New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
McGovern Institute investigator Michale Fee has been selected to receive a 2018 McKnight Technological Innovations in Neuroscience Award for his research on "new technologies for imaging and analyzing neural state-space trajectories in freely-behaving small animals." "I am delighted to get support from the McKnight Foundation," says Fee, who is also the Glen V. and Phyllis F. Dorflinger Professor in the Department of Brain and Cognitive Neurosciences at MIT. "We're very excited about this project which aims to develop technology that will be a great help to the broader neuroscience community." Fee studies the neural mechanisms by which the brain, specifically that of juvenile songbirds, learns complex sequential behaviors. The way that songbirds learn a song through trial and error is analogous to humans learning complex behaviors, such as riding a bicycle. While it would be insightful to link such learning to neural activity, current methods for monitoring neurons can only monitor a limited field of neurons, a big issue since such learning and behavior involve complex interactions between larger circuits.
Scientists are about to get a serious assist in their quest to simulate brains. HPE has deployed Blue Brain 5, a supercomputer dedicated to simulations and reconstructions of mammalian brains as part of the École Polytechnique Fédérale de Lausanne's Blue Brain Project. The system is based on HPE's existing SGI 8600 (above) and packs a hefty 372 compute nodes between its Xeon Gold, Xeon Phi and Tesla V100 processors, not to mention a whopping 94TB of memory. More importantly, it's flexible -- Blue Brain 5 has four configurations to prioritize different computing tasks, and it can host subsystems geared toward relevant tasks (including deep learning and visualization) while operating as a cohesive whole. This kind of power is necessary, even if simulating a complete brain is still a long ways off.
A better understanding of the mammalian brain could help researchers discover how to more effectively treat diseases such as Alzheimer's and Parkinson's. Yet understanding the human brain is no small task: it comprises 100 billion neurons and more than 100 trillion synapses. To gain better insight into the brain's functionality, a Swiss research initiative, the École polytechnique fédérale de Lausanne (EPFL) Blue Brain Project (BBP), has invested in an HPE supercomputer called Blue Brain 5. It will use it to build biologically detailed digital models and simulations of the mammalian brain, starting with the rodent brain. Blue Brain 5 is based on the HPE SGI 8600 System and includes 372 compute nodes delivering 1.06 petaflops of peak performance.
The discovery of the anorexigenic (loss of feeding) adipose-derived hormone leptin (1) and its action on part of the brain, the central melanocortin system (2), confirmed earlier predictions of a crucial role of peptides produced by the neurons in this system, neuropeptide Y (NPY) and proopiomelanocortin (POMC), in the control of appetite and feeding (3). Further studies reaffirmed the relevance of these circuits in metabolism regulation by the use of increasingly sophisticated methods (4, 5). On page 76 of this issue, Luo et al. (6) reveal the unsuspected role of another hypothalamic neuronal population in control of feeding, consisting of cells that express the neuropeptide somatostatin (SST), in the nucleus tuberalis lateralis (NTL). This finding adds to the neurological components that regulate food intake and thus body weight.
The evaluation of the individual "fingerprint" of a human functional connectome (FC) is becoming a promising avenue for neuroscientific research, due to its enormous potential inherent to drawing single subject inferences from functional connectivity profiles. Here we show that the individual fingerprint of a human functional connectome can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of brain connectivity modes. We use data from the Human Connectome Project to demonstrate that the optimal reconstruction of the individual FCs through connectivity eigenmodes maximizes subject identifiability across resting-state and all seven tasks evaluated. The identifiability of the optimally reconstructed individual connectivity profiles increases both at the global and edgewise level, also when the reconstruction is imposed on additional functional data of the subjects. Furthermore, reconstructed FC data provide more robust associations with task-behavioral measurements.
Manan Suri has built key elements of computer chips that mimic the learning ability and energy efficiency of the brain. And he did it by harnessing a quirk of next-generation memory technology. That technology is known as emerging non-volatile memory (eNVM). Because of peculiarities in their nanoscale physics, eNVM devices often behave in random ways, which in computers is usually a flaw. But Suri realized that this irregularity could help researchers build so-called neuromorphic chips, which emulate the neurons and synapses in our brains.
The unclear development direction of human society is a deep reason for that it is difficult to form a uniform ethical standard for human society and artificial intelligence. Since the 21st century, the latest advances in the Internet, brain science and artificial intelligence have brought new inspiration to the research on the development direction of human society. Through the study of the Internet brain model, AI IQ evaluation, and the evolution of the brain, this paper proposes that the evolution of population knowledge base is the key for judging the development direction of human society, thereby discussing the standards and norms for the construction of artificial intelligence ethics.
Bobby Kasthuri has a problem. In an effort to understand, on the finest level, what makes us human, he's set out to create a complete map of the human brain: to chart where every neuron connects to every other neuron. The problem is, the brain has more connections than the Milky Way has stars. Just one millionth of the organ contains more information than all the written works in the Library of Congress. A map of the brain would represent the single largest dataset ever collected about anything in the history of the world.
What makes humans unique as a species and as individuals? Our uniqueness stems from language, tool use, reasoning, and other cognitive abilities that are largely mediated by specialized regions of the cerebral cortex. These regions of higher cognitive function have expanded disproportionately during human evolution (compared with nonhuman primates) and during postnatal maturation, when cortical surface area expands threefold between infancy and adulthood (1). Our uniqueness as individuals reflects countless differences in brain structure, function, and connectivity. One basic anatomical difference between similarly aged individuals is a more than 1.5-fold variation in total brain size (and total cortical volume) (2).
Tourette's syndrome is a neuro-psychiatric disease defined by the occurrence of so-called "tics". Tics are sudden, fast and recurring non-rhythmic movements or sound expressions. The development of tics is associated with specific abnormalities of brain activity. Basal ganglia in particular are attributed a role in the development of tics. The underlying mechanisms are still largely unexplored.