TY - GEN
T1 - Symmetry and self-organization of the oculomotor neural integrator
AU - Anastasio, Thomas J.
PY - 1997
Y1 - 1997
N2 - Modeling and experiment support the assertion that the oculomotor neural integrator works through positive feedback via reciprocal inhibition. Clinical studies reveal that the integrator is operational immediately after birth, suggesting that it develops prenataily. Using facts and computational techniques from dynamic systems theory, it can be shown that self-organization of the oculomotor neural integrator could occur through the construction during development of symmetrically connected clusters of neurons. This method is neurobiologically plausible and robust, and networks designed using it reproduce the dynamics observed for brainstem neurons that are part of the actual oculomotor neural integrator.
AB - Modeling and experiment support the assertion that the oculomotor neural integrator works through positive feedback via reciprocal inhibition. Clinical studies reveal that the integrator is operational immediately after birth, suggesting that it develops prenataily. Using facts and computational techniques from dynamic systems theory, it can be shown that self-organization of the oculomotor neural integrator could occur through the construction during development of symmetrically connected clusters of neurons. This method is neurobiologically plausible and robust, and networks designed using it reproduce the dynamics observed for brainstem neurons that are part of the actual oculomotor neural integrator.
UR - http://www.scopus.com/inward/record.url?scp=84902184904&partnerID=8YFLogxK
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U2 - 10.1007/bfb0032469
DO - 10.1007/bfb0032469
M3 - Conference contribution
AN - SCOPUS:84902184904
SN - 3540630473
SN - 9783540630470
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 116
EP - 123
BT - Biological and Artificial Computation
PB - Springer
T2 - 4th International Work-Conference on Artificial and Natural Neural Networks, IWANN 1997
Y2 - 4 June 1997 through 6 June 1997
ER -