Extended homeostatic adaptation model with metabolic causation in plasticity mechanism—toward constructing a dynamic neural network model for mental imagery
Restricted accessResearch articleFirst published online August, 2013
Extended homeostatic adaptation model with metabolic causation in plasticity mechanism—toward constructing a dynamic neural network model for mental imagery
This study presents an extended dynamic neural network model of homeostatic adaptation as the first step toward constructing a model of mental imagery. In the homeostatic adaptation model, higher-level dynamics internally self-organized from sensorimotor dynamics are associated with desired behaviors. These dynamics are regenerated when drastic changes occur, which might break the internal dynamics. Due to the weak link between desired behavior and internal homeostasis in the original homeostatic adaptation model, adaptivity is limited. In this paper, we improve on the homeostatic adaptation model to create a stronger link between desired behavior and internal homeostasis by introducing a metabolic causation in a plasticity mechanism and show that it becomes more adaptive. Our results show that our model has three different time scales in the adaptive behaviors, which are discussed with our cognition and mental imagery.
AshbyW. R. (1960). Design for a brain: The origin of adaptive behavior (2nd ed.). London: Chapman and Hall.
2.
BarandiaranX.MorenoA. (2006). On what makes certain dynamical systems cognitive: A minimally cognitive organization program. Adaptive Behavior, 14(2), 171–185.
3.
BeerR. D. (1990). Intelligence as adaptive behavior: An experiment in computational neuroscience. San Diego, CA: Academic Press.
4.
BeerR. D. (1995). A dynamical systems perspective on agent–environment interaction. Artificial Intelligence, 72(1–2), 173–215.
5.
BeerR. D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In MaesP.MataricM. J.MeyerJ.-A.PollackJ. B.WilsonS. W. (Eds.), From Animals to Animats 4: Proceedings of the 4th International Conference on Simulation of Adaptive Behavior (pp.421–429). Cambridge, MA: MIT Press.
6.
Di PaoloE. A. (2000). Homeostatic adaptation to inversion in the visual field and other sensorimotor disruptions. In MeyerJ.BerthozA.FloreanoD.RoitblatH.WilsonS. (Eds.), From Animals to Animats VI: Proceedings of the 6th International Conference on Simulation of Adaptive Behavior (pp. 440–449). Cambridge, MA: MIT Press.
7.
Di PaoloE. A. (2002). Spike timing dependent plasticity for evolved robots inAdaptive Behavior, 10(3/4), 243–263.
8.
Di PaoloE. A. (2003). Organismically-inspired robotics: Homeostatic adaptation and natural teleology beyond the closed sensorimotor loop. In: MuraseK.AsakuraT. (Eds.), Dynamical Systems Approach to Embodiment and Sociality (pp. 19–42). Adelaide, Australia: Advanced Knowledge International.
9.
FroeseT.ZiemkeT. (2009). Enactive artificial intelligence: Investigating the systemic organization of life and mind. Artificial Intelligence, 173(3–4), 366–500.
10.
HoinvilleT.HenaffP. (2004). Evolving plastic neural controllers stabilized by homeostatic mechanisms for adaptation to a perturbation. Proceedings of the 9th International Conference on the Simulation and Synthesis of Living Systems (pp. 81–87). Cambridge, MA: MIT Press.
11.
IizukaH.Di PaoloE. A. (2007). Toward Spinozist robotics: Exploring the minimal dynamics of behavioural preference. Adaptive Behavior, 15(4), 359–376.
12.
IkegamiT.HashimotoT. (2002). Dynamical systems approach to higher-level heritability. Journal of Biological Physics, 28, 799–804.
13.
IkegamiT.SuzukiK. (2008). From homeostatic to homeodynamic self. BioSystems, 91(2), 388–400.
14.
GibsonJ. J. (1962). Observations on active touch. Psychological Review, 69, 477–491.
15.
MiyauchiS.EgusaH.AmagaseM.SekiyamaK.ImaruokaT.TashiroT. (2004). Adaptation to left–right reversed vision rapidly activates ipsilateral visual cortex in humans. Journal of Physiology—Paris, 98(1–3), 207–219.
16.
PennA.HarveyI. (2004). The role of non-genetic change in the heritability, variation and response to selection of artificially selected ecosystems. Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Life (pp. 352–357). Cambridge, MA: MIT Press.
17.
ShepardR.CooperL. (1982). Mental images and their transformations. Cambridge, MA: MIT Press.
18.
SwensonW.WilsonD. S.EliasR. (2000). Artificial ecosystem selection. Proceedings of the National Academy of Sciences USA, 97, 9110–9114.
19.
ThompsonE. (2007). Look again: Phenomenology and mental imagery. Phenomenology and the Cognitive Sciences, 6, 137–170.
20.
van GelderT. (1998). The dynamical hypothesis in cognitive science. Behavioural and Brain Sciences, 21, 615–665.
21.
VarelaF. J. (1979). Principles of biological autonomy. New York: Elsevier North Holland.
22.
WilliamsH. (2004). Homeostatic plasticity in recurrent neural networks. From Animals to Animats 8: Proceedings of the 8th International Conference on the Simulation of Adaptive Behavior (pp. 344–353). Cambridge, MA: MIT Press.
23.
WoodR.Di PaoloE. A. (2007). New models for old questions: Evolutionary robotics and the ‘A Not B’ error. Proceedings of 9th European Conference on Artificial Life (pp. 1141–1150). Berlin, Germany: Springer.
