- Series
- School of Mathematics Colloquium
- Time
- Thursday, March 6, 2025 - 11:00am for 1 hour (actually 50 minutes)
- Location
- Skiles 005 and Zoom: https://gatech.zoom.us/j/98474702488?pwd=2CiHNben05BqfpbikKkCuzzdr0MjdZ.1
- Speaker
- Dmitri Chklovskii – NYU and the Flatiron Institute – https://med.nyu.edu/faculty/dmitri-chklovskii
- Organizer
- Alex Dunn, Xiaoyu He, Rose McCarty, Dmitrii Ostrovskii, and Wei Zhu
Modern Artificial Intelligence (AI) systems, such as ChatGPT, rely on artificial neural networks (ANNs), which are historically inspired by the human brain. Despite this inspiration, the similarity between ANNs and biological neural networks is largely superficial. For instance, the foundational McCulloch-Pitts-Rosenblatt unit of ANNs drastically oversimplifies the complexity of real neurons.Recognizing the intricate temporal dynamics in biological neurons and the ubiquity of feedback loops in natural networks, we suggest reimagining neurons as feedback controllers. A practical implementation of such controllers within biological systems is made feasible by the recently developed Direct Data-Driven Control (DD-DC). We find that DD-DC neuron models can explain various neurophysiological observations, affirming our theory.