Spatiotemporal Local Propagation
This addresses the problem of biologically implausible learning algorithms in neural networks for researchers in computational neuroscience and AI, though it appears incremental as it builds on existing variational methods.
The paper tackles the biological implausibility of backpropagation in neural networks by proposing a new computational scheme called SpatioTemporal Local Propagation (STLP), which is local in space and time and does not require error backpropagation, based on a variational framework inspired by physics.
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).