Algorithm 2 Homeostatic Unsupervised Learning of Kernels: $\Phi =H(\mathbf{y};\eta ,{\eta }_h,N_0)$

1:Initialize the point nonlinear gain functions $z_i$ to similar cumulative distribution functions, 2:Initialize N atoms ${\Phi }_i$ to random points on the M-unit sphere, 3:forT epochs do: 4: draw a new batch $\mathbf{y}$ from the database of natural images, 5: for each data point ${\mathbf{y}}_k$ do: 6:  compute the sparse representation vector using sparse coding ${\mathbf{a}}_k=S({\mathbf{y}}_k;\Psi =\{\Phi ,z,N_0\})$, 7:  modify atoms: $\forall i,{\Phi }_i\leftarrow {\Phi }_i+\eta ·{\mathbf{a}}_{k,i}·({\mathbf{y}}_k-\Phi {\mathbf{a}}_k)$, 8:  normalize atoms: $\forall i,{\Phi }_i\leftarrow {\Phi }_i/\left|\right|{\Phi }_i\left|\right|$, 9:   update homeostasis functions: $\forall i,z_i\left(·\right)\leftarrow (1-{\eta }_h)·z_i\left(·\right)+{\eta }_h·\delta ({\mathbf{a}}_{k,i}\le ·)$.