This work presents some first steps toward a
more thorough understanding of the control systems
employed in evolutionary robotics. In order
to choose an appropriate architecture or to construct
an effective novel control system we need
insights into what makes control systems successful,
robust, evolvable, etc. Here we present analysis
intended to shed light on this type of question
as it applies to a novel class of artificial neural
networks that include a neuromodulatory mechanism:
GasNets.
We begin by instantiating a particular GasNet
subcircuit responsible for tuneable pattern generation
and thought to underpin the attractive
property of “temporal adaptivity”. Rather than
work within the GasNet formalism, we develop an
extension of the well-known FitzHugh-Nagumo
equations. The continuous nature of our model
allows us to conduct a thorough dynamical systems
analysis and to draw parallels between this
subcircuit and beating/bursting phenomena reported
in the neuroscience literature.
We then proceed to explore the effects of different
types of parameter modulation on the system
dynamics. We conclude that while there are
key differences between the gain modulation used
in the GasNet and alternative schemes (including
threshold modulation of more traditional synaptic
input), both approaches are able to produce
tuneable pattern generation. While it appears, at
least in this study, that the GasNet’s gain modulation
may not be crucial to pattern generation ,
we go on to suggest some possible advantages it
could confer
