The size and surface area of the cerebral cortex varies dramatically across mammals. It is well known that the human cortex is by far the largest among primates. However, there is no agreement about whether the human prefrontal cortex is larger, in relative terms, than those of other primates. Donahue et al. compared structural brain scan datasets from humans, chimpanzees, and macaques. They found a greater proportion of prefrontal cortex gray matter volume in humans than in the two nonhuman primate species, and they observed an even greater difference between species for white matter volume in the prefrontal cortex.
In science news around the world, the European Commission proposes spending €13 billion on military R&D from 2021 to 2027, of which €4.1 billion would be for research--a 20-fold increase over an existing pilot fund. The British Business Bank, a public company owned by the U.K. government, unveils a £2.5 billion fund called British Patient Capital that will make long-term investments in technology companies. Google announces plans to open an artificial intelligence research center in Accra later this year, its first such laboratory in Africa. Israel says it will begin pumping desalinated water into the Sea of Galilee starting next year to alleviate the effects of a 5-year drought. The launch of the European Space Agency's Euclid mission to study dark energy will be delayed by 2 years to 2022 because of problems with its infrared sensors.
I landed my dream job: a tenure-track position at a primarily undergraduate institution near my hometown where I would develop a new neuroscience major. I entered that position the way one enters a marriage: expecting it to last forever, assuming I would give it everything I had, hoping that--while it would not always be easy--it would be worth it. Soon, though, something seemed amiss. It felt kind of like sexism--but not exactly. Whatever it was, I experienced it from both women and men, from the department chair to the administrative assistant.
Ghost imaging is a technique used to produce an object's image without using a spatially resolving detector. Here we develop a technique we term "ghost cytometry," an image-free ultrafast fluorescence "imaging" cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry.
Scene representation--the process of converting visual sensory data into concise descriptions--is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints.
The ability to understand spatial environments based on visual perception arguably is a key function of the cognitive system of many animals, including mammalians and others. A common presumption about artificial intelligence is that its goal is to build machines with a similar capacity of "understanding." The research community in artificial intelligence, however, has settled on a more pragmatic approach. Instead of attempting to model or quantify understanding directly, the objective is to construct machines that merely solve tasks that seem to require understanding. Understanding can only be measured indirectly, for example, by analyzing the ability of a system to generalize the solving of new tasks, which is sometimes called transfer learning (1).
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).
Biologists have long debated the role of behavior in evolution, yet understanding of its role as a driver of adaptation is hampered by the scarcity of experimental studies of natural selection on behavior in nature. After showing that individual Anolis sagrei lizards vary consistently in risk-taking behaviors, we experimentally established populations on eight small islands either with or without Leiocephalus carinatus, a major ground predator. We found that selection predictably favors different risk-taking behaviors under different treatments: Exploratory behavior is favored in the absence of predators, whereas avoidance of the ground is favored in their presence. On predator islands, selection on behavior is stronger than selection on morphology, whereas the opposite holds on islands without predators. Our field experiment demonstrates that selection can shape behavioral traits, paving the way toward adaptation to varying environmental contexts.
The sense of touch is the ability to perceive consistency, texture, and shape of objects that we manipulate, and the forces we exchange with them. Touch is a source of information that we effortlessly decode to smoothly and naturally grasp and manipulate objects, maintain our posture while walking, or avoid stumbling into obstacles, allowing us to plan, adapt, and correct actions in an ever-changing external world. As such, artificial devices, such as robots or prostheses, that aim to accomplish similar tasks must possess artificial tactile-sensing systems. On page 998 of this issue, Kim et al. (1) report on a "neuromorphic" tactile sensory system based on organic, flexible, electronic circuits that can measure the force applied on the sensing regions. The encoding of the signal is similar to that used by human nerves that are sensitive to tactile stimuli (mechanoreceptors), so the device outputs can substitute for them and communicate with other nerves (e.g., residual nerve fibers of amputees or motor neurons).
In science news around the world, Congress approves a "right to try" bill giving patients with life-threatening illnesses a new means to obtain experimental treatments, a measure that President Donald Trump is expected to sign. A Swedish court blocks plans for a controversial new headquarters building for the Nobel Foundation on central Stockholm's waterfront, saying the design would harm the waterfront's historic character. NASA's Curiosity rover is once again sampling rocks after engineers found a new way to use a drill on its robotic arm that stopped operating properly in late 2016. The world's most sensitive dark matter detector fails to snare its quarry during a year of observations. A new package of instruments arrives at the International Space Station to help scientists study record-low temperatures and look for novel quantum effects.