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.
2014
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]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2732702
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