The Computational Power of Single Neurons: Dendrites as Architectural Primitives for Neural Networks

Most of artificial intelligence, and most models of the brain, treat a neuron as a simple unit that sums its inputs. Real cortical neurons, however, carry elaborate dendritic trees with nonlinear ion channels and thousands of synapses, which points to substantial computation inside a single cell. Ido Aizenbud trains biophysically detailed, realistic neuron models with gradient descent and shows that one cortical neuron can solve sensory tasks such as visual and auditory classification, as well as abstract nonlinear parity tasks that lie far beyond the reach of point neurons. The talk then outlines how this approach scales to networks of detailed neurons, treating dendrites as architectural primitives both for understanding brain circuits and for building more powerful, energy-efficient AI. The talk is one of the Kempner Institute Research Fellow candidate presentations.