In 2012, computer scientist Dharmendra Modha used a powerful supercomputer to simulate the activity of more than 500 billion neurons—more, even, than the 85 billion or so neurons in the human brain. It was the culmination of almost a decade of work, as Modha progressed from simulating the brains of rodents and cats to something on the scale of humans.

The simulation consumed enormous computational resources—1.5 million processors and 1.5 petabytes (1.5 million gigabytes) of memory—and was still agonizingly slow, 1,500 times slower than the brain computes. Modha estimates that to run it in biological real time would have required 12 gigawatts of energy, about six times the maximum output capacity of the Hoover Dam. “And yet, it was just a cartoon of what the brain does,” says Modha, chief scientist for brain-inspired computing at IBM Almaden Research Center in northern California. The simulation came nowhere close to replicating the functionality of the human brain, which uses about the same amount of power as a 20-watt lightbulb…..

But the fact that computers “think” very differently than our brains do actually gives them an advantage when it comes to tasks like number crunching, while making them decidedly primitive in other areas, such as understanding human speech or learning from experience. If scientists want to simulate a brain that can match human intelligence, let alone eclipse it, they may have to start with better building blocks—computer chips inspired by our brains.

So-called neuromorphic chips replicate the architecture of the brain—that is, they talk to each other using “neuronal spikes” akin to a neuron’s action potential. This spiking behavior allows the chips to consume very little power and remain power-efficient even when tiled together into very large-scale systems. Read More

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Imagine picking up a glass of what you think is apple juice, only to take a sip and discover that it’s actually ginger ale. Even though you usually love the soda, this time it tastes terrible. That’s because context and internal states, including expectation, influence how all animals perceive and process sensory information, explained Alfredo Fontanini, a neurobiologist at Stony Brook University in New York. In this case, anticipating the wrong stimulus leads to a surprise, and a negative response.

But this influence isn’t limited to the quality of the perception. Among other effects, priming sensory systems to expect an input, good or bad, can also accelerate how quickly the animal then detects, identifies and reacts to it.

Years ago, Fontanini and his team found direct neural evidence of this speedup effect in the gustatory cortex, the part of the brain responsible for taste perception. Since then, they have been trying to pin down the structure of the cortical circuitry that made their results possible. Now they have. Last month, they published their findings in Nature Neuroscience: a model of a network with a specific kind of architecture that not only provides new insights into how expectation works, but also delves into broader questions about how scientists should think about perception more generally. Moreover, it falls in step with a theory of decision making that suggests the brain really does leap to conclusions, rather than building up to them. Read More

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