Computational neuroscience is an expanding field that is proving to be essential in neurosciences. The aim of this short intensive course will be to provide a common background in computational neuroscience. The course, after a brief historical overview of the field, will focus on the description of a few selected modelling and theoretical approaches that are currently developed, including details about their limits and advantages, and that can be applied to different scales of analysis (from the single neuron to the whole brain). In addition, we will provide a theoretical and a practical session on artificial neuronal networks of spiking neurons.
Objectives: Understanding how computational modelling can be used to formulate and solve neuroscience problems at different spatial and temporal scales; learning the formal notions of information, encoding and decoding and experimenting their use on specific examples
Where: Marseille (France)
The aim of this task is to read a scientific article, to reproduce it with simulations of a neuron and to improve the understanding of the study.
Modalities: students will organize themselves alone, in pairs or in triads to provide a brief in the form of a notebook completed from the model that is provided. Follow the
QUESTION tags in the notebook to guide you in this writing. Comments should be made in the notebook (don’t forget to save your changes).
Here, we will attempt to replicate Figure 1 of Mainen & Sejnowski (1995):
an article to read about time in the brain: https://laurentperrinet.github.io/publication/perrinet-19-temps/ direct link
From illusions to visual hallucinations: a door on perception - (slides) - article on visual perception: https://laurentperrinet.github.io/post/2019-06-06-theconversation/ direct link