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Computational Modeling of Neural Networks and Memory Simulation

Computational Modeling of Neural Networks and Memory Simulation. Alex Sonal Carl Ashwin Rebecca Shreyas Madhu Seth Jeff James Jonathan . Overview: Background Inspirations Biology and Neuroscience Computer Modeling Project Design and Coding Successes and Challenges

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Computational Modeling of Neural Networks and Memory Simulation

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  1. Computational Modeling of Neural Networks and Memory Simulation Alex Sonal Carl Ashwin Rebecca Shreyas Madhu Seth Jeff James Jonathan Overview: • Background • Inspirations • Biology and Neuroscience • Computer Modeling • Project • Design and Coding • Successes and Challenges • Central Question: Can we model a human brain? NJ Governor’s School for the Sciences Team Project T7 Dr. MinjoonKouh Aaron Loether

  2. Current State of Neuroscience • Anatomy is well understood • Lack of a cohesive brain theory • Emergent properties • Prediction versus Behavior

  3. The Brain

  4. Memory • Biology • Patterns of active and inactive neurons in neural networks • Vividness is determined by interneuron connection strength • Psychology • Forgetting • “Networks of knowledge” (associative memory)

  5. The Hebbian Theory “Neurons that Fire Together, Wire Together” • If activity of two neurons is correlated  strong synaptic connection ON Strong Weak ON OFF

  6. The Hopfield Network • Hopfield Network • If stimulus activates single neuron, other related neurons in neural network will also become activated School Friends Projects NJGSS Sciences Research (Output) (Input)

  7. Paul Paul Jr. Paul Jr. Jr. The Program

  8. Step 1: Process Images 1 0 1 0 0 1 . . .

  9. Step 2: Memorize 0 2 0 -2 2 0 0 -2 00 0 0 -2 -2 0 0 1 2 1 2 4 3 4 3 N1 N1 N2 N3 N4 N2 N3 N4 W11 W12 W13 W14 W21 W22 W23 W24 W31 W32 W33 W34 W41 W42 W43 W44 0 1 1 -1 1 0 1 -1 11 0 -1 -1 -1 -1 0 0 1 -1 -1 1 0 -1 -1 -1-1 0 1 -1 -1 1 0 N1 N1 N2 N2 N3 N3 N4 N4

  10. Step 3: Scramble

  11. Step 4: Recall Y(t) = W*Y(t-1)

  12. Pictures Memorized vs. Accuracy of Recall • Performance = • Range from -1 to 1 More pictures in the Memory Worse Recall

  13. The Effect of Noise on Recall More Noise Worse Recall Residual = performance of output – performance of input

  14. Challenges • Memory of MATLAB • Picture similarity Result: low resolution pictures and low performance

  15. The Future of the Hopfield Model - Brain Theory - Education - Artificial Intelligence

  16. SPECIAL THANKS TO:Dr. KouhAaron LoetherHopfield and HebbDr. MiyamotoMs. PapierBUT MOST OF ALL:Donors Who Helped Make NJGSS ‘11 Possible!!

  17. 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.

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