Nonlinear Hebbian learning as a unifying principle in receptive field formation

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely Nonlinear Hebbian Learning. When Nonlinear Hebbian Learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities

Energy and volume of vector fields on spherical domains

We present in this paper a \boundary version" for theorems about minimality of volume and energy functionals on a spherical domain of threedimensional Euclidean sphere

Structure and dynamics in glass-formers: predictability at large length scales

Dynamic heterogeneity in glass-formers has been related to their static structure using the concept of dynamic propensity. We re-examine this relationship by analyzing dynamical fluctuations in two atomistic glass-formers and two theoretical models. We introduce quantitative statistical indicators which show that the dynamics of individual particles cannot be predicted on the basis of the propensity, nor by any structural indicator. However, the spatial structure of the propensity field does have predictive power for the spatial correlations associated with dynamic heterogeneity. Our results suggest that the quest for a connection between static and dynamic properties of glass-formers at the particle level is vain, but they demonstrate that such connection does exist on larger length scales.Comment: 7 pages; 4 figs - Extended, clarified versio

Light clusters and the pasta phase

The effects of including light clusters in nuclear matter at low densities are investigated within four different parametrizations of relativistic models at finite temperature. Both homogeneous and inhomogeneous matter (pasta phase) are described for neutral nuclear matter with fixed proton fractions. We discuss the effect of the density dependence of the symmetry energy, the temperature and the proton fraction on the non-homogeneous matter forming the inner crust of proto-neutron stars. It is shown that the number of nucleons in the clusters, the cluster proton fraction and the sizes of the Wigner Seitz cell and of the cluster are very sensitive to the density dependence of the symmetry energy.Comment: 14 pages, 14 figures; Accepted for publication in Phys. Rev.