IEEE CIS Webinar: The Neurobiology of Artificial Intelligence

Abstract:
Current approaches to artificial intelligence (AI) are almost exclusively based on artificial neuronal networks, following a major (and in the history of AI surprisingly recent) shift away from symbol-processing logic systems [1]. The analogy to biological neural networks seems obvious, but how useful is the comparison? Can we, and should we, learn from each other? In this webinar I will present a neurobiologist's view of what may or may not constitute 'intelligence' and how neural network development and training (nature and nuture) contribute to network performance. In particular, I will explore self-organization of neural network topology and present an example how growth leads to precise, flexible and robust networks [2].

[1] Hiesinger, P.R. (2021). The Self-Assembling Brain. Princeton University Press.
[2] Agi, E., ..., Hiesinger, P.R. (2024). Axonal Self-Sorting Without Target Guidance in Drosophila Visual Map Formation. Science, 2024 Mar 8;383(6687):1084-1092.

Biography:
P. Robin Hiesinger is professor of neurobiology at Freie Universität Berlin, Germany, where he teaches undergraduate and graduate students. Robin leads a neurobiology research laboratory with a focus on basic research in brain wiring (flygen.org) and an interdisciplinary multilab research consortium on the role of noise in neural network assembly (robustcircuit.org). He is the author of the book The Self-Assembling Brain (selfassemblingbrain.com).