In an artificial neural network, which element corresponds to a synapse in the human brain?

In an artificial neural network, the element that corresponds to a synapse in the human brain is the weight. In biological neural networks, synapses are the connections between neurons that facilitate communication and influence how signals are transmitted. Similarly, in an artificial neural network, weights determine the strength and importance of the connections between artificial neurons (or nodes).

When data is input into the network, it is multiplied by these weights, which essentially modulate the input signals based on their importance. Adjusting the weights during the training process allows the neural network to learn from the data, just as the brain strengthens or weakens synapses based on experience and learning.

The other elements serve different purposes: neurons are the processing units (akin to biological neurons), the activation function determines the output based on the weighted sum of inputs, and the input layer is the entry point for data into the network. However, it is the weights that truly represent the synaptic connections in the context of learning and information transfer in the network.