Tag Archives: neural

Can Quantum Neural Networks Bypass the Uncertainty Principle?

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Quantum Chips in Quantum Systems showcasing IBM's first quantum data center

Quantum Computers and Heisenberg’s Uncertainty Principle

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The Heisenberg Uncertainty Principle imposes limits on the precision of measuring specific properties of quantum entities. However, recent research suggests that utilizing quantum neural networks may allow scientists to circumvent this barrier.

For instance, when analyzing a chemically relevant molecule, predicting its properties over time can prove challenging. Researchers must first assess its current characteristics, but measuring quantum properties often leads to interference between measurements, complicating the process. The uncertainty principle asserts that certain quantum attributes cannot be accurately measured at the same time; for example, gaining precise momentum data can distort positional information.

According to Zhou Duanlu from the Chinese Academy of Sciences, recent mathematical insights indicate that quantum neural networks may address these measurement challenges more effectively.

Zhou’s team approached this issue from a practical standpoint. For optimal performance of quantum computers, understanding the properties of qubits—quantum computing’s fundamental components—is crucial. Typical operations, akin to dividing by 2, are employed to yield information about qubits. Yet, the uncertainty principle presents challenges akin to the incompatibility encountered when attempting to execute several conflicting arithmetic operations simultaneously.

Their findings propose that leveraging quantum machine learning algorithms, or Quantum Neural Networks (QNNs), could effectively resolve the compatibility issues inherent to quantum measurements.

Notably, these algorithms rely on randomly selected steps from a predefined set, as shown in previous studies. Zhou et al. demonstrated that introducing randomness into QNNs can enhance the accuracy of measuring a quantum object’s properties. They further extended this approach to simultaneously measure various properties typically constrained by the uncertainty principle, using advanced statistical techniques to aggregate results from multiple random operations for improved precision.

As noted by Robert Fan, this capability to measure multiple incompatible properties swiftly could accelerate scientific understanding of specific quantum systems, significantly impacting quantum computing fields in chemistry and material sciences, as well as large-scale quantum computer research.

The practicality of this innovative approach appears promising, though its effectiveness will hinge on how it compares against other methodologies employing randomness to facilitate reliable quantum measurements, Huang asserts.

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Source: www.newscientist.com

Is This New Book the Key to Unlocking Neuroscience’s Toughest Challenges?

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Exploring Complex Neurological Effects of Drinking Water

Book Review: Neural Mind: How the Brain Thinks

This is a comprehensive two-part review of an intriguing book. The first part delves into the concepts presented in Neural Mind: How the Brain Thinks, while the second part shares my impressions post-reading.

Understanding Neuroscience’s Fundamental Questions

This book confronts a major inquiry in neuroscience: how do neurons facilitate the vast range of human thoughts—from executing motor actions to articulating sentences and contemplating philosophy?

Distinct Perspectives from the Authors

The authors, George Lakoff and Srini Narayanan, offer unique viewpoints. Lakoff, a retired linguist and cognitive scientist from the University of California, Berkeley, has extensively studied metaphor’s role in human cognition. Narayanan, a senior research director at Google DeepMind in Zurich, focuses on how AI learns languages.

Central Thesis: Interconnectedness of Brain Functions

The central premise of Neural Mind is that the brain employs similar processes for motor functions, language acquisition, and abstract thinking. Lakoff and Narayanan suggest that evolution repurposed existing neural circuits for various types of thinking, revealing profound similarities beneath apparent differences.

Learning Concepts Without Language

This notion becomes clearer when examining how infants and non-verbal animals comprehend basic concepts. Despite individual experiences varying, they universally grasp ideas like up and down, motion and rest, force and resistance—essential for brain representation.

Metaphors Shape Our Understanding

In works like Metaphors We Live By, co-authored with Mark Johnson, Lakoff posits that these foundational concepts underpin the metaphors we use. For instance, emotions are often expressed in physical terms, equating happiness with “up” and sadness with “down.” This metaphorical framework explains why we describe communication as a “conveying” process.

Physical Metaphors and Abstract Thinking

A simplistic interpretation suggests that physical metaphors assist in comprehending complex ideas. However, Lakoff and Narayanan argue that these metaphors are the essence of our thought processes. Given the brain’s evolutionary timeline, early neural circuits initially designed for motor control have adapted for advanced language and thought processing.

The Complexity of Simple Actions

Consider the action of drinking a glass of water—a simple task involving multiple stages. From reaching for the glass to sipping water, each phase requires intricate neural coordination. This complexity is mirrored in our language structure, where simple actions and tenses are systematically categorized.

Metaphors and Creative Thinking

Physical metaphors also influence abstract thoughts. For instance, relationships can be described as “separated,” while state failure might be “collapsing.” While clinging to a single metaphor can constrict thinking, embracing new metaphors fosters creativity and innovation.

