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For a time, in the late 1980s, it looked like the field of neural networks was dead. Its researchers, who were seeking answers about consciousness by creating interconnected webs of computing units, could not overcome the limitations of their tools. Hardware did not compute at fast enough speeds. Software was too simplistic. It wasn’t until the 2010s that technology had advanced far enough to allow theories “that seemed almost frozen in amber” to be explored further.

That scientists could leap far ahead into new theoretical territory yet make little experimental progress in computational neuroscience underlines the challenges and complexity in explaining the workings of mind and consciousness. In Putting Ourselves Back in the Equation, journalist and Scientific American contributor George Musser brings readers along on this quest, tracking the development of different ideas and suppositions that aim to elucidate how consciousness might have arisen and what processes inform—if not create—our perceptions of reality.

Investigating the mind and confronting the “hard problem” of consciousness necessarily require the collision of disciplines. The field’s most significant researchers seem to have stumbled into it from myriad backgrounds—semiconductors, psychiatry, and cosmology, among other fields—and it’s Musser who wanders into these scientists at conferences, in cafeterias and in train cars to get details on the latest findings. His book is structured as an overview in the form of an expansive series of questions. It begins with the mechanical and local—say, how a brain might anticipate information—and progresses toward ones that threaten any simplistic notion of reality, such as: What if we’re only a floating blob mind that briefly materializes in the death throes of a universe?

It’s no surprise that the study and building of neural networks have become central to learning about the mind. Unlike simple computers, these networks can involve many parallel systems of interwoven logic, much like our brain and its wiring. Simulated neurons in a network, for instance, allow for the dynamism of feedback, enabling the network to form associations and learn algorithmically. What we consider as consciousness could be an emergent property of these highly organized, interconnected systems.

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Credit: Alex Eben Meyer

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