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Ripples in the fabric of the universe may reveal the start of time

Possible light generated by gravitational waves (credit: L. Rezolla (AEI) & M. Koppitz (AEI & Zuse-Institut Berlin)

Gravitational waves can peer back to the beginning of everything we know, say researchers.

“We can’t see the early universe directly,” but maybe we can see it indirectly if we look at how gravitational waves from that time have affected matter and radiation that we can observe today,” said Deepen Garg, lead author of a paper reporting the results in the Journal of Cosmology and Astroparticle Physics.

Garg and his advisor Ilya Dodin, who is affiliated with both Princeton University and PPPL, adapted this technique from their research into fusion energy, the process powering the sun and stars that scientists are developing to create electricity on Earth without emitting greenhouse gases or producing long-lived radioactive waste. Fusion scientists calculate how electromagnetic waves move through plasma, the soup of electrons and atomic nuclei that fuels fusion facilities known as tokamaks and stellarators. 

It turns out that this process resembles the movement of gravitational waves through matter. “We basically put plasma wave machinery to work on a gravitational wave problem,” Garg said.

Garg and Dodin created formulas that could theoretically lead gravitational waves to reveal hidden properties about celestial bodies, like stars that are many light years away. As the waves flow through matter, they create light whose characteristics depend on the matter’s density.

A physicist could analyze that light and discover properties about a star millions of light years away. This technique could also lead to discoveries about the smashing together of neutron stars and black holes, ultra-dense remnants of star deaths. They could even potentially reveal information about what was happening during the Big Bang and the early moments of our universe.

This research was supported by the U.S. National Science Foundation through Princeton University.

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Will connectomics and machine intelligence map the brain?

We’ve all seen a microscopic image of neurons in the brain. But this image is misleading: Neurons don’t exist in isolation in the human brain. Some 86 billion neurons form 100 trillion connections to each other.

To make sense of these connections, Wei-Chung Allen Lee, Harvard Medical School associate professor of neurology at Boston Children’s Hospital, is working in a field of neuroscience called connectomics, which aims to comprehensively map connections between neurons in the brain. It’s a convergence of neurobiology, engineering, computing power and artificial intelligence.

“The brain is structured so that each neuron is connected to thousands of other neurons, and so to understand what a single neuron is doing, ideally you study it within the context of the rest of the neural network,” says Lee.

A map of the brain

To do that, Lee and associates are trying to couple connectomics with recordings of neural activity to do what they call “functional connectomics.” “Essentially, we take the map of where every neuron is and how it is connected to every other neuron, and we layer on information about the activity of those neurons in a living animal.

”We are also using genetic engineering approaches to label specific cell types, which is additional information that we can layer on top of connectivity.”

What do you see when you turn out the light (and fall asleep)?

“Some have argued that you are your connectome. When you fall asleep at night, your brain activity dramatically changes, interrupting your thoughts and feelings — but when you wake up, you resume your thoughts and feelings without any break in your sense of self.

”This is likely because your brain connectivity has remained largely intact through the night. In essence, the structure of how our neurons are wired is our ‘self,’ and connectomics is the key to understanding this structure.”

Machine learning meets connectomics

The researchers are developing and applying high-throughput microscopy, computational approaches, and machine learning to generate connectomes and translate these detailed maps of neural connectivity into biological and computational insights.

”We have mainly worked with mice and fruit flies, which are powerful and well-studied model systems,” said Lee. “The field has sophisticated genetic tools that allow us to label different populations of neurons across the central nervous systems of these species. In fruit flies, we can use the technologies we’ve been developing for connectomics to capture the entire brain and nervous system at synapse resolution.”

“In the mouse, we can target relevant neural circuits or subcircuits.” The researchers are using these models to study the basic principles of how neural circuits are built and operate — basically how the brain’s neural networks are connected to each other to perform different computations that underlie behavior.”

One key component of their approach is serial transmission electron microscopy, or EM, which has unsurpassed spatial resolution, signal-to-noise ratio, and speed relative to other serial EM methods. This technique allows them to identify excitatory and inhibitory neurons, as well as the synapses, or small gaps where neurons connect to each other. They can also examine connectivity patterns of neurons, and study the organization of synaptic connections.

Reference: Nguyen, T.M., Thomas, L.A., Rhoades, J.L. et al. Structured cerebellar connectivity supports resilient pattern separation. Nature 613, 543–549 (2023). https://doi.org/10.1038/s41586-022-05471-w

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