In this topic, we’ll take a closer look at the powerful connection between neural computing and the biological and cognitive systems that inspired it. The idea is not just to mimic the brain, but to reimagine intelligence in a completely new way—using silicon instead of neurons. We’ll explore how artificial neural networks (ANNs) borrow key ideas from the human brain, such as adaptability, locality, and sparsity—concepts that allow these networks to learn, evolve, and function in ways that are similar to how our brains process information.

We’ll then expand into the world of cognitive models, where we see how insights from human perception, attention, and memory have deeply influenced modern AI architectures, including popular models like convolutional neural networks (CNNs) and transformers. These models reflect how ideas from psychology and neuroscience have shaped the way we build intelligent systems today.

By the end of this topic, you’ll realize that neural computing isn’t just a technical field—it’s the modern continuation of humanity’s long-standing quest to understand the nature of thought, learning, and intelligence. This exploration shows that the field is deeply intertwined with our broader understanding of what it means to think, learn, and experience the world around us. It’s a journey where computation, biology, and psychology meet to redefine what it means to be intelligent.

• How do artificial networks balance biological inspiration with computational practicality?

• In what ways do cognitive models of perception and reasoning influence deep learning design?

• How do attention and memory mechanisms in neural networks parallel human cognition?