Spatial behavior and linguistic representation: Collaborative interdisciplinary specialized workshop

The Collaborative Interdisciplinary Specialized Workshop on Spatial Behavior and Linguistic Representation took place on April 23–24, 2010, at the Hanse-Wissenschaftskolleg, Institute for Advanced Study (HWK), in Delmenhorst, Germany. We report the scientific motivation for this workshop and report its outcomes together with the impact of a gathering of this kind for the scientific community

Collision avoidance in persons with homonymous visual field defects under virtual reality conditions

AbstractThe aim of the present study was to examine the effect of homonymous visual field defects (HVFDs) on collision avoidance of dynamic obstacles at an intersection under virtual reality (VR) conditions. Overall performance was quantitatively assessed as the number of collisions at a virtual intersection at two difficulty levels. HVFDs were assessed by binocular semi-automated kinetic perimetry within the 90° visual field, stimulus III4e and the area of sparing within the affected hemifield (A-SPAR in deg2) was calculated. The effect of A-SPAR, age, gender, side of brain lesion, time since brain lesion and presence of macular sparing on the number of collisions, as well as performance over time were investigated. Thirty patients (10 female, 20 male, age range: 19–71years) with HVFDs due to unilateral vascular brain lesions and 30 group-age-matched subjects with normal visual fields were examined. The mean number of collisions was higher for patients and in the more difficult level they experienced more collisions with vehicles approaching from the blind side than the seeing side. Lower A-SPAR and increasing age were associated with decreasing performance. However, in agreement with previous studies, wide variability in performance among patients with identical visual field defects was observed and performance of some patients was similar to that of normal subjects. Both patients and healthy subjects displayed equal improvement of performance over time in the more difficult level. In conclusion, our results suggest that visual-field related parameters per se are inadequate in predicting successful collision avoidance. Individualized approaches which also consider compensatory strategies by means of eye and head movements should be introduced

Miniature curved artificial compound eyes.

International audienceIn most animal species, vision is mediated by compound eyes, which offer lower resolution than vertebrate single-lens eyes, but significantly larger fields of view with negligible distortion and spherical aberration, as well as high temporal resolution in a tiny package. Compound eyes are ideally suited for fast panoramic motion perception. Engineering a miniature artificial compound eye is challenging because it requires accurate alignment of photoreceptive and optical components on a curved surface. Here, we describe a unique design method for biomimetic compound eyes featuring a panoramic, undistorted field of view in a very thin package. The design consists of three planar layers of separately produced arrays, namely, a microlens array, a neuromorphic photodetector array, and a flexible printed circuit board that are stacked, cut, and curved to produce a mechanically flexible imager. Following this method, we have prototyped and characterized an artificial compound eye bearing a hemispherical field of view with embedded and programmable low-power signal processing, high temporal resolution, and local adaptation to illumination. The prototyped artificial compound eye possesses several characteristics similar to the eye of the fruit fly Drosophila and other arthropod species. This design method opens up additional vistas for a broad range of applications in which wide field motion detection is at a premium, such as collision-free navigation of terrestrial and aerospace vehicles, and for the experimental testing of insect vision theories

Acquisition vs. Memorization Trade-Offs Are Modulated by Walking Distance and Pattern Complexity in a Large-Scale Copying Paradigm

In a “block-copying paradigm”, subjects were required to copy a configuration of colored blocks from a model area to a distant work area, using additional blocks provided at an equally distant resource area. Experimental conditions varied between the inter-area separation (walking distance) and the complexity of the block patterns to be copied. Two major behavioral strategies were identified: in the memory-intensive strategy, subjects memorize large parts of the pattern and rebuild them without intermediate visits at the model area. In the acquisition-intensive strategy, subjects memorize one block at a time and return to the model after having placed this block. Results show that the frequency of the memory-intensive strategy is increased for larger inter-area separations (larger walking distances) and for simpler block patterns. This strategy-shift can be interpreted as the result of an optimization process or trade-off, minimizing combined, condition-dependent costs of the two strategies. Combined costs correlate with overall response time. We present evidence that for the memory-intensive strategy, costs correlate with model visit duration, while for the acquisition-intensive strategy, costs correlate with inter-area transition (i.e., walking) times

Spatial Cognition: Behavioral Competences, Neural Mechanisms and Evolutionary Scaling

. Spatial cognition is a cognitive ability that arose relatively early in animal evolution. It is therefore very well suited for studying the evolution from stereotyped to cognitive behavior and the general mechanisms underlying cognitive abilities. In this paper, I will present a definition of cognition in terms of the complexity of behavior it subserves. This approach allows to ask for the mechanisms of cognition, just as the mechanisms of simpler behavior have been addressed in neuroethology. As an example for this mechanistic view of cognitive abilities, I will discuss the view--graph theory of cognitive maps. I will argue that spatial cognitive abilities can be explained by scaling up simple, stereotyped mechanisms of spatial behavior. This evolutionary view of cognition is supported by two types of empirical evidence: Robot experiments show that the simple mechanisms are in fact sufficient to produce cognitive behavior while behavioral experiments with subjects exploring a comput..

Spatial Cognition: Behavioral Competences, Neural Mechanisms and Evolutionary Scaling

. Spatial cognition is a cognitive ability that arose relatively early in animal evolution. It is therefore very well suited for studying the evolution from stereotyped to cognitive behavior and the general mechanisms underlying cognitive abilities. In this paper, I will present a definition of cognition in terms of the complexity of behavior it subserves. This approach allows to ask for the mechanisms of cognition, just as the mechanisms of simpler behavior have been addressed in neuroethology. As an example for this mechanistic view of cognitive abilities, I will discuss the view--graph theory of cognitive maps. I will argue that spatial cognitive abilities can be explained by scaling up simple, stereotyped mechanisms of spatial behavior. This evolutionary view of cognition is supported by two types of empirical evidence: Robot experiments show that the simple mechanisms are in fact sufficient to produce cognitive behavior while behavioral experiments with subjects exploring a comput..

Computational neuroscience: a first course

Computational Neuroscience - A First Course provides an essential introduction to computational neuroscience and  equips readers with a fundamental understanding of modeling the nervous system at the membrane, cellular, and network level. The book, which grew out of a lecture series held regularly for more than ten years to graduate students in neuroscience with backgrounds in biology, psychology and medicine, takes its readers on a journey through three fundamental domains of computational neuroscience: membrane biophysics, systems theory and artificial neural networks. The required mathematical concepts are kept as intuitive and simple as possible throughout the book, making it fully accessible to readers who are less familiar with mathematics. Overall, Computational Neuroscience - A First Course represents an essential reference guide for all neuroscientists who use computational methods in their daily work, as well as for any theoretical scientist approaching the field of computational neuroscience