Recent origin of a hominoid-specific splice form of neuropsin, a gene involved in learning and memory

Neuropsin is a secreted-type serine protease involved in learning and memory. The type II splice form of neuropsin is abundantly expressed in the human brain but not in the mouse brain. We sequenced the type II-spliced region of neuropsin gene in humans and representative nonhuman primate species. Our comparative sequence analysis showed that only the hominoid species (humans and apes) have the intact open reading frame of the type II splice form, indicating that the type II neuropsin originated recently in the primate lineage about 18 MYA. Expression analysis using RT-PCR detected abundant expression of the type II form in the frontal lobe of the adult human brain, but no expression was detected in the brains of lesser apes and Old World monkeys, indicating that the type II form of neuropsin only became functional in recent time, and it might contribute to the progressive change of cognitive abilities during primate evolution.

A human-specific mutation leads to the origin of a novel splice form of neuropsin (KLK8), a gene involved in learning and memory

Neuropsin (kallikrein 8, ELKS) is a secreted-type serine protease preferentially expressed in the central nervous system and involved in learning and memory. Its splicing pattern is different in human and mouse, with the longer form (type II) only express

Maze model to study spatial learning and memory in freely moving monkeys

Many types of mazes have been used in cognitive brain research and data obtained from those experiments, especially those from rodents' studies, support the idea that the hippocampus is related to spatial learning and memory. But the results from non-huma

Learning large-scale spatial relationships in a maze and effects of MK-801 on retrieval in the rhesus monkey

Monkeys have strong abilities to remember the visual properties of potential food sources for survival in the nature. The present study demonstrated the first observations of rhesus monkeys learning to solve complex spatial mazes in which routes were guid

Geometrical learning, descriptive geometry, and biomimetic pattern recognition

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.