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10.12688/f1000research.7387.1 http://scihub22266oqcxt.onion/10.12688/f1000research.7387.1 C4752021!4752021!26937278     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       F1000Res 2016 ; 5 (ä): ä Nephropedia Template TP {{tp|p=26937278|t=2016. Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation |pdf=|usr=}}{{26937278}} gab.com Text Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation JO: F1000Res http://www.kidney.de/pget.php?&tw=1&pmid=26937278 #FOAvip Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external inputs may also be encoded by low-dimensional CANNs embedded in the high-dimensional space of neural population activity. The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses, which is a correlation structure associated with the unique dynamics of CANNs. This body of evidence, which is reviewed in this report, suggests that CANNs may serve as a canonical model for neural information representation. Twit Text FOAVip Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation ????F1000Res http://www.kidney.de/pget.php?&tw=1&pmid=26937278 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26937278 #FOAvip English Wikipedia <ref>{{Cite pmid|26937278}}</ref> Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation #MMPMID26937278 Wu S; Wong KYM; Fung CCA; Mi Y; Zhang W F1000Res 2016[]; 5 (ä): ä PMID26937278show ga Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external inputs may also be encoded by low-dimensional CANNs embedded in the high-dimensional space of neural population activity. The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses, which is a correlation structure associated with the unique dynamics of CANNs. This body of evidence, which is reviewed in this report, suggests that CANNs may serve as a canonical model for neural information representation. äContinuous Attractor Neural Networks: Candidate of a Canonical Model f(epmc).pdf DeepDyve Pubget Overpricing