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- W4386939478 abstract "Models of sensory processing and learning in physical substrates (such as the cortex) need to efficiently assign credit to synapses in all areas. In deep learning, a well-established solution is error backpropagation; this however carries several biologically implausible requirements, such as weight transport from feed-forward to feedback paths. We present Phaseless Alignment Learning (PAL), a biologically plausible approach for learning efficient feedback weights in layered cortical hierarchies. Our dynamical system enables the simultaneous learning of all weights with always-on plasticity, and exclusively utilizes information locally available at the synapses. PAL is entirely phase-free, avoiding the need for forward and backward passes or phased learning, and enables efficient error propagation across multi-layer cortical hierarchies, while maintaining bio-physically plausible signal transport and learning." @default.
- W4386939478 created "2023-09-22" @default.
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- W4386939478 date "2023-01-01" @default.
- W4386939478 modified "2023-09-28" @default.
- W4386939478 title "Learning Efficient Backprojections Across Cortical Hierarchies in Real Time" @default.
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- W4386939478 doi "https://doi.org/10.1007/978-3-031-44207-0_48" @default.
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