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10.1093/cercor/bhw395 http://scihub22266oqcxt.onion/10.1093/cercor/bhw395 C5903417!5903417!28031176     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       Cereb+Cortex 2017 ; 27 (1): 254-64 Nephropedia Template TP {{tp|p=28031176|t=2017. Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex |pdf=|usr=}}{{28031176}} gab.com Text Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex JO: Cereb Cortex http://www.kidney.de/pget.php?&tw=1&pmid=28031176 #FOAvip How does the cortex combine information from multiple sources? We tested several computational models against data from steady-state electroencephalography (EEG) experiments in humans, using periodic visual stimuli combined across either retinal location or eye-of-presentation. A model in which signals are raised to an exponent before being summed in both the numerator and the denominator of a gain control nonlinearity gave the best account of the data. This model also predicted the pattern of responses in a range of additional conditions accurately and with no free parameters, as well as predicting responses at harmonic and intermodulation frequencies between 1 and 30 Hz. We speculate that this model implements the optimal algorithm for combining multiple noisy inputs, in which responses are proportional to the weighted sum of both inputs. This suggests a novel purpose for cortical gain control: implementing optimal signal combination via mutual inhibition, perhaps explaining its ubiquity as a neural computation. Twit Text FOAVip Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex ????Cereb Cortex http://www.kidney.de/pget.php?&tw=1&pmid=28031176 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28031176 #FOAvip English Wikipedia <ref>{{Cite pmid|28031176}}</ref> Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex #MMPMID28031176 Baker DH; Wade AR Cereb Cortex 2017[Jan]; 27 (1): 254-64 PMID28031176show ga How does the cortex combine information from multiple sources? We tested several computational models against data from steady-state electroencephalography (EEG) experiments in humans, using periodic visual stimuli combined across either retinal location or eye-of-presentation. A model in which signals are raised to an exponent before being summed in both the numerator and the denominator of a gain control nonlinearity gave the best account of the data. This model also predicted the pattern of responses in a range of additional conditions accurately and with no free parameters, as well as predicting responses at harmonic and intermodulation frequencies between 1 and 30 Hz. We speculate that this model implements the optimal algorithm for combining multiple noisy inputs, in which responses are proportional to the weighted sum of both inputs. This suggests a novel purpose for cortical gain control: implementing optimal signal combination via mutual inhibition, perhaps explaining its ubiquity as a neural computation. äEvidence for an Optimal Algorithm Underlying Signal Combination in Hum(epmc).pdf DeepDyve Pubget Overpricing