Galantamine: Effect on nicotinic receptor binding, acetylcholinesterase inhibition, and learning

Classical eyeblink conditioning is a well-characterized model paradigm that engages the septohippocampal cholinergic system. This form of associative learning is impaired in normal aging and severely disrupted in Alzheimer's disease (AD). Some nicotinic cholinergic receptor subtypes are lost in AD, making the use of nicotinic allosterically potentiating ligands a promising therapeutic strategy. The allosterically potentiating ligand galantamine (Gal) modulates nicotinic cholinergic receptors to increase acetylcholine release as well as acting as an acetylcholinesterase (AChE) inhibitor. Gal was tested in two preclinical experiments. In Experiment 1 with 16 young and 16 older rabbits, Gal (3.0 mg/kg) was administered for 15 days during conditioning, and the drug significantly improved learning, reduced AChE levels, and increased nicotinic receptor binding. In Experiment 2, 53 retired breeder rabbits were tested over a 15-wk period in four conditions. Groups of rabbits received 0.0 (vehicle), 1.0, or 3.0 mg/kg Gal for the entire 15-wk period or 3.0 mg/kg Gal for 15 days and vehicle for the remainder of the experiment. Fifteen daily conditioning sessions and subsequent retention and relearning assessments were spaced at 1-month intervals. The dose of 3.0 mg/kg Gal ameliorated learning deficits significantly during acquisition and retention in the group receiving 3.0 mg/kg Gal continuously. Nicotinic receptor binding was significantly increased in rabbits treated for 15 days with 3.0 mg/kg Gal, and all Gal-treated rabbits had lower levels of brain AChE. The efficacy of Gal in a learning paradigm severely impaired in AD is consistent with outcomes in clinical studies.

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.