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Hadjiosif A.M.,Harvard University | Smith M.A.,Harvard University | Smith M.A.,Center for Brain Science
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

Successful manipulation of an object requires exerting grip forces (GF) sufficient to prevent slippage. To prevent slip in more uncertain environments, GF would need to increase. Here we investigate the brain's ability to efficiently control grasp by producing GFs that correspond to confidence estimates of uncertain environments that are characterized by probability density functions of different variances and higher order moments. We found that GFs increased dramatically with the level of environmental uncertainty, and even when environmental uncertainty was held constant while higher order moments were varied, GFs changes in a way that was appropriate for kurtosis. © 2011 IEEE. Source


Turney S.G.,Center for Brain Science | Ahmed M.,University of Washington | Chandrasekar I.,University of Washington | Chandrasekar I.,Sanford Childrens Health Research Center | And 7 more authors.
Molecular Biology of the Cell | Year: 2016

Nerve growth factor (NGF) promotes growth, differentiation, and survival of sensory neurons in the mammalian nervous system. Little is known about how NGF elicits faster axon outgrowth or how growth cones integrate and transform signal input to motor output. Using cultured mouse dorsal root ganglion neurons, we found that myosin II (MII) is required for NGF to stimulate faster axon outgrowth. From experiments inducing loss or gain of function of MII, specific MII isoforms, and vinculin-dependent adhesion-cytoskeletal coupling, we determined that NGF causes decreased vinculin-dependent actomyosin restraint of microtubule advance. Inhibition of MII blocked NGF stimulation, indicating the central role of restraint in directed outgrowth. The restraint consists of myosin IIB- And IIA-dependent processes: retrograde actin network flow and transverse actin bundling, respectively. The processes differentially contribute on laminin-1 and fibronectin due to selective actin tethering to adhesions. On laminin-1, NGF induced greater vinculin-dependent adhesion-cytoskeletal coupling, which slowed retrograde actin network flow (i.e., it regulated the molecular clutch). On fibronectin, NGF caused inactivation of myosin IIA, which negatively regulated actin bundling. On both substrates, the result was the same: NGF-induced weakening of MIIdependent restraint led to dynamic microtubules entering the actin-rich periphery more frequently, giving rise to faster elongation. © 2016 Turney et al. Source


News Article | January 21, 2016
Site: http://phys.org/technology-news/

Harvard's John A. Paulson School of Engineering and Applied Sciences (SEAS), Center for Brain Science (CBS), and the Department of Molecular and Cellular Biology have been awarded over $28 million to develop advanced machine learning algorithms by pushing the frontiers of neuroscience.


