Temporal Contrastive Learning through implicit non-equilibrium memory.

ABSTRACT: The backpropagation method has enabled transformative uses of neural networks. Alternatively, for energy-based models, local learning methods involving only nearby neurons offer benefits in terms of decentralized training, and allow for the possibility of learning in computationally-constrained substrates. One class of local learning methods contrasts the desired, clamped behavior with spontaneous, free behavior. However, directly contrasting free and clamped behaviors requires explicit memory. Here, we introduce 'Temporal Contrastive Learning', an approach that uses integral feedback in each learning degree of freedom to provide a simple form of implicit non-equilibrium memory. During training, free and clamped behaviors are shown in a sawtooth-like protocol over time. When combined with

SUBMITTER: Falk MJ 

PROVIDER: S-EPMC11880436 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature