Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation
Identifieur interne : 000256 ( Main/Exploration ); précédent : 000255; suivant : 000257Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation
Auteurs : Si Wu [République populaire de Chine] ; K Y Michael Wong [Hong Kong] ; C C Alan Fung [Japon] ; Yuanyuan Mi [République populaire de Chine] ; Wenhao Zhang [République populaire de Chine, Hong Kong]Source :
- F1000Research [ 2046-1402 ] ; 2016.
Abstract
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.
Url:
DOI: 10.12688/f1000research.7387.1
PubMed: 26937278
PubMed Central: 4752021
Affiliations:
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Beijing Normal University, Beijing, 100875, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875</wicri:regionArea><wicri:noRegion>100875</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="a2">Department of Physics, Hong Kong University of Science & Technology, Clear Water Bay Peninsula, Hong Kong</nlm:aff><country xml:lang="fr">Hong Kong</country><wicri:regionArea>Department of Physics, Hong Kong University of Science & Technology, Clear Water Bay Peninsula</wicri:regionArea><wicri:noRegion>Clear Water Bay Peninsula</wicri:noRegion></affiliation></author></analytic><series><title level="j">F1000Research</title><idno type="eISSN">2046-1402</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Amari, S" uniqKey="Amari S">S Amari</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ermentrout, B" uniqKey="Ermentrout B">B Ermentrout</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bressloff, Pc" uniqKey="Bressloff P">PC Bressloff</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ben Yishai, R" uniqKey="Ben Yishai R">R Ben-Yishai</name></author><author><name sortKey="Bar Or, Rl" uniqKey="Bar Or R">RL Bar-Or</name></author><author><name sortKey="Sompolinsky, H" uniqKey="Sompolinsky H">H Sompolinsky</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Georgopoulos, Ap" uniqKey="Georgopoulos A">AP Georgopoulos</name></author><author><name sortKey="Taira, M" uniqKey="Taira M">M Taira</name></author><author><name sortKey="Lukashin, A" uniqKey="Lukashin A">A Lukashin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, K" uniqKey="Zhang K">K Zhang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Samsonovich, A" uniqKey="Samsonovich A">A Samsonovich</name></author><author><name sortKey="Mcnaughton, Bl" uniqKey="Mcnaughton B">BL McNaughton</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Deneve, S" uniqKey="Deneve S">S Deneve</name></author><author><name sortKey="Latham, Pe" uniqKey="Latham P">PE Latham</name></author><author><name sortKey="Pouget, A" uniqKey="Pouget A">A Pouget</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wu, S" uniqKey="Wu S">S Wu</name></author><author><name sortKey="Amari, S" uniqKey="Amari S">S Amari</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Compte, A" uniqKey="Compte A">A Compte</name></author><author><name sortKey="Brunel, N" uniqKey="Brunel N">N Brunel</name></author><author><name sortKey="Goldman Rakic, Ps" uniqKey="Goldman Rakic P">PS Goldman-Rakic</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wang, Xj" uniqKey="Wang X">XJ Wang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Trappenberg, Tp" uniqKey="Trappenberg T">TP Trappenberg</name></author><author><name sortKey="Dorris, Mc" uniqKey="Dorris M">MC Dorris</name></author><author><name sortKey="Munoz, Dp" uniqKey="Munoz D">DP Munoz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hopfield, Jj" uniqKey="Hopfield J">JJ Hopfield</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fung, Cc" uniqKey="Fung C">CC Fung</name></author><author><name sortKey="Wong, Ky" uniqKey="Wong K">KY Wong</name></author><author><name sortKey="Wu, S" uniqKey="Wu S">S Wu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wu, S" uniqKey="Wu S">S Wu</name></author><author><name