RC1 – Homeostatically-driven behavioral architectures: How to model biological organisms throughout their life-cycle

Lecturer: Panagiotis Sakagiannis
Fields: Behavioral modeling, Systems Neuroscience, Robotics, Computational Ecology


Why do organisms behave? When do they take risks and when do rewards matter to them? What is the nervous system’s role in a successful life cycle and how does it relate to its evolutionary origins? In this course, we adopt a behavioral modeler’s view integrating insights from systems neuroscience, ecological energetics, and layered robotic architectures in order to sketch a framework for dynamic mechanistic models of biological behavior. We address the advantages and shortcomings of region-specific biologically realistic neurocomputational models, of agent-based ecological simulations and of optimality-driven intelligent artificial agents and discuss ways of combining these powerful computational tools with a focus on the persisting individual homeostasis. Nested behaviors, recurrent neural networks, and entangled spatiotemporal scales are our main modeling challenges. An intensively studied organism, the drosophila fruit fly larva, will serve as our model agent for the whole course.


Participants will benefit from an introduction to diverse scientific fields studying behavior or homeostasis, along with their computational tools. Philosophical debate on the normativity of behavior and mechanistic explanation will be touched upon, in the face of pressing modeling decisions. The valuable interaction between modelers and experimentalists will be highlighted. Finally, the delicate balance between detail and abstraction in behavioral modeling will be interactively discussed.


[1] S. a. L. M. Kooijman, “Dynamic Energy Budget theory for metabolic organisation : third edition,” Water, vol. 365, p. 68, 2010.
[2] T. J. Prescott, P. Redgrave, and K. Gurney, “Layered Control Architectures in Robots and Vertebrates,” Adaptive Behavior, pp. 99–127, 1999.
[3] M. J. Almeida-Carvalho et al., “The Ol1mpiad: Concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae,” J. Exp. Biol., vol. 220, no. 13, pp. 2452–2475, 2017.
[4] A. Campos-Candela, M. Palmer, S. Balle, A. Álvarez, and J. Alós, “A mechanistic theory of personality-dependent movement behaviour based on dynamic energy budgets,” Ecol. Lett., vol. 22, no. 2, pp. 213–232, 2018.
[5] W. Bechtel and A. Abrahamsen, “Dynamic mechanistic explanation: Computational modeling of circadian rhythms as an exemplar for cognitive science,” Stud. Hist. Philos. Sci. Part A, vol. 41, no. 3, pp. 321–333, 2010.


Panagiotis Sakagiannis:
Transitions across scientific fields are signs of both uneasiness and curiosity. In my case, a dual path can be traced, having medicine and clinical neurology on one side, mathematics and computational neuroscience on the other, while lately a PhD on insect behavioral modeling. Always seeking the broad picture when confronted with biological detail and the operationally useful formalization when attending philosophical debates, I still remain agnostic to my true inclination.