In the context of neural networks, what are the fundamental parameters that determine the strength of connections between neurons called?

The fundamental parameters that determine the strength of connections between neurons in the context of neural networks are known as weights. Weights are numerical values that adjust the input signals from one neuron to another based on the strength of the relationship or influence that one neuron has over another.

When a neural network processes data, it multiplies the input values by their associated weights and then passes them through an activation function. This process allows the network to learn and model complex patterns by adjusting the weights during training through algorithms such as backpropagation. Essentially, weights are crucial in controlling how much influence each neuron has on the neuron it is connected to, thus directly affecting the learning outcome and performance of the neural network.

The other terms listed refer to different concepts within a neural network. Connections refer to the pathways between neurons but do not imply strength without the context of weights. Biases are additional parameters used to adjust the output along with weights, but they do not represent the strength of connections. Nodes, or neurons, are the individual processing units in a neural network. While they play an important role, they do not define the strength of the connections between them.