Learning & the aging brain

CSSS Santa Fe 2007

Concept

Next meeting: Tentatively scheduled for the evening of Wednesday, June 20

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
Saleha Habibullah
Yossi Yovel
jd
Natasha Qaisar
Mike Wojnowicz

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

Modeling brain disease

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

Small worlds & the brain

Faster learning in small-world neural networks - Kroger, arXiv 2005
http://arxiv.org/abs/physics/0402076
Comments: Only small-worlds + backprop paper. Read this!!

The meaning of mammalian adult neurogenesis and the function of newly added neurons: the "small-world" network - Manev, Medical Hypotheses 2005
Comments: Kind of half-baked, but good for references & overview

Collective dynamics of 'small-world' networks
http://www.nature.com/nature/journal/v393/n6684/abs/393440a0.html
Please contact me (Gregor) for print version in case you don't have access to Nature

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: Kristen & Vikas & Amitabh
- Biological underpinning, general patterns of damage
Parkinson's disease: jd & Kristen
Alzheimer's disease: Gregor & Natasha & Vikas
Boolean networks and self-healing: Amelie & Amitabh (connected with the Healing strategies for networks project)
Social implications of aging: Saleha & Amelie

Tutorials

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