Computational Neuroscience
description
Transcript of Computational Neuroscience
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Computational Neuroscience
Simulation of Neural Networks for Memory
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What is a Neuron?
synapse
Inputs Integration of Inputs Output
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Action Potentials
• Resting Potential
• Action Potentials
• All-or-none
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• Encoding
• Memory Consolidation
• Memory Storage
• Recall/Recognition
Memory
Hippocampus
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•Patients were shown pictures of celebrities
•A neuron would fire an action potential for J.A.
•The neuron is part of a memory pattern
• Recognition of J.A.
The "Jennifer Aniston" Neuron
R. Quian Quiroga, L. Reddy, C. Koch and I. Fried (2005)
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The "Jennifer Aniston" Neuron
R. Quian Quiroga, L. Reddy, C. Koch and I. Fried (2005)
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Alzheimer's Disease
• Death of neurons• Beta-amyloid plaques• Neurofibrillary tangles• Resulting memory loss
Our Model• Random neuron failure• Predicts effect on memory recall
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Neuroscience and Computers
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Hopfield Network
• Artificial neuron network
• Synaptic weights
• Hebb's principle
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Computational Methods
Learning/Auto Associative Memory
Input (P)
1 1 1
1 1 1
1 1 0
Size 3x3
Output (W)3 3 1
3 3 1
1 1 3
Size 3x3
W(1,1)={[P(1,1)*2]-1}+{[P(1,1)*2]-1}W(1,1)=1+1=2
Output (W)0 3 1
3 0 1
1 1 0
Size 3x3
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Computational Methods
Recall/Synchronous + Asynchronous Update Original (P)
1 1 1
1 1 1
1 1 0
Size 3x3Input (Y0)
110Size 3x3 Size 3x3
Input (W)0 1 31 0 13 1 0
Output (Y)1 1 … 1
1 1 … 1
0 1 … 1
Y(:,2)=W*Y(:,1)
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Simulating Memory
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Better Recall Poorer Recall
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Our Study
• Neurons• Patterns• Recall Percentage
Our Goal: Find Relationships Between Variables
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Percent Recall as a Function of Patterns with a Set Number of Neurons
Number of Patterns
Perc
ent R
ecal
l
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P < NK N = .08
Percent Recall as a Function of Neurons and Patterns
Number of
Neurons
Number of Patterns
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Modeling Random Synaptic Failure
• Randomly lowering synaptic weight values to simulate random neuron failures
• Equate to a preliminary model for Alzheimer's Disease
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Is our model accurate?
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Questions?
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Dr. Minjoon Kouh Dr. David MiyamotoDr. Roger Knowles Dr. Steve SuraceAaron LoetherAnna Mae Dinio-BlochMyrna PapierJanet QuinnJohn and Laura OverdeckThe Crimmins Family Charitable FoundationIna Zucchi Family TrustNJGSS Alumni and Parents 1984 – 2012AT&T FoundationGoogleJohnson & JohnsonWellington Management
Special Thanks To . . .
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• Morris R, Tarassenko L, Kenward M. Cognitive systems: information processing meets brain science. Jordan Hill (GBR): Academic Press. 325 p.
• Nadel L, Samsonovich A, Ryan L, Moscovitch M. Multiple trace theory of human memory: computational, neuroimaging, and neuropsychological results. NCBI (2000) 19-20.
• Knowles, RB, Wyart, C, Buldyrev, SV, Cruz, L, Urbanc, B, Hasselmo, ME, Stanley, HE, and Hyman, BT. Plaque-induced neurite abnormalities: implications for disruption of neural networks in alzheimer's disease. National Academy of Science. (1999) 12-14.
• Squire L, Berg D, Bloom F, Lac S, Ghosh A, Spitzer N. Fundamental neuroscienc. Burlington (MA): Academic Press; 2008. 1225 p.
• James L, BurkeD. Journal of experimental psychology: learning memory and cognition [Internet] American Psychological Association; 2000 [cited 2012 July 26]
• Lu L, Bludau J. 2011. Causes. In: Library of Congress, editors. Alzheimer’s Disease. Santa Barbara (CA): Greenwood. p85-124
• [NINDS] National Institute of Neurological Disorders and Stroke. c2012. Stroke: hope through research. NIH; [cited 2012 July 26].
• [NINDS] National Institute of Neurological Disorders and Stroke. c2012. Parkinson’s disease: hope through research. NIH; [cited 2012 July 26].
• [NIA] National Institutes of Aging. 2008. Alzheimer’s disease: unraveling the mystery [Internet] NIH; [cited 2012 Jul 29].
• Hopfield J. Neural networks and physical systems with emergent collective computational abilities. CIT (1982). 8-9.
• Lee C. 2006. Artificial Neural Networks [Internet] Waltham (MA): MIT; [cited 2012 Jul 29]; 5p.
References