Learning & the aging brain: Difference between revisions
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==Background reading== | ==Background reading== | ||
===Modeling brain disease=== | |||
Integrative neurocomputational perspectives on cognitive aging, neuromodulation, and representation. - Li and Sikstrom | Integrative neurocomputational perspectives on cognitive aging, neuromodulation, and representation. - Li and Sikstrom | ||
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http://www.cs.tau.ac.il/~ruppin/spat.pdf | http://www.cs.tau.ac.il/~ruppin/spat.pdf | ||
===Small worlds & the brain=== | |||
Faster learning in small-world neural networks - Kroger, arXiv 2005<br> | |||
http://arxiv.org/abs/physics/0402076 <br> | |||
''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 <br> | |||
''Comments: Kind of half-baked, but good for references & overview'' | |||
==Possibly related== | ==Possibly related== | ||
Revision as of 21:32, 13 June 2007
| 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
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)