Learning & the aging brain

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Revision as of 17:06, 6 June 2007 by Vikasnshah (talk | contribs)


We can mimic the effect of aging on the human brain by deliberately corrupting neural network models of human learning (e.g. random deletion of nodes/synapses).

Possible directions include: exploring compensatory mechanisms for neuronal loss (related to self-healing networks?), modeling specific age-related diseases - e.g. Alzheimer's, Parkinson's (chaos & tremors?).

Initial brainstorming meeting - June 6, 10am in the lecture room. Everyone interested is welcome!

Please feel free to add questions, theories, suggestions.

Who's interested

Kristen Fortney

Gregor Obernosterer

Amitabh Trehan

Vikas Shah

Biljana Petreska

Amelie Veron

Wenyun Zuo

Saleha Habibullah

Yossi Yovel


Natasha Qaisar

Questions to answer

What sorts of age defects should be incorporated into the network?

What type of neural net should be used as a model? (backprop/attractor/etc)

Background reading

Integrative neurocomputational perspectives on cognitive aging, neuromodulation, and representation. - Li and Sikstrom

Neuroengineering models of brain disease. - Finkel

Patterns of functional damage in neural network models of associative memory

Possibly related

What is physiologic complexity and how does it change with aging and disease? - Goldberger, Peng, Lipsitz

Exploratory committees

General note: all should look at best neural network approach to their problem

  • Demyelination: Biljana & Yossi
    • Process to model these systems, time-delay in neural networks
    • Biology of MS
  • Normal aging: Kristin & Vikas & Amitabh
    • Biological underpinning, general patterns of damage
  • Parkinson's disease: jd & Kristin
  • Alzheimer's disease: Gregor & Natasha & Vikas
  • Boolean networks and self-healing: Amelie & Amitabh & Wenyun
  • Social implications of aging: Saleha & Amelie