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10.1038/srep14149 http://scihub22266oqcxt.onion/10.1038/srep14149 C4570975!4570975!26374672     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       Sci+Rep 2015 ; 5 (ä): ä Nephropedia Template TP {{tp|p=26374672|t=2015. Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity |pdf=|usr=}}{{26374672}} gab.com Text Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26374672 #FOAvip An artificial neural network, such as a Boltzmann machine, can be trained with the Hebb rule so that it stores static patterns and retrieves a particular pattern when an associated cue is presented to it. Such a network, however, cannot effectively deal with dynamic patterns in the manner of living creatures. Here, we design a dynamic Boltzmann machine (DyBM) and a learning rule that has some of the properties of spike-timing dependent plasticity (STDP), which has been postulated for biological neural networks. We train a DyBM consisting of only seven neurons in a way that it memorizes the sequence of the bitmap patterns in an alphabetical image ?SCIENCE? and its reverse sequence and retrieves either sequence when a partial sequence is presented as a cue. The DyBM is to STDP as the Boltzmann machine is to the Hebb rule. Twit Text FOAVip Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26374672 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26374672 #FOAvip English Wikipedia <ref>{{Cite pmid|26374672}}</ref> Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity #MMPMID26374672 Osogami T; Otsuka M Sci Rep 2015[]; 5 (ä): ä PMID26374672show ga An artificial neural network, such as a Boltzmann machine, can be trained with the Hebb rule so that it stores static patterns and retrieves a particular pattern when an associated cue is presented to it. Such a network, however, cannot effectively deal with dynamic patterns in the manner of living creatures. Here, we design a dynamic Boltzmann machine (DyBM) and a learning rule that has some of the properties of spike-timing dependent plasticity (STDP), which has been postulated for biological neural networks. We train a DyBM consisting of only seven neurons in a way that it memorizes the sequence of the bitmap patterns in an alphabetical image ?SCIENCE? and its reverse sequence and retrieves either sequence when a partial sequence is presented as a cue. The DyBM is to STDP as the Boltzmann machine is to the Hebb rule. äSeven neurons memorizing sequences of alphabetical images via spike-ti(epmc).pdf DeepDyve Pubget Overpricing