The first person to grasp the information-processing fundamentals of the brain was the great Spanish neuroscientist, Ramon Y Cajal, who won the 1906 Nobel Prize in Physiology. Before Cajal, the brain was thought to be made of microscopic strands connected in a continuous net or ‘reticulum.’ According to that theory, the brain was different from every other biological thing because it wasn’t made of separate cells. Cajal used new methods of staining brain samples to discover that the brain did have separate cells, which he called neurons. The neurons had long thin strands mixing together like spaghetti—dendrites and axons that presumably carried signals. But when he traced the strands carefully, he realized that one neuron did not grade into another. Instead, neurons contacted each other through microscopic gaps—synapses.

Cajal guessed that the synapses must regulate the flow of signals from neuron to neuron. He developed the first vision of the brain as a device that processes information, channeling signals and transforming inputs into outputs. That realization, the so-called neuron doctrine, is the foundational insight of neuroscience. The last hundred years have been dedicated more or less to working out the implications of the neuron doctrine.

It’s now possible to simulate networks of neurons on a microchip and the simulations have extraordinary computing capabilities. The principle of a neural network is that it gains complexity by combining many simple elements. One neuron takes in signals from many other neurons. Each incoming signal passes over a synapse that either excites the receiving neuron or inhibits it. The neuron’s job is to sum up the many thousands of yes and no votes that it receives every instant and compute a simple decision. If the yes votes prevail, it triggers its own signal to send on to yet other neurons. If the no votes prevail, it remains silent. That elemental computation, as trivial as it sounds, can result in organized intelligence when compounded over enough neurons connected in enough complexity.

The trick is to get the right pattern of synaptic connections between neurons. Artificial neural networks are programmed to adjust their synapses through experience. You give the network a computing task and let it try over and over. Every time it gets closer to a good performance, you give it a reward signal or an error signal that updates its synapses. Based on a few simple learning rules, each synapse changes gradually in strength. Over time, the network shapes up until it can do the task. That deep leaning, as it’s sometimes called, can result in machines that develop spooky, human-like abilities such as face recognition and voice recognition. This technology is already all around us in Siri and in Google.

But can the technology be scaled up to preserve someone’s consciousness on a computer? The human brain has about a hundred billion neurons. The connectional complexity is staggering. By some estimates, the human brain compares to the entire content of the internet. It’s only a matter of time, however, and not very much at that, before computer scientists can simulate a hundred billion neurons. Many startups and organizations, such as the Human Brain project in Europe, are working full-tilt toward that goal. The advent of quantum computing will speed up the process considerably. But even when we reach that threshold where we are able to create a network of a hundred billion artificial neurons, how do we copy your special pattern of connectivity?