Friday, February 28, 2025
From corridors to cognition: How the brain builds mental maps of the world
As the animal started to learn, its neural activity
started to reflect its changes in behavior. At the beginning of learning, the
activity of individual neurons was mostly similar for the two corridors.
However, as the animal's learning progressed, the neural activity representing
the different corridors started to differentiate. At the end of learning, the
activity of these neurons was completely different, with distinct maps encoding
hidden information that enabled the animal to distinguish between the two
choices. Credit: Sun and Winnubst et al.
Our brains
build maps of the environment that help us understand the world around us,
allowing us to think, recall, and plan. These maps not only help us to, say,
find our room on the correct floor of a hotel, but they also help us figure out
if we've gotten off the elevator on the wrong floor.
Neuroscientists know a lot about the activity of
neurons that make up these maps—like which cells fire when we're in a
particular location. But how the brain creates these maps as we learn remains a mystery.
Now, by tracking the activity of thousands of neurons
over days and weeks as an animal learns, researchers at HHMI's Janelia Research
Campus have systematically detailed, step by step, how these cognitive maps
form in the brain's hippocampus—a region responsible for learning and memory.
The study is published in the journal Nature.
The team, led by the Spruston Lab, found that as an
animal learns to collect rewards from two subtly different linear tracks—like
the different floors of a hotel—neurons in the hippocampus start to respond in
disparate ways. Eventually, the brain produces entirely distinct
representations of these visually similar tracks that include information
enabling the animal to differentiate between the two options.
The researchers also identified the type of mathematical model that best reproduces this learning process, shedding light on computations the brain might be using to create these mental maps and providing insight into memory and intelligence.
The researchers studied a mouse as it learned how
to navigate two different virtual corridors: one with a reward at a near
location, and one with a reward at a far location. Mice learned to collect
water rewards at either near or far reward zones in two VR corridors based on
distinct indicator cues (stripes or dots) at the beginning of the corridor.
Expert mice, shown here, only licked at the correct reward zones to trigger a
water reward. Credit: Sun and Winnubst et al.
"We are mapping out the step-by-step process of cognitive map
formation, which is such an important concept," says Weinan Sun, an
assistant professor at Cornell University who co-led the research as a research
scientist in the Spruston Lab.
"But there is also a second contribution: The result of watching that
process gives us a hint about the underlying computations and we get a little
bit closer to understanding what the brain is doing in making these maps."
Understanding how the brain implements these computations could help
researchers develop better treatments for memory disorders like Alzheimer's and
create artificial intelligence systems that reason more like biological brains.
"Neuroscience and AI can learn a lot from each other," says Johan
Winnubst, lead scientist of neuroanatomy at E11 Bio, who co-led the research as
a research scientist in the Spruston Lab.
"What large language models are able to do is very impressive, but they also fail in a lot of very obvious ways and some of that has to do with reasoning and long-term planning. So maybe you introduce some of the lessons that we have learned from the hippocampus to these models."
The researchers tracked the activity of thousands
of neurons over days and weeks as the animal learned, allowing the team to
systematically detail how cognitive maps form in the brain's hippocampus. This
movie shows neural activity with an increasing number of cells plotted. Credit:
Sun and Winnubst et al.
Observing map formation
To see how these cognitive maps form, the researchers used a
Janelia-designed, high-resolution microscope with a large field of view to
image neural activity in
thousands of neurons in the hippocampus of a mouse learning how to navigate two
different virtual corridors: one with a reward at a near location, and one with
a reward at a far location.
Near the beginning of each corridor, the mouse is given a visual cue to
indicate where in the corridor it can expect to find a water reward, at either
the near or far location. The mouse must figure out the relationship between
the indicator cue and where the reward is going to be delivered.
The researchers saw that all the animals learned how to navigate the
corridors in the same specific sequence. First, they learned to suppress their
licking where they knew they wouldn't be rewarded. Then, they learned they were
only getting one reward per corridor. Lastly, they learned to suppress their
licking at the near reward location in the corridor where the reward was at the
far location.
As the animal started to learn, its neural activity started to reflect its
changes in behavior. At the beginning of learning, the activity of individual
neurons was mostly similar for the two corridors, forming a linear track with
only slight differences representing the different cues and reward locations.
However, as the animal's learning progressed, the neural activity representing the different corridors started to differentiate further.
