computational modeling of neural networks and memory simulation

Overview:•Background• Inspirations• Biology and Neuroscience• Computer Modeling

•Project• Design and Coding• Successes and Challenges

•Central Question: Can we model a human brain?

Computational Modeling of Neural Networks and Memory Simulation

NJ Governor’s School for the SciencesTeam Project T7Dr. Minjoon KouhAaron Loether

AlexSonalCarlAshwinRebeccaShreyas

MadhuSethJeffJamesJonathan

Current State of Neuroscience

◦ Anatomy is well understood◦ Lack of a cohesive brain theory Emergent properties Prediction versus Behavior

The Brain

MemoryBiology

◦ Patterns of active and inactive neurons in neural networks

◦ Vividness is determined by interneuron connection strength

Psychology◦ Forgetting◦ “Networks of

knowledge” (associative memory)

The Hebbian Theory“Neurons that Fire Together, Wire Together”If activity of two neurons is correlated strong synaptic

connection

ON

ON

OFF

Strong Weak

The Hopfield NetworkHopfield Network

If stimulus activates single neuron, other related neurons in neural network will also become activated

NJGSSScience

s

School

Research

Friends

Projects

(Input)(Output

)

THE PROGRAMPaul

Paul Jr.Paul Jr. Jr.

Step 1: Process Images

101001...

Step 2: Memorize

W11 W12W13W14

W21 W22W23W24

W31 W32 W33W34

W41 W42W43W44

1 2

340 1

1-1

1 01-1

1 1 0-1

-1 -1-10

4 3

1 2

0 1 -1 -1

1 0 -1 -1

-1 -1 0 1

-1 -1 10

0 2 0 -2

2 0 0 -2

0 0 0 0

-2 -2 0 0

N1

N2

N3N4

N1

N2

N3N4

N1 N2 N3 N4 N1 N2 N3 N4

Step 3: Scramble

Step 4: RecallY(t) = W*Y(t-1)

Pictures Memorized vs. Accuracy of Recall

More pictures in the Memory

Performance =

Range from -1 to 1

Worse Recall

The Effect of Noise on Recall

More Noise

Worse Recall

Residual=

performance of output –

performance of input

ChallengesMemory of MATLABPicture similarity

Result: low resolution pictures and low performance

The Future of the Hopfield Model

- Brain Theory

- Artificial Intelligence

- Education

SPECIAL THANKS TO:Dr. KouhAaron LoetherHopfield and HebbDr. MiyamotoMs. Papier

BUT MOST OF ALL:Donors Who Helped Make NJGSS ‘11 Possible!!

Sources Cited• Anastasio T J. Tutorial on Neural Systems Modeling. Sunderland

(MA): Sinauer Associates Inc.; 2010. 583 p.• Gazzaniga M S. The Cognitive Neurosciences. Cambridge (MA):

Bradford; 1997. 1447 p.• Wells R B.Synaptic Weight Modulation and Adaptation. In:

University of Idaho MRCI [discussion list on the Internet]. 2003 May 15; [cited 2011 July]. 13 p. Available from: http://www.mrc.uidaho.edu/~rwells/techdocs/Synaptic%20Weight%20Modulation%20and%20Adaptation%20I.pdf

• Kandel E R. Principles of Neuroscience. New York (NY): McGraw-Hill; 2000. 1414 p.

• Dayhoff J. School of Computing [homepage on the Internet]. Leeds (UK): University of Leeds; 2003. [cited 2011]. Available from: http://www.comp.leeds.ac.uk/ai23/reading/Hopfield.pdf.