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Computational Neuroscience Simulation of Neural Networks for Memory.

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Presentation on theme: "Computational Neuroscience Simulation of Neural Networks for Memory."— Presentation transcript:

1 Computational Neuroscience Simulation of Neural Networks for Memory

2 What is a Neuron? synapse Inputs Integration of Inputs Output

3 Action Potentials Resting Potential Action Potentials All-or-none

4 Encoding Memory Consolidation Memory Storage Recall/Recognition Memory Hippocampus

5 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)

6 The "Jennifer Aniston" Neuron R. Quian Quiroga, L. Reddy, C. Koch and I. Fried (2005)

7 Alzheimer's Disease Death of neurons Beta-amyloid plaques Neurofibrillary tangles Resulting memory loss Our Model Random neuron failure Predicts effect on memory recall

8 Neuroscience and Computers

9 Hopfield Network Artificial neuron network Synaptic weights Hebb's principle

10 Computational Methods Learning/Auto Associative Memory Input (P) 111 111 110 Size 3x3 Output (W) 331 331 113 Size 3x3 W(1,1)={[P(1,1)*2]-1}+{[P(1,1)*2]-1} W(1,1)=1+1=2 Output (W) 031 301 110 Size 3x3

11 Computational Methods Recall/Synchronous + Asynchronous Update Original (P) 111 111 110 Size 3x3 Input (Y0) 1 1 0 Size 3x3 Input (W) 013 101 310 Output (Y) 11…1 11…1 01…1 Y(:,2)=W*Y(:,1)

12 Simulating Memory

13 Better RecallPoorer Recall

14 Our Study Neurons Patterns Recall Percentage Our Goal: Find Relationships Between Variables

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16 Percent Recall as a Function of Patterns with a Set Number of Neurons Number of Patterns Percent Recall

17 P < NK N =.08 Percent Recall as a Function of Neurons and Patterns Number of Neurons Number of Patterns

18 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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23 Is our model accurate?

24 Questions?

25 Dr. Minjoon KouhDr. David Miyamoto Dr. Roger KnowlesDr. Steve Surace Aaron Loether Anna Mae Dinio-Bloch Myrna Papier Janet Quinn John and Laura Overdeck The Crimmins Family Charitable Foundation Ina Zucchi Family Trust NJGSS Alumni and Parents 1984 – 2012 AT&T Foundation Google Johnson & Johnson Wellington Management Special Thanks To...

26 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


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