The relationship between weights and biases in artificial neurons is that weights determine the strength of the input-output relationship and biases determine the activation threshold. In other words, the weights determine how much each input contributes to the final output, while the bias determines at what point the activation function should fire and produce an output.

Weights are assigned to each input, with a larger weight indicating that the corresponding input has a greater impact on the output. The bias, on the other hand, is a single value that is added to the sum of weighted inputs, allowing for a shift in the activation function.

Together, the weights and biases determine the overall behavior of the artificial neuron and how it responds to inputs. By adjusting the weights and biases, the network can learn to recognize patterns and perform various tasks.
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