Objective To characterize the function of a new xanomeline-derived M 1 agonist, 3-[3-（3-florophenyl-2-propyn- 1- ylthio）-1,2,5-thiadiazol-4-yl]-1,2,5,6- tetrahydro-1-methylpyridine Oxalate （EUK1001）, the acute tox...Objective To characterize the function of a new xanomeline-derived M 1 agonist, 3-[3-（3-florophenyl-2-propyn- 1- ylthio）-1,2,5-thiadiazol-4-yl]-1,2,5,6- tetrahydro-1-methylpyridine Oxalate （EUK1001）, the acute toxicity and the effects on synaptic plasticity and cognition of EUK1001 were evaluated. Methods To examine the median lethal dose （LD50） of EUK1001, a wide dose range of EUK1001 was administered by p.o. and i.p. in aged mice. Furthermore, novel object recognition task and in vitro electrophysiological technique were utilized to investigate the effects of EUK1001 on recognition memory and hippocampal synaptic plasticity in aged mice. Results EUK1001 exhibited lower toxicity than xanomeline, and improved the performance of aged mice in the novel object recognition test. In addition, bath application of 1 μmol/L EUK1001 directly induced long-term potentiation in the hippocampus slices. Conclusion We conclude that EUK1001 can improve the agerelated cognitive deficits.展开更多
An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical re...An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.展开更多
基金the National Basic Research Development Program of China(No. 2003CB716605)National Natural Science Fundation ofChina (No. 30470711, No. 30670682)a grant from Shang-hai Science and Technology Commission (No. 05DJ14007)
文摘Objective To characterize the function of a new xanomeline-derived M 1 agonist, 3-[3-（3-florophenyl-2-propyn- 1- ylthio）-1,2,5-thiadiazol-4-yl]-1,2,5,6- tetrahydro-1-methylpyridine Oxalate （EUK1001）, the acute toxicity and the effects on synaptic plasticity and cognition of EUK1001 were evaluated. Methods To examine the median lethal dose （LD50） of EUK1001, a wide dose range of EUK1001 was administered by p.o. and i.p. in aged mice. Furthermore, novel object recognition task and in vitro electrophysiological technique were utilized to investigate the effects of EUK1001 on recognition memory and hippocampal synaptic plasticity in aged mice. Results EUK1001 exhibited lower toxicity than xanomeline, and improved the performance of aged mice in the novel object recognition test. In addition, bath application of 1 μmol/L EUK1001 directly induced long-term potentiation in the hippocampus slices. Conclusion We conclude that EUK1001 can improve the agerelated cognitive deficits.
文摘An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.
