#Imbizo - Simons Computational Neuroscience Imbizo

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

Application info

Imbizo 2027

Applications will open in January 2027

Opening 05 January 2027

Open until: Monday 01 Mar 2027 at 23:59:59 SAST

Application period

05 January 2027 - 01 Mar 2027 23:59:59 SAST
Decision emails in April 2027

The school

late Aug - mid Sept 2027

The school will consist of three cycles. Cycles 1, 2, and 3 will include 6 working days 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 further lectures or practical tutorials in the afternoons.

Molecules to Behavior

Cycle 1 will introduce biophysical mechanisms in individual cells and how these cell-autonomous features become building blocks of neural networks. We will cover some introductory neuroscience across levels of analysis (molecules-neurons-networks-behavior) as we begin to analyze functional and architectural specializations that are leveraged to resolve computational demands across behavioral domains.

Cells to circuits to behavior

This cycle will bridge the gap between what we have learned about the biology and dynamics of brains and what we observe in behaviour. We will learn about systems-level functions including sensory processing, birdsong, motor control and decision-making.

NeuroAI

Biological and artificial systems have at least three things in common: (a) they sense "the world", (b) they turn data from the senses into representations, and (c) they are useful for accomplishing tasks/goals. During the final week, we will explore guiding principles for intelligent systems (both artificial and natural), with an emphasis on how biological principles can inform machine learning, and how machine learning can help us understand brains.