Computational Neuroscience

Computational Neuroscience Simulation of Neural Networks for Memory

What is a Neuron? synapse Output Inputs Integration of Inputs

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

Memory Encoding Memory Consolidation Memory Storage Recall/Recognition Hippocampus

The "Jennifer Aniston" Neuron •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. 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 Size 3x3 Size 3x3 Size 3x3 W(1,1)={[P(1,1)*2]-1}+{[P(1,1)*2]-1} W(1,1)=1+1=2

Computational Methods Recall/Synchronous + Asynchronous Update Size 3x3 Size 3x3 Size 3x3 Y(:,2)=W*Y(:,1)

Simulating Memory

Better Recall Poorer 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 Percent Recall Number of Patterns

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

Special Thanks To . . . Dr. Minjoon Kouh Dr. David Miyamoto Dr. Roger Knowles Dr. 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

References 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.

Computational Neuroscience