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Category: computing

Biology-based brain model matches animals in learning, enables new discovery

A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the discovery of counterintuitive activity by a group of neurons that researchers working with animals to perform the same task had not noticed in their data before, says a team of scientists at Dartmouth College, MIT, and the State University of New York at Stony Brook.

Notably, the model produced these achievements without ever being trained on any data from animal experiments. Instead, it was built from scratch to faithfully represent how neurons connect into circuits and then communicate electrically and chemically across broader brain regions to produce cognition and behavior. Then, when the research team asked the model to perform the same task that they had previously performed with the animals (looking at patterns of dots and deciding which of two broader categories they fit), it produced highly similar neural activity and behavioral results, acquiring the skill with almost exactly the same erratic progress.

“It’s just producing new simulated plots of brain activity that then only afterward are being compared to the lab animals. The fact that they match up as strikingly as they do is kind of shocking,” says Richard Granger, a professor of psychological and brain sciences at Dartmouth and senior author of a new study in Nature Communications that describes the model.

Particle permutation task can be tackled by quantum but not classical computers, study finds

Quantum computers, systems that process information leveraging quantum mechanical effects, are expected to outperform classical computers on some complex tasks. Over the past few decades, many physicists and quantum engineers have tried to demonstrate the advantages of quantum systems over their classical counterparts on specific types of computations.

Researchers at Autonomous University of Barcelona and Hunter College of CUNY recently showed that quantum systems could tackle a problem that cannot be solved by classical systems, namely determining the even or odd nature of particle permutations without marking all and each one of the particles with a distinct label. This task essentially entails uncovering whether re-arranging particles from their original order to a new order requires an even or odd number of swaps in the position of particle pairs.

These researchers have been conducting research focusing on problems that entail the discrimination between quantum states for several years. Their recent paper, published in Physical Review Letters, demonstrates that quantum technologies could solve one of these problems in ways that are unfeasible for classical systems.

Computational model discovers new types of neurons hidden in decade-old dataset

“We saw some peculiar brain activity in the model,” Miller says. “There was a group of neurons that predicted the wrong answer, yet they kept getting stronger as the model learned. So we went back to the original macaque data, and the same signal was there, hiding in plain sight. It wasn’t a quirk of the model — the monkeys’ brains were doing it too. Even as their performance improved, both the real and simulated brains maintained a reserve of neurons that continued to predict the incorrect answer.”

The new work, published in Nature Communications, puts a name to these overlooked signals: incongruent neurons, or ICNs, and explores theories as to why a primate brain might want to keep alternate options in mind, even if they’re not the right ones at the moment.

Beyond identifying a previously unrecognized class of neurons involved in learning, the study shows that the model behaves like a brain and generates realistic brain activity, even without being trained on neural data. The findings could have major implications for testing potential neurological drugs and for using computational models to investigate how cognition emerges and functions.

Engineers invent wireless transceiver that rivals fiber-optic speed

A new transceiver invented by electrical engineers at the University of California, Irvine boosts radio frequencies into 140-gigahertz territory, unlocking data speeds that rival those of physical fiber-optic cables and laying the groundwork for a transition to 6G and FutureG data transmission protocols.

To create the transceiver, researchers in UC Irvine’s Samueli School of Engineering devised a unique architecture that blends digital and analog processing. The result is a silicon chip system, comprising both a transmitter and a receiver, that’s capable of processing digital signals significantly faster and with much greater energy efficiency than previously available technologies.

The team from UC Irvine’s Nanoscale Communication Integrated Circuits Labs outline its work in two papers published this month in the IEEE Journal of Solid-State Circuits. In one, the engineers discuss the technology they call a “bits-to-antenna” transmitter, and in the second, they cover their “antenna-to-bits” receiver.

New code connects microscopic insights to the macroscopic world

In inertial confinement fusion, a capsule of fuel begins at temperatures near zero and pressures close to vacuum. When lasers compress that fuel to trigger fusion, the material heats up to millions of degrees and reaches pressures similar to the core of the sun. That process happens within a miniscule amount of space and time.

To understand this process, scientists need to know about the large-scale conditions, like temperature and pressure, throughout the target chamber. But they also want detailed information about the material—and the atoms—contained within. Until now, computer models have struggled to bridge that gap across the wide range of conditions encountered in such experiments.

Magnetic ‘sweet spots’ enable optimal operation of hole spin qubits

Quantum computers, systems that process information leveraging quantum mechanical effects, could reliably tackle various computational problems that cannot be solved by classical computers. These systems process information in the form of qubits, units of information that can exist in two states at once (0 and 1).

Hole spins, the intrinsic angular momentum of holes (i.e., missing electrons in semiconductors that can be trapped in nanoscale regions called quantum dots), have been widely used as qubits. These spins can be controlled using electric fields, as they are strongly influenced by a quantum effect known as spin-orbit coupling, which links the motion of particles to their magnetism.

Unfortunately, due to this spin-orbit coupling, hole spin qubits are also known to be highly vulnerable to noise, including random electrical disturbances that can prompt decoherence. This in turn can result in the loss of valuable quantum information.

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