Neuroscientist Henry Markram, who discovered spike-timing-dependent plasticity, has attacked Modha’s work on networks of simulated neurons, saying their behavior is too simplistic. He believes that successfully emulating the brain’s faculties requires copying synapses down to the molecular scale; the behavior of neurons is influenced by the interactions of dozens of ion channels and thousands of proteins, he notes, and there are numerous types of synapses, all of which behave in nonlinear, or chaotic, ways. In Markram’s view, capturing the capabilities of a real brain would require scientists to incorporate all those features.
The other part of DARPA’s project aims to make chips that mimic brains even more closely. HRL (was Hughes Research Labs), which looks out over Malibu from the foothills of the Santa Monica Mountains, was founded by Hughes Aircraft and now operates as a joint venture of General Motors and Boeing.
The HRL chip has neurons and synapses much like IBM’s. But like the neurons in your own brain, those on HRL’s chip adjust their synaptic connections when exposed to new data. In other words, the chip learns through experience.
The HRL chip mimics two learning phenomena in brains. One is that neurons become more or less sensitive to signals from another neuron depending on how frequently those signals arrive. The other is more complex: a process believed to support learning and memory, known as spike-timing-dependent plasticity. This causes neurons to become more responsive to other neurons that have tended to closely match their own signaling activity in the past. If groups of neurons are working together constructively, the connections between them strengthen, while less useful connections fall dormant.
Results from experiments with simulated versions of the chip are impressive. The chip played a virtual game of Pong, just as IBM’s chip did. But unlike IBM’s chip, HRL’s wasn’t programmed to play the game—only to move its paddle, sense the ball, and receive feedback that either rewarded a successful shot or punished a miss. A system of 120 neurons started out flailing, but within about five rounds it had become a skilled player. “You don’t program it,” Srinivasa says. “You just say ‘Good job,’ ‘Bad job,’ and it figures out what it should be doing.” If extra balls, paddles, or opponents are added, the network quickly adapts to the changes.
This approach might eventually let engineers create a robot that stumbles through a kind of “childhood,” figuring out how to move around and navigate. “You can’t capture the richness of all the things that happen in the real-world environment, so you should make the system deal with it directly,” says Srinivasa. Identical machines could then incorporate whatever the original one has learned. But leaving robots some ability to learn after that point could also be useful. That way they could adapt if damaged, or adjust their gait to different kinds of terrain.
The DARPA teams counters Markram that they don’t have to capture the full complexity of brains to get useful things done, and that successive generations of their chips can be expected to come closer to representing biology. HRL hopes to improve its chips by enabling the silicon neurons to regulate their own firing rate as those in brains do, and IBM is wiring the connections between cores on its latest neuromorphic chip in a new way, using insights from simulations of the connections between different regions of the cortex of a macaque.
Modha believes these connections could be important to higher-level brain functioning. Yet even after such improvements, these chips will still be far from the messy, complex reality of brains. It seems unlikely that microchips will ever match brains in fitting 10 billion synaptic connections into a single square centimeter, even though HRL is experimenting with a denser form of memory based on exotic devices known as memristors.
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