Biological Inspiration for Biological Inspiration for Artificial Neural NetworksArtificial Neural Networks

Nick MascolaNick Mascola

Artificial NeuronArtificial Neuron

Output=f(Σ(Weights*Inputs))

Basic Structure

Several Layered NetworkSeveral Layered Network

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

Typical Transfer Typical Transfer FunctionsFunctions

Recall that Over Time:Recall that Over Time:

Finite Amount of ResourcesFinite Amount of Resources

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

Long Term PotentiationLong Term Potentiation

SpecificityCooperativity

Features Similar to ANN Functionality:

Distinct FeatureDistinct Feature

Associativity

Possible SolutionPossible Solution

……Or More GenerallyOr More Generally

ReferencesReferences

http://hagan.ecen.ceat.okstate.edu/nnd.htmlhttp://hagan.ecen.ceat.okstate.edu/nnd.html Matlab Neural Network ToolboxMatlab Neural Network Toolbox Pattern ClassificationPattern Classification (2nd ed) by Richard O. (2nd ed) by Richard O.

Duda, Peter E. Hart and David G. StorkDuda, Peter E. Hart and David G. Stork Pattern Recognition and Machine Learning. Pattern Recognition and Machine Learning.

Christopher M. BishopChristopher M. Bishop The long-term potential of LTP Robert C.Malenka