RT Journal Article
SR Electronic
T1 A Neuronal Least-Action Principle for Real-Time Learning in Cortical Circuits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.03.25.534198
DO 10.1101/2023.03.25.534198
A1 Walter Senn
A1 Dominik Dold
A1 Akos F. Kungl
A1 Benjamin Ellenberger
A1 Jakob Jordan
A1 Yoshua Bengio
A1 João Sacramento
A1 Mihai A. Petrovici
YR 2023
UL http://biorxiv.org/content/early/2023/03/25/2023.03.25.534198.abstract
AB One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neural least-action principle that we apply to motor control. The central notion is the somato-dendritic mismatch error within individual neurons. The principle postulates that the somato-dendritic mismatch errors across all neurons in a cortical network are minimized by the voltage dynamics. Ongoing synaptic plasticity reduces the somato-dendritic mismatch error within each neuron and performs gradient descent on the output cost in real time. The neuronal activity is prospective, ensuring that dendritic errors deep in the network are prospectively corrected to eventually reduce motor errors. The neuron-specific errors are represented in the apical dendrites of pyramidal neurons, and are extracted by a cortical microcircuit that ‘explains away’ the feedback from the periphery. The principle offers a general theoretical framework to functionally describe real-time neuronal and synaptic processing.Competing Interest StatementThe authors have declared no competing interest.