In terms of artificial neuron weights, which statement is true regarding adjustable and fixed terms?

In the context of artificial neurons, adjustable weights and fixed biased terms work together to influence the output of the neuron. Weights are parameters that are adjusted during the training process of a neural network. They determine the importance of each input signal by scaling its contribution to the final output. The ability to adjust weights is crucial because it enables the network to learn from the data it processes, optimizing performance based on the task at hand.

On the other hand, the biased term (or bias) is typically treated as a fixed value during the inference process. The bias is added to the weighted sum of the inputs before passing through an activation function, helping the model to shift the activation threshold. While the bias itself can sometimes be learned and thus could be considered adjustable in some configurations, in many foundational models, it is fixed in a manner that allows the weights to be the primary means through which the model learns.

The assertion that weights are adjustable while biased terms are fixed aligns with the standard behavior of artificial neuron models, where flexibility in learning comes from the manipulation of weights, enhancing the network's ability to generalize and perform tasks effectively.