Robert Koch, Creation, and the Specificity of Germs

Microbiology is dominated by evolution today. Just look at any text, journal article, or the topics presented at professional scientific meetings. Darwin is dominant. Microbiology is dominated by evolution today. Just look at any text, journal article, or the topics presented at professional scientific meetings. Darwin is dominant. Many argue that “nothing in biology makes sense except in the light of evolution” (Dobzhansky 1973). But it was not always this way. In fact, a review of the major founders of microbiology has shown that they were creationists.1 We would argue that a better idea thanevolution and one of much more practical importance is the germ theory of disease, originally put forth primarily by non-Darwinian biologists (Gillen and Oliver 2009). In our previous article (Gillen and Oliver 2009), we documented these and many other creation and Christian contributions to germ theory. But only recently has it become known that another important microbiology founder, Robert Koch (Fig. 1) and his co-workers were Linnaean creationists in their classification.2 This is due, in part, to additional works of Robert Koch that were translated from German to English. The year 2010 marks the 100thanniversary of his death (died: May 27, 1910). Although Koch and other German microbiologists were fairly secular in their thinking, their acceptance of Darwinian evolution was minimal

Neural Representations for Sensory-Motor Control I: Head-Centered 3-D Target Positions from Opponent Eye Commands

This article describes how corollary discharges from outflow eye movement commands can be transformed by two stages of opponent neural processing into a head-centered representation of 3-D target position. This representation implicitly defines a cyclopean coordinate system whose variables approximate the binocular vergence and spherical horizontal and vertical angles with respect to the observer's head. Various psychophysical data concerning binocular distance perception and reaching behavior are clarified by this representation. The representation provides a foundation for learning head-centered and body-centered invariant representations of both foveated and non-foveated 3-D target positions. It also enables a solution to be developed of the classical motor equivalence problem, whereby many different joint configurations of a redundant manipulator can all be used to realize a desired trajectory in 3-D space.Air Force Office of Scientific Research (URI 90-0175); Defense Advanced Research Projects Agency (AFOSR-90-0083); National Science Foundation (IRI-87-16960, IRI-90-24877

Neural Representations for Sensory-Motor Control, II: Learning a Head-Centered Visuomotor Representation of 3-D Target Position

A neural network model is described for how an invariant head-centered representation of 3-D target position can be autonomously learned by the brain in real time. Once learned, such a target representation may be used to control both eye and limb movements. The target representation is derived from the positions of both eyes in the head, and the locations which the target activates on the retinas of both eyes. A Vector Associative Map, or YAM, learns the many-to-one transformation from multiple combinations of eye-and-retinal position to invariant 3-D target position. Eye position is derived from outflow movement signals to the eye muscles. Two successive stages of opponent processing convert these corollary discharges into a. head-centered representation that closely approximates the azimuth, elevation, and vergence of the eyes' gaze position with respect to a cyclopean origin located between the eyes. YAM learning combines this cyclopean representation of present gaze position with binocular retinal information about target position into an invariant representation of 3-D target position with respect to the head. YAM learning can use a teaching vector that is externally derived from the positions of the eyes when they foveate the target. A YAM can also autonomously discover and learn the invariant representation, without an explicit teacher, by generating internal error signals from environmental fluctuations in which these invariant properties are implicit. YAM error signals are computed by Difference Vectors, or DVs, that are zeroed by the YAM learning process. YAMs may be organized into YAM Cascades for learning and performing both sensory-to-spatial maps and spatial-to-motor maps. These multiple uses clarify why DV-type properties are computed by cells in the parietal, frontal, and motor cortices of many mammals. YAMs are modulated by gating signals that express different aspects of the will-to-act. These signals transform a single invariant representation into movements of different speed (GO signal) and size (GRO signal), and thereby enable YAM controllers to match a planned action sequence to variable environmental conditions.National Science Foundation (IRI-87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-1309

A Self-Organizing Neural Network for Learning a Body-Centered Invariant Representation of 3-D Target Position

This paper describes a self-organizing neural network that rapidly learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets (Bullock, Grossberg, and Guenther, 1993).National Science Foundation (IRI-87-16960, IRI-90-24877); Air Force Office of Scientific Research (F49620-92-J-0499

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI -87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-l309

Sexual segregation in moose: effects of incisor morphology, quality of willows, and foraging behavior

Thesis (M.S.) University of Alaska Fairbanks, 2002Differences in the jaw morphology of Alaskan moose (Alces alces gigas) may relate to sexual segregation. Male Alaskan moose had significantly wider incisor breadths than did females; however, incisor depth did not differ between sexes. Those differences in jaw architecture might relate to the diets of sexes when they are spatially segregated. Moose consume willow (Salix spp.) as a fundamental component of their diet. Smaller-diameter twigs were more digestible, had more protein, and contained less fiber than larger-diameter twigs. Conversely, no relation existed between age of twigs and digestibility. Ruminants may segregate spatially because females competitively exclude males. An experiment on foraging behavior, however, rejected that hypothesis. Nonetheless, females fed more selectively and had higher rates of forage intake than did males. Thus, differences in foraging behavior between the sexes still may relate to sexual segregation