University of Amsterdam

About: University of Amsterdam is a education organization based out in Amsterdam, Noord-Holland, Netherlands. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 59309 authors who have published 140894 publications receiving 5984137 citations. The organization is also known as: UvA & Universiteit van Amsterdam.

Replay of rule-learning related neural patterns in the prefrontal cortex during sleep

Abstract: Slow-wave sleep (SWS) is important for memory consolidation. During sleep, neural patterns reflecting previously acquired information are replayed. One possible reason for this is that such replay exchanges information between hippocampus and neocortex, supporting consolidation. We recorded neuron ensembles in the rat medial prefrontal cortex (mPFC) to study memory trace reactivation during SWS following learning and execution of cross-modal strategy shifts. In general, reactivation of learning-related patterns occurred in distinct, highly synchronized transient bouts, mostly simultaneous with hippocampal sharp wave/ripple complexes (SPWRs), when hippocampal ensemble reactivation and cortico-hippocampal interaction is enhanced. During sleep following learning of a new rule, mPFC neural patterns that appeared during response selection replayed prominently, coincident with hippocampal SPWRs. This was learning dependent, as the patterns appeared only after rule acquisition. Therefore, learning, or the resulting reliable reward, influenced which patterns were most strongly encoded and successively reactivated in the hippocampal/prefrontal network.

Job satisfaction of older workers

Abstract: Using data for The Netherlands, this paper analyzes the relation between allocation, wages and job satisfaction. Five conclusions emerge from the empirical analysis: satisfaction with the job content is the main factor explaining overall job satisfaction; the effects of individual and job characteristics on job satisfaction differ by the aspect of the job considered; the response to a general question on job satisfaction differs from the response to questions on satisfaction with different aspects of the job; it is relevant to consider the joint relation between wages and job satisfaction; and skill mismatches do not seem to affect job satisfaction.

Learning Sparse Neural Networks through L_0 Regularization

Abstract: We propose a practical method for L0 norm regularization for neural networks:pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of L0 regularization. However, since the L0 norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly,for certain distributions over the gates, the expected L0 regularized objective is differentiable with respect to the distribution parameters. We further propose the hard concrete distribution for the gates, which is obtained by “stretching” a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer.