Artificial intelligence has been the inspiration for countless books and movies, as well as the aspiration of countless scientists and engineers. Researchers at Caltech have now taken a major step toward creating artificial intelligence—not in a robot or a silicon chip, but in a test tube. The researchers are the first to have made an artificial neural network out of DNA, creating a circuit of interacting molecules that can recall memories based on incomplete patterns, just as a brain can.
In many ways, life is like a computer. An organism's genome is the software that tells the cellular and molecular machinery—the hardware—what to do. But instead of electronic circuitry, life relies on biochemical circuitry—complex networks of reactions and pathways that enable organisms to function. Now, researchers at Caltech have built the most complex biochemical circuit ever created from scratch, made with DNA-based devices in a test tube that are analogous to the electronic transistors on a computer chip.
When a group of gamblers gather around a roulette table, individual players are likely to have different reasons for betting on certain numbers. Recently, researchers at the California Institute of Technology (Caltech) and Ireland's Trinity College Dublin hedged their bets—and came out winners—when they proposed that a certain region of the brain drives these different types of decision-making behaviors.
When it comes to making choices, say Caltech's Antonio Rangel and his colleagues, much depends on which items catch—and keep—your eye. "We're interested in how the brain makes simple choices, like which item to pick from a buffet table," says Rangel, professor of neuroscience and economics. "Why is it that when we look at the buffet table, our gaze shifts back and forth between the items in order to make a choice? What is the role of visual attention in all this?"
We've all heard the predictions: e-commerce is going to be the death of traditional commerce; online shopping spells the end of the neighborhood brick-and-mortar store. While it's true that online commerce has had an impact on all types of retail stores, it's not time to bring out the wrecking ball quite yet, says a team of researchers from Caltech.
Our belief as to whether we will likely succeed or fail at a given task—and the consequences of winning or losing—directly affects the levels of neural effort put forth in movement-planning circuits in the human cortex, according to a new brain-imaging study by Caltech neuroscientists.
The process of learning requires the sophisticated ability to constantly update our expectations of future rewards so we may make accurate predictions about those rewards in the face of a changing environment. Although exactly how the brain orchestrates this process remains unclear, a new study by researchers at the California Institute of Technology (Caltech) suggests that a combination of two distinct learning strategies guides our behavior.
Biologists from Caltech and Yale University have identified two genes, the leucokinin neuropeptide and the leucokinin receptor, that appear to regulate meal sizes and frequency in fruit flies. Both genes have mammalian counterparts that seem to play a similar role in food intake, indicating that the steps that control meal size and meal frequency are not just behaviorally similar but are controlled by the same genes throughout the animal kingdom.
Parents pursuing adoption within the United States have strong preferences regarding the types of babies they will apply for, tending to choose non-African-American girls, and favoring babies who are close to being born as opposed to those who have already been born or who are early in gestation. These preferences are significant and can be quantified in terms of the amount of money the potential adoptive parents are willing to pay in finalizing their adoption.
Economists and neuroscientists from Caltech have shown that they can use information obtained through fMRI measurements of whole-brain activity to create feasible, efficient, and fair solutions to one of the stickiest dilemmas in economics, the public goods free-rider problem—long thought to be unsolvable. This is one of the first-ever applications of neurotechnology to real-life economic problems, the researchers note.