It is assumed that a central pattern generator possesses an exponentially stable limit cycle, which originates a periodic output signal. We propose a method based on a Gauss-Newton iteration to determine the values of the neural coupling parameters that allows to approximate a given reference output signal. We present two applications. The first is a ring network of Morris-Lecar neurons, where the output of the system is the sum of the membrane potential of all neurons. The second is a network of six neural cells for the generation of the leg movements of a hexapod.
A Gauss-Newton Method for the Synthesis of Periodic Outputs With Central Pattern Generators / Consolini, Luca; Lini, Gabriele. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - 25:7(2014), pp. 1394-1400. [10.1109/TNNLS.2013.2288260]
A Gauss-Newton Method for the Synthesis of Periodic Outputs With Central Pattern Generators
CONSOLINI, Luca;LINI, Gabriele
2014-01-01
Abstract
It is assumed that a central pattern generator possesses an exponentially stable limit cycle, which originates a periodic output signal. We propose a method based on a Gauss-Newton iteration to determine the values of the neural coupling parameters that allows to approximate a given reference output signal. We present two applications. The first is a ring network of Morris-Lecar neurons, where the output of the system is the sum of the membrane potential of all neurons. The second is a network of six neural cells for the generation of the leg movements of a hexapod.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
