Biological Inspiration for Artificial Neural Networks Nick Mascola

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

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

Typical Transfer Functions

Recall that Over Time:

Finite Amount of Resources

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;}  }

Long Term Potentiation SpecificityCooperativity Features Similar to ANN Functionality:

Distinct Feature Associativity

Possible Solution

…Or More Generally

References   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