OmniPlex Neural Data Acquisition System 在体多通道神经信号记录分析系统是在在体神经电生理研究领域的的旗舰产品。出色的前端放大器、灵活直观的控制软件再结合专门为进行细胞外记录的神经学家设计的记录系统,使得OmniPlex系统成为目前市场上最先进的神经数据采集系统和在线spikesorting系统。
OmniPlex 系统是一个紧凑、灵活的高性能数据采集平台和强大的在线spike排序平台。它提供低延迟,优越的共模抑制放大,并以其使用简易而闻名。OmniPlex 系统是由OmniPlex chassis、OmniPlex 软件组成,以及新的OmniPlex 系统,Digital Headstages Processor (DHP) 系统-如下所述。OmniPlex 系统的其他版本可以使用OmniAmp或DigiAmp A/D 设备。


Publications文献资料:
1. Cui, G., et al., Concurrent activation of striatal direct and indirect pathways during action initiation. Nature, 2013. 494(7436): p. 238-42
2. Chapin, J.K., et al, Real-time control of a robot arm using simultane-ously recorded neurons in the motor cortex. Nat Neurosci, 1999. 2(7):p.664-70
3. Wessberg, J, et al, Real-time prediction of hand trajectory byensembles of cortical neurons in primates. Nature, 2000. 408(6810): p361-5
4. Laubach, M, et al., Cortical ensemble activity increasingly predictsbehaviour outcomes during learning of a motor task. Nature, 2000.405(6786): p.567-71
5. Wallis, J.D., et al., Single neurons in prefrontal cortex encode abstractrules.Nature, 2001.411(6840): p.953-6
6. Fries, P, et al., Modulation of oscillatory neuronal synchronization byselective visual attention.Science, 2001. 291(5508): p.1560-3
7. Taylor, D.M.. et al.. Direct Cortical Control of 3D NeuroprostheticDevices.Science, 2002.296(5574): p.1829 -1832
8. Carmena, J.M., et al., Learning to control a brain-machine interface forreaching and grasping by primates. PLoSBiol, 2003. 1(2): p.E42
9. Nicolelis, M.A., et al., Chronic, multisite, multielectrode recordings inmacaque monkeys. ProcNatlAcadSci U S A, 2003.100(19): p.11041-6
10.Hasegawa, R.P,et al., Prefrontal neurons coding suppression ofspecific saccades.Neuron, 2004.43(3): p.415-25
11.Musallam, S., et al., Cognitive control signals for neural prostheticsScience,2004.305(5681): p.258-62
12.0lson, B.P, et al., Closed-loop cortical control of direction usincsupport vector machines. IEEE Trans Neural SystRehabilEng, 2005. 13(1): p.72-80
13.Gage, G.J., et al., Naive coadaptive cortical control. J Neural Eng, 20052(2): p.52-63
14. Lebedev, M.A., et al, Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. .Neurosci, 2005.25(19): p.4681-93
15.Smirnakis, S.M., et al., Lack of long-term cortical reorganization aftermacaque retinal lesions.Nature, 2005.435(7040): p.300-7
16.Bichot, N.P. et al., Parallel and serial neural mechanisms for visuasearch in macaque area V4.Science, 2005.308(5721): p.529-34
17. Paton, J.J., et al., The primate amygdala represents the positive andnegative value of visual stimuli during learning. Nature, 2006. 439(7078): p.865-70
18. Butts, D.A., et al., Temporal precision in the neural code and the timescales of natural vision.Nature, 2007.449(7158): p.92-5
19.Herry, C., et al, Switching on and off fear by distinct neuronal circuitsNature, 2008.454(7204): p.600-6
20.Tye, K.M., et al., Rapid strengthening of thalamo-amygdala synapsesmediates cue-reward learning.Nature, 2008.453(7199): p.1253-7
|