News Article
Site: http://www.scientificcomputing.com/rss-feeds/all/rss.xml/all

Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS), Center for Brain Science (CBS), and the Department of Molecular and Cellular Biology have been awarded over $28 million to develop advanced machine learning algorithms by pushing the frontiers of neuroscience. The Intelligence Advanced Research Projects Activity (IARPA) funds large-scale research programs that address the most difficult challenges facing the intelligence community. Today, intelligence agencies are inundated with data — more than they are able to analyze in a reasonable amount of time. Humans, naturally good at recognizing patterns, can’t keep pace. The pattern-recognition and learning abilities of machines, meanwhile, still pale in comparison to even the simplest mammalian brains. IARPA’s challenge: figure out why brains are so good at learning, and use that information to design computer systems that can interpret, analyze and learn information as successfully as humans. To tackle this challenge, Harvard researchers will record activity in the brain's visual cortex in unprecedented detail, map its connections at a scale never before attempted, and reverse engineer the data to inspire better computer algorithms for learning. “This is a moonshot challenge, akin to the Human Genome Project in scope,” said project leader David Cox, assistant professor of molecular and cellular biology and computer science. “The scientific value of recording the activity of so many neurons and mapping their connections alone is enormous, but that is only the first half of the project. As we figure out the fundamental principles governing how the brain learns, it's not hard to imagine that we’ll eventually be able to design computer systems that can match, or even outperform, humans.” These systems could be designed to do everything from detecting network invasions, to reading MRI images, to driving cars. The research team tackling this challenge includes Jeff Lichtman, the Jeremy R. Knowles Professor of Molecular and Cellular Biology; Hanspeter Pfister, the An Wang Professor of Computer Science; Haim Sompolinsky, the William N. Skirball Professor of Neuroscience; and Ryan Adams, assistant professor of computer science; as well as collaborators from MIT, Notre Dame, New York University, University of Chicago, and Rockefeller University. The multi-stage effort begins in Cox’s lab, where rats will be trained to recognize various visual objects on a computer screen. As the animals are learning, Cox’s team will record the activity of visual neurons using next-generation laser microscopes built for this project with collaborators at Rockefeller University, to see how brain activity changes as the animals learn. Then, a substantial portion of the rat's brain — one-cubic millimeter in size — will be sent down the hall to Lichtman’s lab, where it will be diced into ultra-thin slices and imaged under the world’s first multi-beam scanning electron microscope, housed in the Center for Brain Science. “This is an amazing opportunity to see all the intricate details of a full piece of cerebral cortex,” says Lichtman. “We are very excited to get started but have no illusions that this will be easy.” This difficult process will generate over a petabyte of data — equivalent to about 1.6 million CDs worth of information. This vast trove of data will then be sent to Pfister, whose algorithms will reconstruct cell boundaries, synapses and connections, and visualize them in three dimensions. “This project is not only pushing the boundaries of brain science, it is also pushing the boundaries of what is possible in computer science,” said Pfister. “We will reconstruct neural circuits at an unprecedented scale from petabytes of structural and functional data. This requires us to make new advances in data management, high performance computing, computer vision and network analysis.” If the work stopped here, its scientific impact would already be enormous — but it doesn’t. Once researchers know how visual cortex neurons are connected to each other in three dimensions, the next question is to figure out how the brain uses those connections to quickly process information and infer patterns from new stimuli. Today, one of the biggest challenges in computer science is the amount of training data that deep learning systems require. For example, in order to learn to recognize a car, a computer system needs to see hundreds of thousands of cars. But humans and other mammals don’t need to see an object thousands of times to recognize it — they only need to see it a few times. In subsequent phases of the project, researchers at Harvard and their collaborators will build computer algorithms for learning and pattern recognition that are inspired and constrained by the connectomics data. These biologically-inspired computer algorithms will outperform current computer systems in their ability to recognize patterns and make inferences from limited data inputs. For example, this research could improve the performance of computer vision systems that can help robots see and navigate through new environments. "We have a huge task ahead of us in this project, but at the end of the day, this research will help us understand what is special about our brains," Cox said. "One of the most exciting things about this project is that we are working on one of the great remaining achievements for human knowledge — understanding how the brain works at a fundamental level."


Smoller J.W.,Massachusetts General Hospital | Smoller J.W.,Center for Human Genetic Research | Gallagher P.J.,Massachusetts General Hospital | Duncan L.E.,Massachusetts General Hospital | And 33 more authors.
Biological Psychiatry | Year: 2014

Background: Individuals with panic disorder (PD) exhibit a hypersensitivity to inhaled carbon dioxide, possibly reflecting a lowered threshold for sensing signals of suffocation. Animal studies have shown that carbon dioxide-mediated fear behavior depends on chemosensing of acidosis in the amygdala via the acid-sensing ion channel ASIC1a. We examined whether the human ortholog of the ASIC1a gene, ACCN2, is associated with the presence of PD and with amygdala structure and function.Methods: We conducted a case-control analysis (n = 414 PD cases and 846 healthy controls) of ACCN2 single nucleotide polymorphisms and PD. We then tested whether variants showing significant association with PD are also associated with amygdala volume (n = 1048) or task-evoked reactivity to emotional stimuli (n = 103) in healthy individuals.Results: Two single nucleotide polymorphisms at the ACCN2 locus showed evidence of association with PD: rs685012 (odds ratio = 1.32, gene-wise corrected p =.011) and rs10875995 (odds ratio = 1.26, gene-wise corrected p =.046). The association appeared to be stronger when early-onset (age ≤ 20 years) PD cases and when PD cases with prominent respiratory symptoms were compared with controls. The PD risk allele at rs10875995 was associated with increased amygdala volume (p =.035) as well as task-evoked amygdala reactivity to fearful and angry faces (p =.0048).Conclusions: Genetic variation at ACCN2 appears to be associated with PD and with amygdala phenotypes that have been linked to proneness to anxiety. These results support the possibility that modulation of acid-sensing ion channels may have therapeutic potential for PD. © 2014 Society of Biological Psychiatry. Source

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