sortKey="Hamaguchi, K" uniqKey="Hamaguchi K">K Hamaguchi</name></author><author><name sortKey="Amari, S" uniqKey="Amari S">S Amari</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nijhawan, R" uniqKey="Nijhawan R">R Nijhawan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fung, Cc" uniqKey="Fung C">CC Fung</name></author><author><name sortKey="Wong, Ky" uniqKey="Wong K">KY Wong</name></author><author><name sortKey="Mao, H" uniqKey="Mao H">H Mao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mi, Y" uniqKey="Mi Y">Y Mi</name></author><author><name sortKey="Fung, Ca" uniqKey="Fung C">CA Fung</name></author><author><name sortKey="Wong, Kym" uniqKey="Wong K">KYM Wong</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fung, Cc" uniqKey="Fung C">CC Fung</name></author><author><name sortKey="Wong, Ky" uniqKey="Wong K">KY Wong</name></author><author><name sortKey="Wang, H" uniqKey="Wang H">H Wang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, W" uniqKey="Zhang W">W Zhang</name></author><author><name sortKey="Wu, S" uniqKey="Wu S">S Wu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="S Fard, F" uniqKey="S Fard F">F S Fard</name></author><author><name sortKey="Hollensen, P" uniqKey="Hollensen P">P Hollensen</name></author><author><name sortKey="Heinke, D" uniqKey="Heinke D">D Heinke</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Goodridge, Jp" uniqKey="Goodridge J">JP Goodridge</name></author><author><name sortKey="Touretzky, Ds" uniqKey="Touretzky D">DS Touretzky</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ernst, Mo" uniqKey="Ernst M">MO Ernst</name></author><author><name sortKey="Banks, Ms" uniqKey="Banks M">MS Banks</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, W" uniqKey="Zhang W">W Zhang</name></author><author><name sortKey="Wu, S" uniqKey="Wu S">S Wu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ponce Alvarez, A" uniqKey="Ponce Alvarez A">A Ponce-Alvarez</name></author><author><name sortKey="Thiele, A" uniqKey="Thiele A">A Thiele</name></author><author><name sortKey="Albright, Td" uniqKey="Albright T">TD Albright</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wimmer, K" uniqKey="Wimmer K">K Wimmer</name></author><author><name sortKey="Nykamp, Dq" uniqKey="Nykamp D">DQ Nykamp</name></author><author><name sortKey="Constantinidis, C" uniqKey="Constantinidis C">C Constantinidis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Logothetis, Nk" uniqKey="Logothetis N">NK Logothetis</name></author><author><name sortKey="Pauls, J" uniqKey="Pauls J">J Pauls</name></author><author><name sortKey="Bulthoff, Hh" uniqKey="Bulthoff H">HH Bülthoff</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mante, V" uniqKey="Mante V">V Mante</name></author><author><name sortKey="Sussillo, D" uniqKey="Sussillo D">D Sussillo</name></author><author><name sortKey="Shenoy, Kv" uniqKey="Shenoy K">KV Shenoy</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Hong Kong</li><li>Japon</li><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Wu, Si" sort="Wu, Si" uniqKey="Wu S" first="Si" last="Wu">Si Wu</name></noRegion><name sortKey="Mi, Yuanyuan" sort="Mi, Yuanyuan" uniqKey="Mi Y" first="Yuanyuan" last="Mi">Yuanyuan Mi</name><name sortKey="Zhang, Wenhao" sort="Zhang, Wenhao" uniqKey="Zhang W" first="Wenhao" last="Zhang">Wenhao Zhang</name></country><country name="Hong Kong"><noRegion><name sortKey="Wong, K Y Michael" sort="Wong, K Y Michael" uniqKey="Wong K" first="K Y Michael" last="Wong">K Y Michael Wong</name></noRegion><name sortKey="Zhang, Wenhao" sort="Zhang, Wenhao" uniqKey="Zhang W" first="Wenhao" last="Zhang">Wenhao Zhang</name></country><country name="Japon"><noRegion><name sortKey="Fung, C C Alan" sort="Fung, C C Alan" uniqKey="Fung C" first="C C Alan" last="Fung">C C Alan Fung</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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