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- W71269394 abstract "This work presents some first steps toward amore thorough understanding of the control systemsemployed in evolutionary robotics. In orderto choose an appropriate architecture or to constructan effective novel control system we needinsights into what makes control systems successful,robust, evolvable, etc. Here we present analysisintended to shed light on this type of questionas it applies to a novel class of artificial neuralnetworks that include a neuromodulatory mechanism:GasNets.We begin by instantiating a particular GasNetsubcircuit responsible for tuneable pattern generationand thought to underpin the attractiveproperty of “temporal adaptivity”. Rather thanwork within the GasNet formalism, we develop anextension of the well-known FitzHugh-Nagumoequations. The continuous nature of our modelallows us to conduct a thorough dynamical systemsanalysis and to draw parallels between thissubcircuit and beating/bursting phenomena reportedin the neuroscience literature.We then proceed to explore the effects of differenttypes of parameter modulation on the systemdynamics. We conclude that while there arekey differences between the gain modulation usedin the GasNet and alternative schemes (includingthreshold modulation of more traditional synapticinput), both approaches are able to producetuneable pattern generation. While it appears, atleast in this study, that the GasNet’s gain modulationmay not be crucial to pattern generation ,we go on to suggest some possible advantages itcould confer." @default.
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- W71269394 date "2004-01-01" @default.
- W71269394 modified "2023-09-25" @default.
- W71269394 title "Toward a Dynamical Systems Analysis of Neuromodulation" @default.
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- W71269394 doi "https://doi.org/10.7551/mitpress/3122.003.0042" @default.
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