Learning & the aging brain: Difference between revisions
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'''Tutorial meeting:''' Sunday 4pm in the lecture room. Bring a laptop if you've got one - download & install JavaNNS. <br> | '''Tutorial meeting:''' Sunday 4pm in the lecture room. Bring a laptop if you've got one - download & install JavaNNS. <br> | ||
'''Next group meeting:''' | '''Next group meeting:''' Wednesday, June 20, 8:30pm in the lecture room <br> | ||
Revision as of 03:17, 20 June 2007
| CSSS Santa Fe 2007 |
Concept
Tutorial meeting: Sunday 4pm in the lecture room. Bring a laptop if you've got one - download & install JavaNNS.
Next group meeting: Wednesday, June 20, 8:30pm in the lecture room
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
Scale-Free Brain Functional Networks (Physical Review Letters, PRL 94, 018102, 2005)
Comments: Evidence that brain functionally behaves as a small-world network with scale-invariant properties
I will hand this out at the 6/17 meeting. (Mike)
Faster learning in small-world neural networks - Kroger, arXiv 2005
http://arxiv.org/abs/physics/0402076
Comments: Only small-worlds + backprop paper. Read this!!
Extremely high stability to perturbations in small-world networks - Albert, Jeong, & Barabasi, 2000
(Nature, 406, 378-381)
Comments: This finding treads dangerously close to ours! (Mike)
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 - Duncan Watts & Steven Strogatz
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
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)