Learning & the aging brain
From Santa Fe Institute Events Wiki
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.
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)
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
Patterns of functional damage in neural network models of associative memory
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
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