Researchers from Penn Medicine are utilizing artificial intelligence (AI) to better identify the size and shape of children's brain networks, which could improve the understanding of psychiatric disorders. In the team's study, published recently in the journal Neuron, the researchers showed how each child's unique brain network can be used to predict their cognition. This work showed that functional brain anatomy expresses strong variance in children and that it is refined during development stages.
Eating a diet of junk food for just one week was enough to damage part of the brain that stops us eating more when we are already full, research suggests. Study participants who ate an abundance of fast food and high-fat milkshakes had increased cravings for more after seven days. They performed worse on cognitive tests, with results suggesting an area of the brain called the hippocampus was impaired. The hippocampus normally stops us from gorging on more food when we are full by suppressing memories of how tasty it is. When it's not working properly, the memories are more powerful and we are left unable to resist more cake, chocolates and crisps in front of us, the researchers believe.
Credit: Jana Dünnhaupt/University of Magdeburg Computer scientists at Otto von Guericke University Magdeburg are aiming to use the findings and established methods of brain research to better understand the way in which artificial intelligence works. As part of a research project, the scientists led by Professor Dr.-Ing. Sebastian Stober from the Artificial Intelligence Lab at the University of Magdeburg will apply methods from cognitive neuroscience to analyze artificial neural networks and better understand the way they work.
The demographics of Canada are changing quickly. By 2050, 26% of Canada's population is expected to be aged 65 or better, up from 18% today. With smaller families, busier schedules, and tighter budgets, the pressure is on to find solutions to ensure this growing group of people receives quality care. Fortunately, artificial intelligence is helping the retirement industry serve up innovative solutions to meet the burgeoning need. Though results from our 2019 Sklar Wilton AI tracker* indicate that 57% of people aged 65 and older don't understand the current state of artificial intelligence, 71% believe AI may affect them.
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One obvious reason is to restore the strength of our bodies and limbs. But another very important role of sleep is to consolidate memories and organize all the information that your brain has ingested while being awake. People who lack proper sleep see their cognitive abilities degrade and their memories fail. The wonders and mysteries of sleep remain an active area of research.
Traditionally, neuroscientists and AI researchers have had different goals and different ways of understanding, even if the phenomena or tasks of interest are similar, such as vision. Neuroscientists try to explain, in mechanistic and computational detail, how various processes are implemented in the brain. Face perception, for example, is a fundamental ability that most humans perform well, and usually automatically. Although much is known about the neuroscience of face perception, a great deal is not known, and hence it is an area of active research, one that will probably be investigated for decades to come. It may not be possible, then, for neuroscientists to get a full mechanistic picture of a function like face perception, at least in the short term.
The terminology humans have conceived to explain and study our own brain may be mis-aligned with how these constructs are actually represented in nature. For example, in many human societies, when a baby is born either a "male" or a "female" box is checked on the birth certificate. Reality, however, may be less black and white. In fact, the assumption of dichotomic differences between only two sex/gender categories may be at odds with our endeavors that try to carve nature at its joints. Such is the case with a new paper, published recently in the journal Cerebral Cortex, where researchers argue that there are at least nine directions of brain-gender variation.
The study included more than 300 patients with depression who were randomized to treatment with an SSRI or placebo as part of the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) trial. Using participants' EEG data, which measured electrical activity in the brain cortex before treatment, researchers developed a machine-learning algorithm that analyzed the information.
Working memory is a central topic of cognitive neuroscience because it is critical for solving real world problems in which information from multiple temporally distant sources must be combined to generate appropriate behavior. However, an often neglected fact is that learning to use working memory effectively is itself a difficult problem. The Gating" framework is a collection of psychological models that show how dopamine can train the basal ganglia and prefrontal cortex to form useful working memory representations in certain types of problems. We bring together gating with ideas from machine learning about using finite memory systems in more general problems. Thus we present a normative Gating model that learns, by online temporal difference methods, to use working memory to maximize discounted future rewards in general partially observable settings. The model successfully solves a benchmark working memory problem, and exhibits limitations similar to those observed in human experiments. Moreover, the model introduces a concise, normative definition of high level cognitive concepts such as working memory and cognitive control in terms of maximizing discounted future rewards."
In neuropsychiatric disorders such as schizophrenia or depression, there is often a disruption in the way that regions of the brain synchronize with one another. To facilitate understanding of network-level synchronization between brain regions, we introduce a novel model of multisite low-frequency neural recordings, such as local field potentials (LFPs) and electroencephalograms (EEGs). The proposed model, named Cross-Spectral Factor Analysis (CSFA), breaks the observed signal into factors defined by unique spatio-spectral properties. These properties are granted to the factors via a Gaussian process formulation in a multiple kernel learning framework. In this way, the LFP signals can be mapped to a lower dimensional space in a way that retains information of relevance to neuroscientists.