To see how cognitive maps form in the brain,
researchers used a Janelia-designed, high-resolution microscope with a large
field of view to image neural activity in thousands of neurons in the
hippocampus of a mouse as it learned. Credit: Sun and Winnubst et al.
While the near and far reward locations were always represented differently
from each other, now these reward locations were treated differently depending
on which corridor the mouse was in. The near location in the near corridor was
represented differently than the near location in the far corridor, even though
they were visually identical.
At the end of learning, the activity of these neurons was completely
different, with distinct maps encoding hidden information that enabled the
animal to distinguish between the two corridors. The researchers found that
there are specific cells—they call them "state cells"—that extract
hidden information from the environment to enable this differentiation.
In the hotel analogy, initially the brain might represent all the floors
similarly. But after a few days, we learn the differences between the floors.
Our brains generate different maps for the different floors, each containing
hidden or contextual information—like what number was displayed inside the
elevator but is no longer visible when we get out—that allows us to distinguish
between them.
For animals in the real world, this process helps to distinguish similar
but different areas in a forest or field.
"Initially, the brain activity is very
similar, and with learning, the activity becomes more and more different until
they are orthogonal. And then, in the end, each neural pattern of activity will
encode a hidden state that will reflect the true hidden state of the
task," Sun says. "The brain cares about the immediate sensory input
but interprets it in the context of the hidden state the animal is in."
Finally, the researchers looked at what computations might be happening in
the brain to enable the map formation they observed.
The team discovered that the brain builds these maps like a state machine—a
system that figures out true situations by inferring hidden states beyond what
is immediately visible. Among various computational models tested, only one
type—called a Clone-Structured Causal Graph—could accurately reproduce this
learning process.
The researchers, who also created an online visualization tool so
scientists around the world can explore the data, say that being able to
connect these pieces—from behavior to individual cells to groups of neurons to
algorithms—is a critical step toward truly understanding how the brain and
intelligence works.
"One of the ultimate goals of neuroscience is: if we observe a
behavior or cognitive function, we want to understand that behavior or
cognitive function in terms of not only the cellular and molecular processes
responsible for it, but also the algorithmic representation the brain
uses," says Janelia Executive Director Nelson Spruston, the senior author
on the new research.
"We are getting at the algorithmic level—arguably the hardest to pin down—which helps us connect the dots of how the cellular and molecular processes actually operate to produce an algorithm in the brain that can form this computation that we observe in the form of behavior."
by Howard Hughes Medical Institute
Source: From corridors to cognition: How the brain builds mental maps of the world
Is There Potential for Life on Europa? We Asked a NASA Expert: Episode 52 - UNIVERSE
That’s a great question. And it’s a
question that NASA will seek to answer with the Europa Clipper spacecraft.
Europa is a moon of Jupiter. It’s about
the same size as Earth’s Moon, but its surface looks very different. The
surface of Europa is covered with a layer of ice, and below that ice, we think
there’s a layer of liquid water with more water than all of Earth’s oceans
combined.
So because of this giant ocean, we think
that Europa is actually one of the best places in the solar system to look for
life beyond the Earth.
Life as we know it has three main
requirements: liquid water — all life here on Earth uses liquid water as a
basis.
The second is the right chemical
elements. These are elements like carbon, hydrogen, nitrogen, oxygen,
phosphorus, sulfur. They’re elements that create the building blocks for life
as we know it on Earth. We think that those elements exist on Europa.
The third component is an energy source.
As Europa orbits around Jupiter, Jupiter’s strong gravity tugs and pulls on it.
It actually stretches out the surface. And it produces a heat source called
tidal heating. So it’s possible that hydrothermal systems could exist at the
bottom of Europa’s ocean, and it’s possible that those could be locations for
abundant life.
So could there be life on Europa? It’s possible. And Europa Clipper is going to explore Europa to help try to answer that question.
Source: Is There Potential for Life on Europa? We Asked a NASA Expert: Episode 52 - NASA
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
Thursday, February 27, 2025
Hubble Spies a Spiral That May Be Hiding an Imposter - UNIVERSE
The spiral galaxy UGC 5460 shines in this NASA/ESA
Hubble Space Telescope image. UGC 5460 sits about 60 million light-years away
in the constellation Ursa Major.
