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Andreas Schierwagen

Publications and source records attributed to Andreas Schierwagen.

2 recordsLinked to original sources

On Reverse Engineering in the Cognitive and Brain Sciences

Various research initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms and artificial cognitive systems. This paper reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Actually, recent findings in the experimental and theoretical fields, question central assumptions and hypotheses made for reverse engineering. Using the modeling relation as analyzed by Robert Rosen, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e. complex cognitive systems can be analyzed, understood and duplicated by reverse engineering, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed.

nlin.AO↗

Active percolation analysis of pyramidal neurons of somatosensory cortex: A comparison of wildtype and p21H-ras^Val12 transgenic mice

This article describes the investigation of morphological variations among two set of neuronal cells, namely a control group of wild type rat cells and a group of cells of a trangenic line. Special attention is given to sigular points in the neuronal structure, namely the branching points and extremities of the dendritic processes. The characterization of the spatial distribution of such points is obtained by using a recently reported morphological technique based on forced percolation and window-size compensation, which is particularly suited to the analysis of scattered points presenting several coexisting densities. Different dispersions were identified in our statistical analysis, suggesting that the transgenic line of neurons is characterized by a more pronounced morphological variation. A classification scheme based on a canonical discriminant function was also considered in order to identify the morphological differences.

q-bio.NC↗