Synaptic connections are continuously formed and eliminated even in adult neuronal networks. This process - called structural plasticity - allows networks to change their topology to learn and adapt to their environment or compensate for lesions. Yet, in contrast to synaptic plasticity, structural plasticity is less we...
M. Fauth, Han Lu, Jannik Luboeinski et al.· 0 citations
This work examines how batch size, training duration, and estimator stabilization affect weight perturbation accuracy in fully connected MNIST networks, and identifies near-BP accuracy as a configuration- and budget-dependent possibility, rather than evidence of equal efficiency or an unconditional accuracy guarantee a...
Biologically motivated artificial visual learning requires local plasticity rules to be evaluated within architectures whose connectivity assumptions remain explicit. Convolutional Hebbian networks demonstrate that competitive local learning can support visual classification, but their performance is coupled to exa...
Patrick Inoue, F. Röhrbein, Andreas Knoblauch· Frontiers in Computational N...· 0 citations
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