Computational Neuroscience Simulation of Neural Networks for Memory

What is a Neuron? synapse Inputs Integration of Inputs Output

Action Potentials Resting Potential Action Potentials All-or-none

Encoding Memory Consolidation Memory Storage Recall/Recognition Memory Hippocampus

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)

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

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

Neuroscience and Computers

Hopfield Network Artificial neuron network Synaptic weights Hebb's principle

Computational Methods Learning/Auto Associative Memory Input (P) Size 3x3 Output (W) Size 3x3 W(1,1)={[P(1,1)*2]-1}+{[P(1,1)*2]-1} W(1,1)=1+1=2 Output (W) Size 3x3

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

Simulating Memory

Better RecallPoorer Recall

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

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

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

Modeling Random Synaptic Failure Randomly lowering synaptic weight values to simulate random neuron failures Equate to a preliminary model for Alzheimer's Disease

Is our model accurate?

Questions?

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...

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) 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) Squire L, Berg D, Bloom F, Lac S, Ghosh A, Spitzer N. Fundamental neuroscienc. Burlington (MA): Academic Press; 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 Causes. In: Library of Congress, editors. Alzheimer’s Disease. Santa Barbara (CA): Greenwood. p [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 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) Lee C Artificial Neural Networks [Internet] Waltham (MA): MIT; [cited 2012 Jul 29]; 5p. References