no code implementations • ICLR 2018 • Davide Zambrano, Isabella Pozzi, Roeland Nusselder, Sander Bohte
These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains.
1 code implementation • 5 Nov 2018 • Isabella Pozzi, Sander Bohté, Pieter Roelfsema
Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain.
no code implementations • 25 Sep 2019 • Isabella Pozzi, Sander M. Bohte, Pieter R. Roelfsema
While much recent work has focused on biologically plausible variants of error-backpropagation, learning in the brain seems to mostly adhere to a reinforcement learning paradigm; biologically plausible neural reinforcement learning frameworks, however, were limited to shallow networks learning from compact and abstract sensory representations.
1 code implementation • NeurIPS 2020 • Isabella Pozzi, Sander Bohte, Pieter Roelfsema
We show how the new learning scheme – Attention-Gated Brain Propagation (BrainProp) – is mathematically equivalent to error backpropagation, for one output unit at a time.