The activation value output for a neuron in a neural network is obtained by applying computation to the neuron. Which ONE of the following options BEST describes the inputs used to compute the activation value? SELECT ONE OPTION

The computation for a neuron's output involves two steps. First, a net input value is calculated. This is the weighted sum of the activation values from all neurons in the previous layer, plus an individual bias term for the current neuron. Second, this net input value is passed through an activation function to produce the final activation value (output). Therefore, the inputs required to compute the activation value are the previous layer's activation values, the weights on the connections, and the neuron's own bias.

B. This option is incomplete as it omits the individual bias term, which is a critical component in shifting the activation function.
C. This option is incorrect because it omits the activation values from the previous layer, which are the primary inputs to the neuron.
D. This option is incomplete as it omits the weights, which determine the strength of the connection from each neuron in the previous layer.

1. ISTQB

"ISTQB Certified Tester AI Testing (CT-AI) Syllabus

" Version 1.0

2021. Section 2.2.3

"Neural Networks

" describes the structure including inputs

weights

bias

and activation functions.

2. Goodfellow

I.

Bengio

Y.

& Courville

A. (2016). Deep Learning. MIT Press. Chapter 6

"Deep Feedforward Networks

" Section 6.2

describes the computation within a neuron as a linear transformation (weights and bias) followed by a nonlinear activation function.

3. Stanford University

CS231n Convolutional Neural Networks for Visual Recognition

"Neural Networks Part 1: Setting up the Architecture" notes. (Details the neuron model as f(w.x + b)).