The study, led by Costas Anastassiou, PhD, a research scientist in the departments of Neurology, Neurosurgery and Biomedical Sciences at Cedars-Sinai, used data from laboratory mice to establish a new method for examining relationships between neuron type and function, and focused on the mouse primary visual cortex, which receives and processes visual information. Photo by Cedars-Sinai.
The study, led by Costas Anastassiou, PhD, a research scientist in the departments of Neurology, Neurosurgery and Biomedical Sciences at Cedars-Sinai, used data from laboratory mice to establish a new method for examining relationships between neuron type and function, and focused on the mouse primary visual cortex, which receives and processes visual information. Photo by Cedars-Sinai.
Tyler O'Neal, Staff Editor LATEST

Cedars-Sinai researchers develop computational models to determine the identities, roles of individual neurons in the brain’s complex machine

Investigators at Cedars-Sinai have created supercomputer-generated models to bridge the gap between “test tube” data about neurons and the function of those cells in the living brain. Their study could help in the development of treatments for neurological diseases and disorders that target specific neuron types based on their roles. Keith Black, MD

“This work allows us to start looking at the brain like the complex machine that it is, rather than as one homogenous piece of tissue,” said Costas Anastassiou, Ph.D., a research scientist in the departments of Neurology, Neurosurgery, and Biomedical Sciences at Cedars-Sinai and senior author of the study. “Once we are able to distinguish between the different cell types, instead of saying that the entire brain has a disease, we can ask which neuron types are affected by the disease and tailor treatments to those neurons.” 

Neurons are the main functional units of the brain. The signals passing through these cells—in the form of electrical waves—give rise to all thought, sensation, movement, memory, and emotion. 

The study used data from laboratory mice to establish a new method for examining relationships between neuron type and function and focused on the mouse's primary visual cortex, which receives and processes visual information. It is one of the best-studied parts of the brain—both in vitro, where tissue is studied in a dish or test tube outside the living organism, and in vivo, where it is studied in the living animal. 

The investigators’ goal was to link the two worlds. 

“Based on in vitro studies of genetic makeup and physical structure, we know something about what various classes of neurons look like, but not their function in the living brain,” Anastassiou said. “When we record the activity of brain cells in vivo, we can see what neurons are responding to and what their function is, but not which classes of neurons they are.” 

To link form to function, investigators first used in vitro information to create computational models of various types of neurons and to simulate their signaling patterns. 

They next took advantage of the newest technology in single-neuron recording to observe activity in the brains of laboratory mice while the mice were exposed to different sorts of visual stimuli. Based on the shapes of the signals or “spikes” of neurons in response to visual input, investigators separated the cells they recorded into six groups. 

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“Once we had our models and our in vivo data, the fundamental question was which computational models produced the most similar signaling shape and waveform to each of the six in vivo clusters we identified, and vice versa,” Anastassiou said. “Not all of the in vivo clusters and models matched perfectly, but some did.”  

More data, and possibly experiments involving more-sophisticated visual stimuli, might be required to match all the computational models and cell clusters, and Anastassiou said that future studies will be dedicated to perfecting the method established in the current paper. 

“There’s a wealth of information about the identity of cell types in the human brain, but not about the role of those cell types in cognitive functioning or how they are affected by the disease,” Anastassiou said. “Now there is a window through which we can look at these things and ask these questions. It’s clear that we have a long way to go, but we’re excited about the next steps in this journey.”

The ultimate goal is to pave the way for discoveries that change patients’ lives.

“Our research scientists are continually striving to expand our knowledge of the workings of the human brain at the most detailed level,” said Keith L. Black, MD, chair of the Department of Neurosurgery and the Ruth and Lawrence Harvey Chair in Neuroscience at Cedars-Sinai. “Pinning down the specific type and function of each neuron may one day lead to the discovery of lifesaving treatments for brain diseases and neurological disorders.”

