  This paper proposes an in-depth re-thinking of neural computation that
parallels apparently unrelated laws of physics, that are formulated in the
variational framework of the least action principle. The theory holds for
neural networks that are also based on any digraph, and the resulting
computational scheme exhibits the intriguing property of being truly
biologically plausible. The scheme, which is referred to as SpatioTemporal
Local Propagation (STLP), is local in both space and time. Space locality comes
from the expression of the network connections by an appropriate Lagrangian
term, so as the corresponding computational scheme does not need the
backpropagation (BP) of the error, while temporal locality is the outcome of
the variational formulation of the problem. Overall, in addition to conquering
the often invoked biological plausibility missed by BP, the locality in both
space and time that arises from the proposed theory can neither be exhibited by
Backpropagation Through Time (BPTT) nor by Real-Time Recurrent Learning (RTRL).

Machine Learning;Machine Learning