Bad at Math, Good at Everything Else

Painful exercises in basic arithmetic are a vivid part of our elementary school memories. A multiplication like 3,752 × 6,901 carried out with just pencil and paper for assistance may well take up to a minute. Of course, today, with a cellphone always at hand, we can quickly check that the result of our little exercise is 25,892,552. Indeed, the processors in modern cellphones can together carry out more than 100 billion such operations per second. What’s more, the chips consume just a few watts of power, making them vastly more efficient than our slow brains, which consume about 20 watts and need significantly more time to achieve the same result.

Of course, the brain didn’t evolve to perform arithmetic. So it does that rather badly. But it excels at processing a continuous stream of information from our surroundings. And it acts on that information—sometimes far more rapidly than we’re aware of. No matter how much energy a conventional computer consumes, it will struggle with feats the brain finds easy, such as understanding language and running up a flight of stairs.

If we could create machines with the computational capabilities and energy efficiency of the brain, it would be a game changer. Robots would be able to move masterfully through the physical world and communicate with us in plain language. Large-scale systems could rapidly harvest large volumes of data from business, science, medicine, or government to detect novel patterns, discover causal relationships, or make predictions. Intelligent mobile applications like Siri or Cortana would rely less on the cloud. The same technology could also lead to low-power devices that can support our senses, deliver drugs, and emulate nerve signals to compensate for organ damage or paralysis.

But isn’t it much too early for such a bold attempt? Isn’t our knowledge of the brain far too limited to begin building technologies based on its operation? I believe that emulating even very basic features of neural circuits could give many commercially relevant applications a remarkable boost. How faithfully computers will have to mimic biological detail to approach the brain’s level of performance remains an open question. But today’s brain-inspired, or neuromorphic, systems will be important research tools for answering it.

A key feature of conventional computers is the physical separation of memory, which stores data and instructions, from logic, which processes that information. The brain holds no such distinction. Computation and data storage are accomplished together locally in a vast network consisting of roughly 100 billion neural cells (neurons) and more than 100 trillion connections (synapses). Most of what the brain does is determined by those connections and by the manner in which each neuron responds to incoming signals from other neurons.

When we talk about the extraordinary capabilities of the human brain, we are usually referring to just the latest addition in the long evolutionary process that constructed it: the neocortex. This thin, highly folded layer forms the outer shell of our brains and carries out a diverse set of tasks that includes processing sensory inputs, motor control, memory, and learning. This great range of abilities is accomplished with a rather uniform structure: six horizontal layers and a million 500-micrometer-wide vertical columns all built from neurons, which integrate and distribute electrically coded information along tendrils that extend from them—the dendrites and axons.

Like all the cells in the human body, a neuron normally has an electric potential of about –70 millivolts between its interior and exterior. This membrane voltage changes when a neuron receives signals from other neurons connected to it. And if the membrane voltage rises to a critical threshold, it forms a voltage pulse, or spike, with a duration of a few milliseconds and a value of about 40 mV. This spike propagates along the neuron’s axon until it reaches a synapse, the complex biochemical structure that connects the axon of one neuron to a dendrite of another. If the spike meets certain criteria, the synapse transforms it into another voltage pulse that travels down the branching dendrite structure of the receiving neuron and contributes either positively or negatively to its cell membrane voltage.

Connectivity is a crucial feature of the brain. The pyramidal cell, for example—a particularly important kind of cell in the human neocortex—contains about 30,000 synapses and so 30,000 inputs from other neurons. And the brain is constantly adapting. Neuron and synapse properties—and even the network structure itself—are always changing, driven mostly by sensory input and feedback from the environment.

General-purpose computers these days are digital rather than analog, but the brain is not as easy to categorize. Neurons accumulate electric charge just as capacitors in electronic circuits do. That is clearly an analog process. But the brain also uses spikes as units of information, and these are fundamentally binary: At any one place and time, there is either a spike or there is not. Electronically speaking, the brain is a mixed-signal system, with local analog computing and binary-spike communication. This mix of analog and digital helps the brain overcome transmission losses. Because the spike essentially has a value of either 0 or 1, it can travel a long distance without losing that basic information; it is also regenerated when it reaches the next neuron in the network.

Another crucial difference between brains and computers is that the brain accomplishes all its information processing without a central clock to synchronize it. Although we observe synchronization events—brain waves—they are self-organized, emergent products of neural networks. Interestingly, modern computing has started to adopt brainlike asynchronicity, to help speed up computation by performing operations in parallel. But the degree and the purpose of parallelism in the two systems are vastly different.