Review Bivalent BAM theorem. Every matrix is bidirectionally stable for synchronous or asynchronous state changes. Synchronous:update an entire field of neurons at a time. ● Simple asynchronous:only one neuron makes a state- change decision. Subset asynchronous:one subset of neurons per field makes state-change decisions at a time. 17
17 Bivalent BAM theorem. Every matrix is bidirectionally stable for synchronous or asynchronous state changes. • Synchronous:update an entire field of neurons at a time. • Simple asynchronous:only one neuron makes a statechange decision. • Subset asynchronous:one subset of neurons per field makes state-change decisions at a time. Review
Chapter 3.Neural Dynamics II:Activation Models The most popular method for constructing M:the bipolar Hebbian or outer-product learning method binary vector associations:(4,B) i=1,2,…m bipolar vector associations:(XY A=2x,+训 X,=2A-1
Chapter 3. Neural Dynamics II:Activation Models The most popular method for constructing M:the bipolar Hebbian or outer-product learning method binary vector associations: bipolar vector associations: ( , ) Ai Bi ( , ) Xi Yi i = 1,2, m [ 1] 2 1 Ai = Xi + Xi = 2Ai −1