by Professor Johannes Schmidt-Hieber,Link opens in a new window (University of Twente)

Abstract: Compared to artificial neural networks (ANNs), the brain learns faster, generalizes better to new situations and consumes much less energy. ANNs are motivated by the functioning of the brain, but differ in several crucial aspects. For instance, ANNs are deterministic while biological neural networks (BNNs) are stochastic. Moreover, it is biologically implausible that the learning of the brain is based on gradient descent. In this talk we look at biological neural networks as a statistical method for supervised learning. We relate the local updating rule of the connection parameters in BNNs to a zero-order optimization method and derive some first statistical risk bounds.