Transcript 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
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