Discovering how the brain works through computation
Columbia Engineering researchers propose new computational model of the brain based on assemblies of neurons; their model applied to syntactic processing in the production of language is consistent with recent experimental results.
New York, NY—June 11, 2020—Accelerating progress in neuroscience is helping us understand the big picture—how animals behave and which brain areas are involved in bringing about these behaviors—and also the small picture—how molecules, neurons, and synapses interact. But there is a huge gap of knowledge between these two scales, from the whole brain down to the neuron.
A team led by Christos Papadimitriou, the Donovan Family Professor of Computer Science at Columbia Engineering, proposes a new computational system to expand the understanding of the brain at an intermediate level, between neurons and cognitive phenomena such as language. The group, which includes computer scientists from Georgia Institute of Technology and a neuroscientist from the Graz University of Technology, has developed a brain architecture that is based on neuronal assemblies, and they demonstrate its use in the syntactic processing in the production of language; their model, published online June 9 in PNAS, is consistent with recent experimental results.
“For me, understanding the brain has always been a computational problem,” says Papadimitriou, who became fascinated by the brain five years ago. “Because if it isn’t, I don’t know where to start.”
He was spurred on by Columbia researcher and Nobel laureate Richard Axel, who recently noted, “We do not have a logic for the transformation of neural activity into thought and action.” Papadimitriou wondered what would happen if he interpreted this “logic” as a programming language like Python: just as Python manipulates numbers, the brain’s logic manipulates populations of neurons.
He and his team developed a computational system, the Assembly Calculus, that encompasses operations on assemblies, or large populations, of neurons that appear to be involved in cognitive processes such as imprinting memories, concepts, and words. In just the way Python programs can be compiled to machine code and execute, the Assembly Calculus can in principle be translated down to the language of neurons and synapses. The researchers were able to show, both analytically as well as through simulations, that the system is plausibly realizable at the level of neurons and synapses.
“So, we have finally articulated our theory about the nature of the ‘logic’ sought by Axel, and its supporting evidence,” says Papadimitriou, who is also a member of the Data Science Institute. “Now comes the hard part, will neuroscientists take our theory seriously and try to find evidence that something like it takes place in the brain, or that it does not?”
With a new three-year grant from the National Science Foundation, the team is now working with experimental neuropsychologists at CUNY to carry out fMRI experiments in humans to check the predictions of their theory regarding language.
About the Study
The study is titled “Brain computation by assemblies of neurons.”
Authors are: Christos H. Papadimitriou a, Santosh S. Vempala b , Daniel Mitropolsky a, Michael Collins a, and Wolfgang Maass c
A Department of Computer Science, Columbia Engineering
b College of Computing, Georgia Institute of Technology
c Institute of Theoretical Computer Science, Graz University of Technology, Austria
The study was supported in part by National Science Foundation Awards CCF1763970, CCF1910700, 1717349, 1839323, and 1909756; a research contract with Softbank;and the European Union’s Human Brain Project Grant 991 785907.
The authors declare no financial or other conflicts of interest.
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LINKS:
Paper: https://www.pnas.org/content/early/2020/06/08/2001893117
DOI: 10.1073/pnas.2001893117
https://www.pnas.org/
http://engineering.columbia.edu/
https://engineering.columbia.edu/faculty/christos-papadimitriou
https://www.cs.columbia.edu/
https://datascience.columbia.edu/
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Columbia Engineering
Columbia Engineering, based in New York City, is one of the top engineering schools in the U.S. and one of the oldest in the nation. Also known as The Fu Foundation School of Engineering and Applied Science, the School expands knowledge and advances technology through the pioneering research of its more than 220 faculty, while educating undergraduate and graduate students in a collaborative environment to become leaders informed by a firm foundation in engineering. The School’s faculty are at the center of the University’s cross-disciplinary research, contributing to the Data Science Institute, Earth Institute, Zuckerman Mind Brain Behavior Institute, Precision Medicine Initiative, and the Columbia Nano Initiative. Guided by its strategic vision, “Columbia Engineering for Humanity,” the School aims to translate ideas into innovations that foster a sustainable, healthy, secure, connected, and creative humanity.
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