Binary Neurons hard threshold 1.2 output Stimulus Response l4=∑ furs tu “Hard” threshold heaviside z≥⊙→ON -1 else→OFF 0= threshold ex: Perceptrons, Hopfield nns, boltzmann Machines Main drawbacks: can only map binary functions, biologically implausible 02/02/2021 Artificial Neural Networks02/02/2021 Artificial Neural Networks - I 11 Binary Neurons ( )               = else OFF z ON f z “Hard” threshold = threshold hard threshold -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 -10 -8 -6 -4 -2 0 2 4 6 8 10 input output heaviside • ex: Perceptrons, Hopfield NNs, Boltzmann Machines • Main drawbacks: can only map binary functions, biologically implausible. off on =  j i ij j u w x Stimulus ( ) i urest ui y = f + Response