
The main topic of the doctoral class builds on networks and population equations, but a bit of background in the description of single-neurons and stochasticity helps.
The class will run from Monday 12 – Friday 16 June at EPFL Lausanne campus. Start time will be 09:15am every morning.
|Monday 12 June||Tuesday 13 June||Wednesday 14 June||Thursday 15 June||Friday 16 June|
9:15 – 12:15
|Guillaume Hennequin||Alex Roxin||Friedemann Zenke||Moritz Helias||Anna Levina|
14:00 – 17:00
|Francesca Mastrogiuseppe||Alfonso Renart||Gianluigi Mongillo||Tilo Schwalger|
- 9-10 teachers will each organise a half-day class.
- 50 minutes public research talk on one of the teachers foundational papers, followed by 10 minute discussion
- 45 minutes on the math component of the research – for course participants. This part might be taught on the blackboard.
- 45 minutes further application of the theory OR calculation exercises for course participants
|Alex Roxin||CRM, Barcelona||An exact meanfield model for networks of quadratic integrate-and-fire neurons|
|Alfonso Renart||Champalimaud Neuroscience Programme, Lisbon||Correlated Variability in Recurrent Cortical Circuits|
|Anna Levina||Universitat Tubingen||Subsampling scaling: uncovering network states from incomplete observations|
|Francesca Mastrogiuseppe||Champalimaud Center, Lisbon||Linking connectivity, dynamics and computations in low-rank recurrent neural networks|
|Friedemann Zenke||FMI, Basel||The temporal paradox of Hebbian learning and homeostatic plasticity|
|Gianluigi Mongillo||Centre National de la Recherche Scientifique, Paris||Synaptic volatility, inhibitory plasticity and the synaptic trace theory of memory|
|Guillaume Hennequin||University of Cambridge||Failing to prepare is preparing to fail: a network theory of movement preparation and execution|
|Moritz Helias||Jülich Forschungszentrum, Aachen||Chaos, criticality, and computation in recurrent networks|
|Tilo Schwalger||Technische Universität Berlin||Mesoscopic description of neural population dynamics|
The class is co-organised by Professor Wulfram Gerstner (Laboratory of Computational Neuroscience, EPFL) and Professor Sahand Rahi (Laboratory of the Physics of Biological Systems, EPFL)
Please register for the course here. (for EPFL and external participants)
Registration deadline – Monday 20 May 2023, 23:00
Confirmation of acceptance – Wednesday 22 May 2023.
Course cost for non-EPFL students is 90CHF (including morning and afternoon coffee breaks each day). Payment due for final acceptance. See form below.
EPFL PhD students will receive 2 ECTS credit point for participation in the class – please also register on ISA! Course name : PHYS-722
PhD students from other universities will receive a certificate of attendance.
The class is listed in the Doctoral Program of Physics at EPFL.