• Supervised Learning algorithms require a target output
  • As opposed to unsupervised in which has no target output
  • Reinforcement is gives feedback
• Underfitting can be a result of having a lack of hidden neurons or lack of data
• Overtraining can be a result of too many hidden neurons
• Each neuron has many inputs but only one output
• Activation layer of hidden layer must be nonlinear
  • Other layers (I/O) can be linear

Shunting Model

• X* = -Ax + (B-x)S+ -(D+X)S-
• S+ = Excitatory Input to neuron
• S- = Inhabitory Input to neuron
• A = Passive Decay Rate
• B = Upper Bound of neural activity
• D = Lower Bound of neural activity