ePoster · Computational Neuroscience
Biological-plausible learning with a two compartment neuron model in recurrent neural networks
Timo Oess, Daniel Schmid, Heiko Neumann · Bernstein Conference 2024
Artificial recurrent neural networks (RNNs) are difficult to train due to their tendency towards instability, and common training algorithms that tame such networks are not biologically plausible, i.e., back-propagation through time (BPTT). Node perturbation learning in combination with local Hebbian weight updates has been shown to approximate BPTT [1] and, thus, could achieve similar performance while being biologically plausible. However, where these perturbations could arise from and how they are utilized within neurons remains obscure. For years, the neuroscientific community has known ab