1. Biological Inspiration for Artificial Neural Networks Nick Mascola

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

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 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 SpecificityCooperativity Features Similar to ANN Functionality:

9. Distinct Feature Associativity

10. Possible Solution

11. …Or More Generally

12. References  http://hagan.ecen.ceat.okstate.edu/nnd.html 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