Research on Heliconius butterflies illustrates how variations in brain circuits are aligned with their unique foraging behaviors, enhancing their spatial and visual memory.
A tropical butterfly species with uniquely expanded brain structures shows a fascinating mosaic pattern of neural expansion linked to a key cognitive innovation.
The study, published today (October 18) in Current Biology, explores the neural basis of behavioral innovation in Heliconius butterflies, the only genus known to feed on both nectar and pollen. As part of this behavior, these butterflies exhibit an impressive ability to learn and remember the locations of their food sources—abilities tied to the expansion of a brain region called the mushroom bodies, which play a crucial role in learning and memory.
Plants can emit electric potential when pulling water from their roots to nourish their stems and leaves.
Experiments showed that the electrical potential in plants varies in a cyclic rhythm that matches their daily biological processes. This potential increases with decreased ion concentration or increased pH in the fluid, linking it to the plant’s water transpiration and ion transport mechanisms.
“Our eureka moment was when our first experiments showed it is possible to produce electricity in a cyclic rhythm and the precise linkage between this and the plant’s inherent daily rhythm,” Chakraborty added. “We could exactly pinpoint how this is related to water transpiration and the ions the plant carries via the ascent of sap.”
Chakraborty also noted that not only did the researchers rediscover the plant’s electrical rhythm, articulating it in terms of voltages and currents, alongside potentially tapping electrical power output from them in a sustainable manner with no environmental impact and no disruption to the ecosystem.
It’s easy to marvel at the technical wizardry behind breakthroughs such as AlphaFold.
Pioneering crystallographer Helen Berman helped to set up the massive collection of protein structures that underpins the Nobel-prize-winning tool’s success.
Next Generation Biomanufacturing Technologies — Dr. Leonard Tender, Ph.D. — Biological Technologies Office, Defense Advanced Research Projects Agency — DARPA
Dr. Leonard Tender, Ph.D. is a Program Manager in the Biological Technologies Office at DARPA (https://www.darpa.mil/staff/dr-leonar…) where his research interests include developing new methods for user-defined control of biological processes, and climate and supply chain resilience.
Prior to coming to DARPA, Dr. Tender was a principal investigator and led the Laboratory for Molecular Interfaces in the Center for Bio/Molecular Science and Engineering at the U.S. Naval Research Laboratory. There, among other accomplishments, he facilitated numerous international collaborations with key external stakeholders in academia, industry, and government and his highly interdisciplinary research team, comprised of electrochemists, microbiologists, and engineers, is widely recognized for its many contributions to the field of microbial electrochemistry.
Dr. Tender earned a doctorate degree in analytical chemistry from the University of North Carolina, Chapel Hill; a bachelor’s degree in chemistry from the Massachusetts Institute of Technology; completed a post-doctoral fellowship in the Department of Chemistry from the University of California, Berkeley; and served as a visiting scientist in the Stanford University Department of Chemistry.
Dr. Tender co-founded the International Society for Microbial Electrochemistry and Technology and is a recipient of the Arthur S. Flemming Award, which honors outstanding federal employees, by the George Washington University’s Trachtenberg School of Public Policy and Public Administration.
Biological components are less reliable than electrical ones, and rather than instantaneously receive the incoming signals, the signals arrive with a variety of delays. This forces the brain to cope with said delays by having each neuron integrate the incoming signals over time and fire afterwards, as well as using a population of neurons, instead of one, to overcome neuronal cells that temporarily don’t fire.
Life expectancy growth has slowed since 1990, with average gains of only 6.5 years in the longest-living populations, suggesting a possible biological limit. A new study emphasizes shifting focus from merely extending life to improving the quality of life through advancements in aging science. Life expectancy saw dramatic increases throughout…
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Researchers at the Department of Energy’s Oak Ridge National Laboratory, the University of Tennessee, and Texas A&M University demonstrated bio-inspired devices that accelerate routes to neuromorphic, or brain-like, computing.
Results published in Nature Communications report the first example of a lipid-based “memcapacitor,” a charge storage component with memory that processes information much like synapses do in the brain. Their discovery could support the emergence of computing networks modeled on biology for a sensory approach to machine learning.
“Our goal is to develop materials and computing elements that work like biological synapses and neurons—with vast interconnectivity and flexibility—to enable autonomous systems that operate differently than current computing devices and offer new functionality and learning capabilities,” said Joseph Najem, a recent postdoctoral researcher at ORNL’s Center for Nanophase Materials Sciences, a DOE Office of Science User Facility, and current assistant professor of mechanical engineering at Penn State.
Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience “catastrophic forgetting” when taught additional tasks: They can successfully learn the new assignments, but “forget” how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.
Biological brains, on the other hand, are remarkably flexible. Humans and animals can easily learn how to play a new game, for instance, without having to re-learn how to walk and talk.
Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of algorithm that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars.
