Learning & the aging brain

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Concept

Next meeting: Monday, June 11, 7PM: should present short blurb on exploratory topics

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?).

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
• jd
• 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 http://www.lucs.lu.se/People/Sverker.Sikstrom/NBR-Li-Sikstrom.pdf

Neuroengineering models of brain disease. - Finkel
http://www.mssm.edu/cnic/pdfs/FinkelNeuroengineering.pdf

Patterns of functional damage in neural network models of associative memory
http://www.cs.tau.ac.il/~ruppin/spat.pdf

Possibly related

What is physiologic complexity and how does it change with aging and disease? - Goldberger, Peng, Lipsitz http://reylab.bidmc.harvard.edu/heartsongs/neurobiology-of-aging-2002-v23-23.pdf

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

Tutorials

• General neural networks: Biljana
• Attractor neural networks: Kristin & Vikas
• Boolean networks: Amelie & Amitabh
• Biological basis diseases (once chosen)