Funding: This research was supported by National Institutes of Health grant number RO1 NS120300-01; National Natural Science Foundation of China grant number 12101570; and Scientific Project of Zhejiang Lab grants 2021KE0PI03, 2022KI0AC01, 2022KI0AC02 and 2022ND0AN01.

Matching form and function of brain cell types

Tyler O'Neal, Staff Editor LATEST
This animation is based on a computational model showing the signaling of a neuron in the mouse's primary visual cortex. Courtesy of the Anastassiou Lab.

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Electron density of the Ionosphere around the Earth for a certain point of time: high values in red, low values in blue. The white line marks the geomagnetic equator. (Figure: CCBY 4.0 Smirnov et al. (2023)
Electron density of the Ionosphere around the Earth for a certain point of time: high values in red, low values in blue. The white line marks the geomagnetic equator. (Figure: CCBY 4.0 Smirnov et al. (2023)

GFZ German Research Centre for Geosciences builds a more precise model of the Earth's ionosphere

Tyler O'Neal, Staff Editor LATEST

The ionosphere – the region of geospace spanning from 60 to 1000 kilometers above the Earth – impairs the propagation of radio signals from global navigation satellite systems (GNSS) with its electrically charged particles. This is a problem for the ever-higher precision required by these systems – both in research and for applications such as autonomous driving or precise orbit determination of satellites. Models of the ionosphere and its uneven, dynamic charge distribution can help correct the signals for ionospheric delays, which are one of the main error sources in GNSS applications. Researchers led by Artem Smirnov and Yuri Shprits of the GFZ German Research Centre for Geosciences have presented a new model based on neural networks and satellite measurement data from 19 years. In particular, it can reconstruct the topside ionosphere, the upper, electron-rich part of the ionosphere much more precisely than before. It is thus also an important basis for progress in ionospheric research, with applications in studies on the propagation of electromagnetic waves or for the analysis of certain space weather events, for example. 

Importance and complexity of the ionosphere

The Earth's ionosphere is the region of the upper atmosphere that extends from about 60 to 1000 kilometers in altitude. Here, charged particles such as electrons and positive ions dominate, caused by the radiation activity of the Sun – hence the name. The ionosphere is important for many scientific and industrial applications because the charged particles influence the propagation of electromagnetic waves such as radio signals. The so-called ionospheric propagation delay of radio signals is one of the most important sources of interference for satellite navigation. This is proportional to the electron density in the space traversed. Therefore, a good knowledge of electron density can help in correcting the signals. In particular, the upper region of the ionosphere, above 600 kilometers, is of interest, since 80 percent of the electrons are gathered in this so-called topside ionosphere.

The problem is that the electron density varies greatly – depending on the longitude and latitude above the Earth, the time of day and year, and solar activity. This makes it difficult to reconstruct and predict them, the basis for correcting radio signals, for example.

Previous models

There are various approaches to modeling electron density in the ionosphere, among others, the International Reference Ionosphere Model IRI, which has been recognized since 2014. It is an empirical model that establishes a relationship between input and output variables based on the statistical analysis of observations. However, it still has weaknesses in the important area of the topside ionosphere because of the limited coverage of previously collected observations in that region.

Recently, however, large amounts of data have become available for this area. Therefore, Machine learning (ML) approaches lend themselves to deriving regularities from this, especially for complex non-linear relationships. Electron density of the Ionosphere around the Earth for a certain point of time: high values in red, low values in blue. The white line marks the geomagnetic equator. (Figure: CCBY 4.0 Smirnov et al. (2023)

A new approach using machine learning and neural networks

A team from the GFZ German Research Centre for Geosciences around Artem Smirnov, Ph.D. student and first author of the study, and Yuri Shprits, head of the “Space Physics and Space Weather” section and Professor at University Potsdam, took a new ML-based empirical approach. For this, they used data from satellite missions from 19 years, in particular CHAMP, GRACE, and GRACE-FO, which were and are significantly co-operated by the GFZ, and COSMIC. The satellites measure – among other things – the electron density in different height ranges of the ionosphere and cover different annual and local times as well as solar cycles.

