OK, this story on the BRAIN Initiative in the New Yorker is pretty weird:

To progress, we need to learn how to combine the insights of molecular biochemistry…with the study of computation and cognition… (Though some dream of eliminating psychology from the discussion altogether, no neuroscientist has ever shown that we can understand the mind without psychology and cognitive science.)

Who, exactly, has suggested eliminating psychology from the study of neuroscience? Anyone? And then there’s this misleading paragraph:

The most important goal, in my view, is buried in the middle of the list at No. 5, which seeks to link human behavior with the activity of neurons. This is more daunting than it seems: scientists have yet to even figure out how the relatively simple, three-hundred-and-two-neuron circuitry of the C. Elegans worm works, in part because there are so many possible interactions that can take place between sets of neurons. A human brain, by contrast, contains approximately eighty-six billion neurons.

As a C. elegans researcher, I have to say: it’s true there’s a lot we don’t know about worm behavior! There’s also not quite as many worm behavioralists as there are, say, human behavioralists. But there is a lot that we do know. We know full circuits for several behaviors, and with the tools that we have now that numbers going to explode over the next few years.

But then we learn that, whatever else, Gary Marcus really doesn’t like the work that computational neuroscientists have done to advance their tools and models:

Perhaps the least compelling aspect of the report is one of its justifications for why we should invest in neuroscience in the first place: “The BRAIN Initiative is likely to have practical economic benefits in the areas of artificial intelligence and ‘smart’ machines.” This seems unrealistic in the short- and perhaps even medium-term: we still know too little about the brain’s logical processes to mine them for intelligent machines. At least for now, advances in artificial intelligence tend to come from computer science (driven by its longstanding interest in practical tools for efficient information processing), and occasionally from psychology and linguistics (for their insights into the dynamics of thought and language).

Interestingly, he gives his own field, psychology and linguistics, a pass for how much more they’ve done. So besides, obviously, the study of neural networks, let’s think about what other aspects of AI have been influenced by neuroscience. I’d count deep learning as a bit separate and clearly Google’s pretty excited about that. Algorithms for ICA, a dimensionality reduction method used in machine learning, were influenced by ideas about how the brain uses information (Tony Bell). The role of dopamine and serotonin have contributed to reinforcement learning. Those are just the first things that I can think of off the top of my head (interestingly, almost all of this sprouted out of the lab of Terry Sejnowski.) There have been strong efforts on dimensionality reduction – an important component of machine learning – from many, many labs in computational neuroscience. These all seem important to me; what, exactly, does Gary Marcus want? He doubles down on it in the last paragraph:

There are plenty of reasons to invest in basic neuroscience, even if it takes decades for the field to produce significant advances in artificial intelligence.

What’s up with that? There are even whole companies whose sole purpose is to design better algorithms based on principles from spiking networks. Based on his previous output, he seems dismissive of modern AI (such as deep learning). Artificial intelligence is no longer the symbolism we used to think it was: it’s powerful statistical techniques. We don’t live in the time of Chomskian AI anymore! It’s the era of Norvig. And the modern AI focuses on statistical principles which are highly influenced by ideas neuroscience.