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Biological Inspiration for Artificial Neural Networks

Biological Inspiration for Artificial Neural Networks. Nick Mascola. Artificial Neuron. Basic Structure. Output=f( Σ (Weights*Inputs)). Several Layered Network. A Typical Network Organizes these Neurons into layers that feed into each other sequentially. Typical Transfer Functions.

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Biological Inspiration for Artificial Neural Networks

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  1. Biological Inspiration for Artificial Neural Networks Nick Mascola

  2. Artificial Neuron Basic Structure Output=f(Σ(Weights*Inputs))

  3. Several Layered Network A Typical Network Organizes these Neurons into layers that feed into each other sequentially

  4. Typical Transfer Functions

  5. Recall that Over Time:

  6. Finite Amount of Resources

  7. Implementation • void distributeweightpoints(Connections con){ • vector<Weight> list = con.weights; • int totalpoints=con.points; • double total=weightsummation(list); • double temp; • for(unsigned int i=0; i<con.weights.size(); i++){ • temp=list[i].value/total; • if(temp<1/totalpoints){ • con.weights[i]=0;} • else{ • con.weights[i]=temp;} • } • }

  8. Long Term Potentiation Features Similar to ANN Functionality: Cooperativity Specificity

  9. Distinct Feature Associativity

  10. Possible Solution

  11. …Or More Generally

  12. References • http://hagan.ecen.ceat.okstate.edu/nnd.html • Matlab Neural Network Toolbox • Pattern Classification (2nd ed) by Richard O. Duda, Peter E. Hart and David G. Stork • Pattern Recognition and Machine Learning. Christopher M. Bishop • The long-term potential of LTP Robert C.Malenka

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