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Researchers have created atomically thin artificial neurons capable of processing both light and electric signals for computing. The material enables the simultaneous existence of separate feedforward and feedback paths within a neural network, boosting the ability to solve complex problems.

For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: electronic components capable of storing a value by modifying their conductance and then utilizing that value for in-memory processing.

However, a key challenge to replicating the complex processes of biological neurons and brains using memristors has been the difficulty in integrating both feedforward and feedback neuronal signals. These mechanisms underpin our cognitive ability to learn complex tasks, using rewards and errors.

face_with_colon_three 2022 Microbes can now clean up plastics in lakes, streams, and oceans eventually.

Ultra-high resolution mass spectrometry revealed that plastic bags leach labile compounds. Bioassays performed in Scandinavian lakes indicated that these compounds are incorporated into biomass faster and more efficiently than natural organic matter.

Neural networks are distributed computing structures inspired by the structure of a biological brain and aim to achieve cognitive performance comparable to that of humans but in a much shorter time.

These technologies now form the basis of machine learning and artificial intelligence systems that can perceive the environment and adapt their own behavior by analyzing the effects of previous actions and working autonomously. They are used in many areas of application, such as speech and image recognition and synthesis, autonomous driving and augmented reality systems, bioinformatics, genetic and molecular sequencing, and high-performance computing technologies.

Compared to conventional computing approaches, in order to perform complex functions, neural networks need to be initially “trained” with a large amount of known information that the network then uses to adapt by learning from experience. Training is an extremely energy-intensive process and as computing power increases, the neural networks’ consumption grows very rapidly, doubling every six months or so.

An artificial photosynthesis system that combines semiconducting nanoparticles with a non-photosynthetic bacterium could offer a promising new route for producing sustainable solar-driven hydrogen fuel.

Other artificial photosynthesis systems that integrate nanomaterials into living microbes have been developed before, which reduce carbon dioxide or produce hydrogen, for example. However, usually it is the microorganism itself that makes the product via a metabolic pathway, which is aided by a light-activated nanomaterial that supplies necessary electrons.

Now, the labs of Kara Bren and Todd Krauss at the University of Rochester, US, have turned this concept on its head. They have designed a new hybrid bio-nano system that combines a finely-tuned photocatalytic semiconducting nanoparticles to make hydrogen with a bacterium which, while it does not photosynthesise or make hydrogen itself, it provides the necessary electrons to the nanomaterial to synthesise hydrogen.

Universal computation has significant real-world implications in fields such as computer science, physics, biology, and beyond. It is highly relevant to simulation metaphysics and its idea that the physical world could be a type of computer simulation.

Why do some people live lawful lives, while others gravitate toward repeated criminal behavior? Do people choose to be moral or immoral, or is morality simply a genetically inherited function of the brain? Research suggests that psychopathy as a biological condition explained by defective neural circuits that mediate empathy, but what does that mean when neuroscience is used as evidence in criminal court? How can understanding neuroscience give us an insight into the actions and behaviors of our political leaders?

Forensic psychiatrist Dr. Octavio Choi https://med.stanford.edu/profiles/ochoi will explore how emerging neuroscience challenges long-held assumptions underlying the basis—and punishment—of criminal behavior.

Continue reading “The Neuroscience of Real Life Monsters: Psychopaths, CEOs, & Politicians (Science on Tap Livestream)” »

Neuroscientists have uncovered how exploratory actions enable animals to learn their spatial environment more efficiently. Their findings could help build better AI agents that can learn faster and require less experience.

Researchers at the Sainsbury Wellcome Center and Gatsby Computational Neuroscience Unit at UCL found the instinctual exploratory runs that animals carry out are not random. These purposeful actions allow mice to learn a map of the world efficiently. The study, published today, April 28, in Neuron, describes how neuroscientists tested their hypothesis that the specific exploratory actions that animals undertake, such as darting quickly towards objects, are important in helping them learn how to navigate their environment.

“There are a lot of theories in psychology about how performing certain actions facilitates learning. In this study, we tested whether simply observing obstacles in an environment was enough to learn about them, or if purposeful, sensory-guided actions help animals build a cognitive map of the world,” said Professor Tiago Branco, Group Leader at the Sainsbury Wellcome Center and corresponding author on the paper.

A fighting faction in Sudan has occupied the country’s main public health lab, putting pathogen collections at risk, the WHO said.

Scientists have demonstrated that nanowire networks can exhibit short-and long-term memory, similar to the human brain. These networks, comprised of highly conductive silver wires covered in plastic and arranged in a mesh-like pattern, mimic the physical structure of the human brain. The team successfully tested the nanowire network’s memory capabilities using a task similar to human psychology experiments. This breakthrough in nanotechnology suggests that non-biological hardware systems could potentially replicate brain-like learning and memory, and has numerous real-world applications, such as improving robotics and sensor devices in unpredictable environments.

In a groundbreaking study, an international team has shown that nanowire networks can mimic the short-and long-term memory functions of the human brain. This breakthrough paves the way for replicating brain-like learning and memory in non-biological systems, with potential applications in robotics and sensor devices.

Continue reading “Neural Nanotechnology: Nanowire Networks Learn and Remember Like a Human Brain” »