Patching up an artificial neuron in Pure Data Vanilla for some nonlinear mixing. There’s no talking on this one, just building the patch, and listening to it go.
An artificial neuron is basically just a mixer: inputs come in, and are weighted differently, modelling the dendrites of a biological neuron; then the mixed signal is transformed by an “activation function”, usually nonlinear, and output, modelling the axon.
Now, we can say that “learning” occurs when we adjust the weights (levels) of the inputs based on the output, but let’s not do that here, let’s just revel in our our nonlinear mix.
0:00 Nonlinear Mixing and Artificial Neurons
1:17 Adding “Bias”
2:28 Neuron Complete
3:27 Automating the Weights
7:09 Adding Feedback
8:42 Adding Noise
9:58 Commenting our Code
11:25 Trying the ReLU Activation Function
12:04 Linear Mixing (with Hard Clipping)