MP Neuron

  1. The model takes inputs (x1, x2, …., xm)
  2. Applies Adder function(g) and
  3. Takes decision in Activation function (f)
  4. Gives an output Y
  1. When MP neurons are modeled as neural networks, they are connected by directed weighted paths in a neural network which we will study later.
  2. When we pass the Adder function value in the Activation function of an MP neuron, there are 2 possibilities: the neuron may fire (label 0) or it does not fire (label 1).
  3. The activation function is based on the threshold value. There is a fixed threshold for each neuron and if the net input to the neuron is greater than the threshold then the neuron fires.

A real-life simple example of MP Neuron :

Problem Statement :

  1. Sigmoid / Logistic function
  2. TanH / Hyperbolic Tangent
  3. Relu function
  4. Leaky Relu
  5. Softmax etc.

Geometrical Representation of MP Neuron




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Akash Jain

Akash Jain

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