Projects

• Neural Parameter Space Degeneracy: there are many ways to configure a nervous system's components to produce the same desired outcome (a.k.a. adaptive behavior)
  • How is the variability between the neural parameters of individuals structured? Do organisms actually make use of all theoretically acceptable configurations of their components?*
  • How do processes like development, neuromodulation, disease, and homeostatic plasticity, change a system's position in that space of possibilities? For better or worse?
  • The nature of degeneracy means that organisms have limited information about their current state. Can they still effectively guide their behavior despite this limitation?*
  • How can organisms balance their robustness to unwanted external perturbations, while responding flexibly to their environment when appropriate?*
• Motor Pattern Generators: Rhythmic movements are essential in most brain-body-environment systems
  • Many rhythmic motor patterns (e.g. the Aplysia feeding cycle) exhibit cycle-to-cycle variability. What kinds of environmental perturbation might underly this variability, and what kinds would be "averaged away"?*
  • Olfaction requires thatregular breathing rhythms be co-opted for sniffing. What is the mechanism behind this control, and how is it shaped by incoming odor information to produce adaptive behaviors like navigation and source differentiation?
  • What tools from dynamical systems theory are most useful for describing these patterns in situated, embodied organisms?
• Activity-dependent Homeostatic Plasticity: Neurons and neural circuits must maintain stable activity levels in the face of constant internal and external perturbations
  • How do neurons use local information to globally regulate their activity levels?*
  • What are the dynamical consequences of different homeostatic mechanisms on single neurons and neural circuits?*
  • How do different homeostatic mechanisms interact with one another, and with other forms of plasticity (e.g. Hebbian plasticity)?*
  • How can we leverage our understanding of homeostatic plasticity to design more robust artificial neural networks?