With the help of Neural Networks, the researchers then developed a model for the electron density of the topside ionosphere, which they call the NET model. They used the so-called MLP method (Multi-Layer Perceptrons), which iteratively learns the network weights to reproduce the data distributions with very high accuracy.

The researchers tested the model with independent measurements from three other satellite missions.

Evaluation of the new model

“Our model is in remarkable agreement with the measurements: It can reconstruct the electron density very well in all height ranges of the topside ionosphere, all around the Globe, at all times of the year and day, and at different levels of solar activity, and it significantly exceeds the International Reference Ionosphere Model IRI in accuracy. Moreover, it covers space continuously,” first author Artem Smirnov sums up.

Yuri Shprits adds: “This study represents a paradigm shift in ionospheric research because it shows that ionospheric densities can be reconstructed with very high accuracy. The NET model reproduces the effects of numerous physical processes that govern the dynamics of the topside ionosphere and can have broad applications in ionospheric research.”

Possible applications in ionosphere research

The researchers see possible applications, for instance, in wave propagation studies, for calibrating new electron density data sets with often unknown baseline offsets, for tomographic reconstructions in the form of a background model, as well as to analyze specific space weather events and perform long-term ionospheric reconstructions. Furthermore, the developed model can be connected to plasmaspheric altitudes and thus can become a novel topside option for the IRI.

The developed framework allows the seamless incorporation of new data and new data sources. The retraining of the model can be done on a standard PC and can be performed regularly. Overall, the NET model represents a significant improvement over traditional methods and highlights the potential of neural network-based models to provide a more accurate representation of the ionosphere for communication and navigation systems that rely on GNSS.

Animation of the changing electron density of the Ionosphere

Tyler O'Neal, Staff Editor LATEST
Animation of the changing electron density of the Ionosphere around the Earth over three full days: high values in red, low values in blue. The white line marks the geomagnetic equator.

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UGA researchers use ML to discover new exoplanet

Tyler O'Neal, Staff Editor LATEST

A University of Georgia research team has confirmed evidence of a previously unknown planet outside of our solar system, and they used machine learning tools to detect it. 

A recent study by the team showed that machine learning could correctly determine if an exoplanet is present by looking in protoplanetary disks, the gas around newly formed stars.

The newly published findings represent a first step toward using machine learning to identify previously overlooked exoplanets.

“We confirmed the planet using traditional techniques, but our models directed us to run those simulations and showed us exactly where the planet might be,” said Jason Terry, a doctoral student in the UGA Franklin College of Arts and Sciences Department of Physics and astronomy and lead author on the study.

“When we applied our models to a set of older observations, they identified a disk that wasn't known to have a planet despite having already been analyzed. Like previous discoveries, we ran simulations of the disk and found that a planet could re-create the observation.”

According to Terry, the models suggested a planet’s presence, indicated by several images that strongly highlighted a particular region of the disk that turned out to have the characteristic sign of a planet — an unusual deviation in the velocity of the gas near the planet. 

“This is an incredibly exciting proof of concept. We knew from our previous work that we could use machine learning to find known forming exoplanets,” said Cassandra Hall, assistant professor of computational astrophysics and principal investigator of the Exoplanet and Planet Formation Research Group at UGA. “Now, we know for sure that we can use it to make brand new discoveries.”

The discovery highlights how machine learning has the power to enhance scientists’ work, utilizing artificial intelligence as an added tool to expand researchers’ accuracy and more efficiently economize their time when engaged in such a vast endeavor as investigating deep, outer space.

The models could detect a signal in data that people had already analyzed; they found something that previously had gone undetected.

“This demonstrates that our models — and machine learning in general — have the ability to quickly and accurately identify important information that people can miss. This can dramatically speed up analysis and subsequent theoretical insights,” Terry said. “It only took about an hour to analyze that entire catalog and find strong evidence for a new planet in a specific spot, so we think there will be an important place for these types of techniques as our datasets get even larger.”