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…

In a neural network, the activation value of a neuron is determined by a combination of inputs from

the previous layer, the weights of the connections, and the bias at the neuron level. Here’s a detailed

breakdown:

Inputs for Activation Value:

Activation Values of Neurons in the Previous Layer: These are the outputs from neurons in the

preceding layer that serve as inputs to the current neuron.

Weights Assigned to the Connections: Each connection between neurons has an associated weight,

which determines the strength and direction of the input signal.

Individual Bias at the Neuron Level: Each neuron has a bias value that adjusts the input sum, allowing

the activation function to be shifted.

Calculation:

The activation value is computed by summing the weighted inputs from the previous layer and

adding the bias.

Formula: z=∑(wi

⋅

⋅

ai)+bz = \sum (w_i \cdot a_i) + bz=∑(wi​

ai​)+b, where wiw_iwi​are the weights,

aia_iai​are the activation values from the previous layer, and bbb is the bias.

The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation

value.

Why Option A is Correct:

Option A correctly identifies all components involved in computing the activation value: the

individual bias, the activation values of the previous layer, and the weights of the connections.

Eliminating Other Options:

B . Activation values of neurons in the previous layer, and weights assigned to the connections

between the neurons: This option misses the bias, which is crucial.

C . Individual bias at the neuron level, and weights assigned to the connections between the

neurons: This option misses the activation values from the previous layer.

D . Individual bias at the neuron level, and activation values of neurons in the previous layer: This

option misses the weights, which are essential.

Reference:

ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of

neurons in a neural network.

"Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).