dc.contributor.author | Favorov, Oleg V. | |
dc.contributor.author | Kursun, Olcay | |
dc.date.accessioned | 2021-03-06T07:48:29Z | |
dc.date.available | 2021-03-06T07:48:29Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Favorov O. V. , Kursun O., "Neocortical layer 4 as a pluripotent function linearizer", JOURNAL OF NEUROPHYSIOLOGY, cilt.105, ss.1342-1360, 2011 | |
dc.identifier.issn | 0022-3077 | |
dc.identifier.other | av_ddfd596f-7b8f-4296-8d4c-ae5651b8021a | |
dc.identifier.other | vv_1032021 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12627/146246 | |
dc.identifier.uri | https://doi.org/10.1152/jn.00708.2010 | |
dc.description.abstract | Favorov OV, Kursun O. Neocortical layer 4 as a pluripotent function linearizer. J Neurophysiol 105: 1342-1360, 2011. First published January 19, 2011; doi:10.1152/jn.00708.2010.-A highly effective kernel-based strategy used in machine learning is to transform the input space into a new "feature" space where nonlinear problems become linear and more readily solvable with efficient linear techniques. We propose that a similar "problem-linearization" strategy is used by the neocortical input layer 4 to reduce the difficulty of learning nonlinear relations between the afferent inputs to a cortical column and its to-be-learned upper layer outputs. The key to this strategy is the presence of broadly tuned feed-forward inhibition in layer 4: it turns local layer 4 domains into functional analogs of radial basis function networks, which are known for their universal function approximation capabilities. With the use of a computational model of layer 4 with feed-forward inhibition and Hebbian afferent connections, self-organized on natural images to closely match structural and functional properties of layer 4 of the cat primary visual cortex, we show that such layer-4-like networks have a strong intrinsic tendency to perform input transforms that automatically linearize a broad repertoire of potential nonlinear functions over the afferent inputs. This capacity for pluripotent function linearization, which is highly robust to variations in network parameters, suggests that layer 4 might contribute importantly to sensory information processing as a pluripotent function linearizer, performing such a transform of afferent inputs to a cortical column that makes it possible for neurons in the upper layers of the column to learn and perform their complex functions using primarily linear operations. | |
dc.language.iso | eng | |
dc.subject | Biyokimya | |
dc.subject | Fizyoloji | |
dc.subject | Yaşam Bilimleri | |
dc.subject | Temel Bilimler | |
dc.subject | Sağlık Bilimleri | |
dc.subject | Temel Tıp Bilimleri | |
dc.subject | Tıp | |
dc.subject | Biyoloji ve Biyokimya | |
dc.subject | FİZYOLOJİ | |
dc.subject | Yaşam Bilimleri (LIFE) | |
dc.subject | Sinirbilim ve Davranış | |
dc.subject | NEUROSCIENCES | |
dc.title | Neocortical layer 4 as a pluripotent function linearizer | |
dc.type | Makale | |
dc.relation.journal | JOURNAL OF NEUROPHYSIOLOGY | |
dc.contributor.department | University Of North Carolina At Asheville , , | |
dc.identifier.volume | 105 | |
dc.identifier.issue | 3 | |
dc.identifier.startpage | 1342 | |
dc.identifier.endpage | 1360 | |
dc.contributor.firstauthorID | 74452 | |