In the context of neural networks, connections refer to the links between individual neurons that facilitate the transfer of information across the network. Each connection has an associated weight that adjusts as learning occurs, influencing how signals are processed and integrated. These connections are fundamental for both backpropagation and neuroevolution techniques, as they determine the network's ability to learn from inputs and improve performance over time.
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