In this topic, we’ll dive into the exciting world of neural computing—the science behind building machines that can learn and reason like the human brain. We’ll trace the history of this field, from the earliest philosophical dreams of understanding thought, all the way to the advanced, data-driven models that can learn on their own.

You’ll see how neural computing brings together ideas from neuroscience, psychology, mathematics, and computer science, creating a rich and evolving field. We’ll explore key moments in the journey, from the McCulloch-Pitts neuron, to the Perceptron, and finally to today’s deep learning breakthroughs. Along the way, you’ll get a sense of how the field has grown through cycles of innovation and skepticism—showing both the challenges and the breakthroughs that have led us to where we are now.

By the end of this topic, you’ll understand that neural computing isn’t just about building technology; it’s about exploring how we can model intelligence itself, and how diverse disciplines come together to shape our understanding of the mind and machines.

• Why is modeling human intelligence considered both a scientific and philosophical challenge?

• How did the integration of neuroscience, mathematics, and computer science shape the early trajectory of neural computing?

• What can the rise, fall, and resurgence of neural networks teach us about how science evolves over time?