#Imbizo - Computational Neuroscience Imbizo

Imbizo is a Xhosa word meaning “a gathering to share knowledge”. The IBRO-Simons Computational Neuroscience Imbizo, or isiCNI is exactly that: an opportunity for African and international students to learn about cutting edge research techniques in computational neuroscience.

Application info

Applications are closed

Opening end June 2023

Open until: Friday 18 Aug 2023 at 19:00:00 +0200

Application period

end June 2023 - 18 Aug 2023 19:00:00 +0200
notified October 2023 (approx)

The school

Duration

Jan 07, 2024 - Jan 28, 2024

The school will consist of four 5-day work weeks with 1 day off in-between. Each week will be focused on one of the themes appearing below. Your day off will be spent exploring some of the beautiful wonders around Cape Town. Each workday will consist of lectures in the morning, followed by either further lectures/practical tutorials in the afternoons.

Biophysics, Plasticity and Neural Recordings

What makes a neurons special? How do we model them mathematically? How do we model synapses and why are they important for neural function? These questions will guide the first part of the Imbizo. We will also learn about cutting-edge techniques used for large-scale neural data recording from world experts in patch-clamping techniques and from the NEUROPIXEL project.

Network Dynamics

The functioning of the brain is ultimately an emergent phenomenon; a result of the complex interactions between billions of neurons. We will learn how to model and analyse these dynamic networks, and how biological trends like Dale's Law make understanding these system more difficult and fascinating.

Higher Level Function, Motor Control & Decision Making

This cycle will try to bridge the gap from what we have learnt about the biology and dynamics of brains to what we observe in behaviour. We will also learn about cutting-edge research in brain-computer interfacing and what this is revealing about how brains work.

Machine Learning

We will also learn what the modern field of machine learning can teach us about learning in network architectures and how these artificial systems are similar and different to biological neural systems.