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» The Recurrent Control Neural Network
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BC
2000
95views more  BC 2000»
13 years 8 months ago
Cerebellar learning of accurate predictive control for fast-reaching movements
Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study...
Jacob Spoelstra, Nicolas Schweighofer, Michael A. ...
GLVLSI
1998
IEEE
124views VLSI» more  GLVLSI 1998»
14 years 23 days ago
Non-Refreshing Analog Neural Storage Tailored for On-Chip Learning
In this research, we devised a new simple technique for statically holding analog weights, which does not require periodic refreshing. It further contains a mechanism to locally u...
Bassem A. Alhalabi, Qutaibah M. Malluhi, Rafic A. ...
AUSAI
2006
Springer
14 years 7 days ago
Robust Character Recognition Using a Hierarchical Bayesian Network
There is increasing evidence to suggest that the neocortex of the mammalian brain does not consist of a collection of specialised and dedicated cortical architectures, but instead ...
John Thornton, Torbjorn Gustafsson, Michael Blumen...
GECCO
2008
Springer
138views Optimization» more  GECCO 2008»
13 years 9 months ago
Modular neuroevolution for multilegged locomotion
Legged robots are useful in tasks such as search and rescue because they can effectively navigate on rugged terrain. However, it is difficult to design controllers for them that ...
Vinod K. Valsalam, Risto Miikkulainen
ICRA
2005
IEEE
106views Robotics» more  ICRA 2005»
14 years 2 months ago
Robot Guidance with Neuromorphic Motion Sensors
— Neuromorphic motion sensors are attractive for use on battery powered robots which require a low payload. Their features include low power consumption, continuous computation, ...
Lukas Reichel, David Liechti, Karl Presser, Shih-C...