Artificial Neural Networks

Stefan Mihalas

Biological systems learn differently than current machine learning systems, with generally higher sample efficiency but also strong inductive biases. The scientist will explore the effects which bio-realistic neurons, plasticity rules and architectures have on learning in artificial neural networks. This will be done by combining construction of artificial neural network with bio-inspired constraints.