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Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time(More)
We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the(More)
In this study we investigate temporal slowness as a learning principle for receptive fields using slow feature analysis, a new algorithm to determine functions that extract slowly varying signals from the input data. We find a good qualitative and quantitative match between the set of learned functions trained on image sequences and the population of(More)
The system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Diierent poses are represented by diier-ent graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of(More)
Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here,(More)
We present a neural system for the recognition of objects from realistic images, together with results of tests of face recognition from a large gallery. The system is inherently invariant with respect to shift, and is robust against many other variations, most notably rotation in depth and deformation. The system is based on Dynamic Link Matching. It(More)
'Function' is the key criterion for determining whether adult neurogenesis - be it endogenous, induced, or after transplantation - is successful and has truly generated new nerve cells. Function, however, is an elusive and problematic term. A satisfying statement of function will require evaluation on the three conceptual levels of cells, networks, and(More)
Hafting et al. (2005) described grid cells in the dorsocaudal region of the medial entorhinal cortex (dMEC). These cells show a strikingly regular grid-like firing-pattern as a function of the position of a rat in an enclosure. Since the dMEC projects to the hippocampal areas containing the well-known place cells, the question arises whether and how the(More)
The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypothesis is that they help the dentate gyrus to avoid the problem of catastrophic interference when adapting to new environments. We assume that(More)
We present a system for recognizing human faces from single images out of a large database containing one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial(More)