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)

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

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)

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

Number of Patterns

Perc

ent R

ecal

l

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 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 . . .

• 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