ESA/Hubble & NASA, W.
Jacobson-Galán, A. Filippenko, J. Mauerhan
The sparkling spiral galaxy gracing this NASA/ESA Hubble Space Telescope image is UGC 5460, which sits about 60 million light-years away in the constellation Ursa Major. This image combines four different wavelengths of light to reveal UGC 5460’s central bar of stars, winding spiral arms, and bright blue star clusters. Also captured in the upper left-hand corner is a far closer object: a star just 577 light-years away in our own galaxy.
UGC 5460 has hosted two recent
supernovae: SN 2011ht and SN 2015as. It’s because of these two stellar
explosions that Hubble targeted this galaxy, collecting data for three
observing programs that aim to study various kinds of supernovae.
SN 2015as was as a core-collapse
supernova: a cataclysmic explosion that happens when the core of a star far
more massive than the Sun runs out of fuel and collapses under its own gravity,
initiating a rebound of material outside the core. Hubble observations of SN
2015as will help researchers understand what happens when the expanding
shockwave of a supernova collides with the gas that surrounds the exploded
star.
SN 2011ht might have been a core-collapse supernova as well, but it could also be an impostor called a luminous blue variable. Luminous blue variables are rare stars that experience eruptions so large that they can mimic supernovae. Crucially, luminous blue variables emerge from these eruptions unscathed, while stars that go supernova do not. Hubble will search for a stellar survivor at SN 2011ht’s location with the goal of revealing the explosion’s origin.
Source: Hubble Spies a Spiral That May Be Hiding an Imposter - NASA Science
From collisions to stellar cannibalism—the surprising diversity of exploding white dwarfs - UNIVERSE
The Palomar 48 inch telescope at the
Palomar Observatory in California with an image of the Milky Way in the
background. The stars represent the number of supernovae discovered in each
direction and the inset is an image of a galaxy after (left) and before (right)
the supernova exploded. Credit: Mickael Rigault.
Astrophysicists
have unearthed a surprising diversity in the ways in which white dwarf stars
explode in deep space after assessing almost 4,000 such events captured in
detail by a next-gen astronomical sky survey. Their findings may help us more
accurately measure distances in the universe and further our knowledge of
"dark energy."
The dramatic explosions of white dwarf stars at the ends of their lives have for decades
played a pivotal role in the study of dark energy—the mysterious force
responsible for the accelerating expansion of the universe. They also provide
the origin of many elements in our periodic table, such as titanium, iron and
nickel, which are formed in the extremely dense and hot conditions present
during their explosions.
A major milestone has been achieved in
our understanding of these explosive transients with the release of a major
dataset, and associated 21 publications in an Astronomy
& Astrophysics special issue.
This unique dataset of nearly 4,000 nearby supernovae is many times larger than previous similar samples and has allowed crucial breakthroughs in understanding how these white dwarfs explode. The sample was obtained by Zwicky Transient Facility (ZTF), a Caltech-led astronomical sky survey, with key involvement of researchers at Trinity College Dublin, led by Prof. Kate Maguire in the School of Physics.
Each star is a SN exploding with the size
indicating how bright it appears and the color indicating the color of the
supernova, they go from blue (hotter) to yellow (cooler) as they grow older and
cool. Credit: Mickael Rigault
"Thanks to ZTF's unique
ability to scan the sky rapidly and deeply, it has been possible to discover
new explosions of stars up to one million times fainter than the dimmest stars
visible to the naked eye," says Prof. Maguire.
One of the key results, led by the
group at Trinity, is the discovery that there are multiple exotic ways that
white dwarfs can explode, including in collisions of two stars in luminous
stellar spectacles, as well as the cannibalism of stars by their companions in
double star systems.
This is only possible with this
sample due to the ability to discover very faint blips combined with large
sample sizes. And the surprising diversity may have implications for the use of
these supernovae to measure distances in the universe, since the constraints on
the properties of dark energy crucially demand that these explosions can be
standardized.
"The diversity of ways that white dwarf stars can blow up is much greater than previously expected, resulting in explosions that range from being so faint they are barely visible to others that are bright enough to see for many months to years afterwards," says Prof. Maguire.
Source: From collisions to stellar cannibalism—the surprising diversity of exploding white dwarfs
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
.gif)
Subscribe to:
Posts (Atom)