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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 Recall that Over Time: Finite Amount of Resources 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;} } } Long Term Potentiation Features Similar to ANN Functionality: Cooperativity Specificity Distinct Feature Associativity Possible Solution …Or More Generally 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