I think the definition will change as we learn more, but my working definition of solving the brain is: one, we can model, maybe in a computer, the processes that generate things like thoughts and feelings, and two, we can understand how to cure brain disorders, like Alzheimer's and epilepsy. Those are my two driving goals. One is more human-condition oriented, and one more clinical.

Suppose there are some things that we don't understand about the universe, but if you understand human intelligence and you understand the gaps in our abilities to think about things, maybe we can engineer in a computer more advanced intelligences that can help augment our ability to think.

Remember, when we're conscious of something, that state is quite often generated by unconscious processes that happen right before it.

I would argue that if you understand how the cells of the brain are organized into circuits, almost computational circuits if you will, and we see how information flows through those circuits and how it's transformed, we might have a much firmer grasp on why our brains make decisions the way that they do. If we get a handle on that, maybe we can overcome some of our limitations and at the very least we'll understand why we do what we do.

The skill-providers want to have more impact and solve problems; the problem people want new tools to get their problems solved.

You can imagine over very long timescales, perhaps far beyond the multi-decade time scale, we might be able to ask very deep questions about why we feel the way we feel about things, or why we think of ourselves in certain ways - questions that have been in the realm of psychology and philosophy but have been very difficult to get a firm mechanistic laws-of-physics grasp on.

The brain is really hard to see. The whole thing is very large - the human brain is several pounds in weight - but the connections between brain cells, known as synapses, are really tiny. They're nanoscale in dimension. So if you want to see how the cells of the brain are connected in networks, you have to see those connections, those synapses.