Synaptic Dynamics: Unsupervised Learning PartⅡ Wang Xiumei 2023/7/9
2023/7/9 Synaptic Dynamics: Unsupervised Learning Part Ⅱ Wang Xiumei
1.Stochastic unsupervised learning and stochastic equilibrium; 2.Signal Hebbian Learning; 3.Competitive Learning. 2023/7/9
2023/7/9 1.Stochastic unsupervised learning and stochastic equilibrium; 2.Signal Hebbian Learning; 3.Competitive Learning
Stochastic unsupervised learning stochastic equilibrium (1)The noisy random unsupervised learning law; (2)Stochastic equilibrium; (3)The random competitive learning law; (4)The learning vector quantization system. 2023/7/9
2023/7/9 1.Stochastic unsupervised learning and stochastic equilibrium ⑴ The noisy random unsupervised learning law; ⑵ Stochastic equilibrium; ⑶ The random competitive learning law; ⑷ The learning vector quantization system
The noisy random unsupervised learning law The random-signal Hebbian learning law: dmj=-mi dt+S,(x,)S,(y)dt+dB (4-92) (B,(t)}denotes a Browian-motion diffusion process,each term in (4-92)demotes a separate random process. 2023/7/9
2023/7/9 The noisy random unsupervised learning law The random-signal Hebbian learning law: (4-92) denotes a Browian-motion diffusion process, each term in (4-92)demotes a separate random process. ( ) ( ) ij ij i i i i ij dm m dt S x S y dt dB = − + + { ( )} B t ij
The noisy random unsupervised learning law dB Using noise relationship: dt we can rewrite (4-92): m,=-m+S,(x)S,(y,)+n, (4-93) We assume the zero-mean, Gaussian white- noise process in(t);and use equation f(x,y,M)=-m,+S,(x)S,y,) 2023/7/9
2023/7/9 The noisy random unsupervised learning law • Using noise relationship: we can rewrite (4-92): (4-93) We assume the zero-mean, Gaussian whitenoise process ,and use equation : ( ) ( ) m m S x S y n ij ij i i j j ij = − + + dB n dt = { ( )} ij n t ( , , ) ( ) ( ) ij ij i i j j f x y M m S x S y = − +