Future Research and Challenges

Testing these theories is challenging. Lakoff and Narayanan propose models of the brain’s circuit architecture, yet we lack a complete neuron-level map of the human brain. It may take years before their hypotheses can be rigorously validated.

Final Thoughts on Neural Mind

Despite its complexities, Lakoff and Narayanan present compelling arguments worth considering. However, the readability of Neural Mind is debatable, with its repetitive structure and disjointed thoughts making it a demanding read. Important ideas sometimes feel rushed, and the prolonged sentences can be overwhelming. Ultimately, while the book is insightful, it may be better to explore summaries rather than tackle the original text directly.

— Michael Marshall, Writer based in Devon, England

Source: www.newscientist.com

Researchers create detailed map of neural connections in mouse brain

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The human brain is so complex that the scientific brain has a hard time understanding it. Nerve tissue, the size of a grain of sand, could be packed with hundreds of thousands of cells connected by miles of wiring. In 1979, Nobel Prize-winning scientist Francis Crick concluded that the anatomy and activity of only a cubic millimeter of brain material would forever surpass our understanding.

“It’s useless to seek the impossible,” says Dr. Crick. I wrote it.

46 years later, a team of over 100 scientists achieved that impossible by recording cell activity and mapping the structure of cubic millimeters of the mouse brain. In achieving this feat, they accumulated 1.6 petabytes of data. This is equivalent to 22 years of non-stop high-resolution video.

“This is a milestone,” said Davi Bock, a neuroscientist at the University of Vermont. the studywas published in the journal Nature on Wednesday. Dr. Bock said that it enabled advances that allowed it to cover the cubic bones of the cubic brain to map the entire brain wiring of a mouse.

“It’s completely doable and I think it’s worth doing,” he said.

Over 130 years It has passed since Spanish neuroscientist Santiago Ramon y Kajal first spies on individual neurons under a microscope, creating a unique branching shape. Scientists from subsequent generations have resolved many of the details about how neurons send voltage spikes into long arms called axons. Each axon makes contact with small branches or dendrites of adjacent neurons. Some neurons excite their neighbors and fire their own voltage spikes. Some quiet other neurons.

Human thinking emerges in some way from this combination of excitation and inhibition. But how this happens remains a ridiculous mystery as scientists could only study a small number of neurons at a time.

Over the past few decades, technological advances have allowed scientists to begin mapping the whole brain. 1986, British researcher Published A small worm circuit made up of 302 neurons. The researchers then charted larger brains, including 140,000 neurons in the fly’s brain.

After all, is Dr. Crick’s impossible dream possible? The US government began in 2016 100 million dollar effort Scan cubic millimeters of mouse brain. The project was called Cortical Network (or Mechanical Intelligence from Microns) and was led by scientists from the Allen Institute of Brain Science, Princeton University, and Baylor School of Medicine.

Researchers have zeroed into part of the mouse’s brain, which receives signals from the eyes and reconstructs what the animal is seeing. In the first phase of the study, the team recorded the neuronal activity in that area as they showed mouse videos of different landscapes.

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Source: www.nytimes.com

Astronomers discover far-off galaxies using neural networks

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It’s similar to how paleontologists use certain known fossils Indexed Fossil Until assessing rock formations and ancient environments so far, astronomers look for specific patterns of light emissions from space to mark the age of space history. For example, early galaxies give the UV rays that originate from electrons in hydrogen atoms to the type of ultraviolet rays that exert from the second lowest to the lowest energy state. Lyman Alpha or ly⍺ Emission.

For decades, astronomers have associated ly⍺ emissions with periods within billions of years of a big bang called the Big Bang. The era of reionizationwhen the average speed of star formation in galaxies was much higher than today. When they find a galaxy that emits light strongly, they classify it into ly⍺Emitter or Lae And we can be sure that it goes back to the era of reionization. Observing Laes, astronomers talk more about the history of the Milky Way and other galaxies like us.

However, researchers face confounding factors when looking for Laes. The expansion of the universe distorts light in a process called Cosmological redshift. However, more prominently Dustboth Intergalacticcovers the light. While astronomers can analyze the full light of light from the galaxy to find evidence of ly⍺ emissions, it would be much faster to develop tools to predict whether a galaxy is likely to be a LAE based on more readily available measurements.

One team of astronomers developed a model for this problem only Machine Learning A technique known as a Neural Networks. This technique replicates how neurons in the brain function, with several interconnected layers receiving and transmitting signals based on initial inputs and generating final outputs.. The trick is that the programmer knows what inputs to input and what output they expect in the end. The algorithm itself needs to know how best to set up a central connection, what to look for, and how to rank the importance of each input.

The team began with data from two surveys of light sources in space: 926 galaxies VanderOf these, only 520 are laes, starting from 507 Musethey were all laes. They trained the algorithm using 80% of this data to explicitly communicate which sources are actual LAES and which sources are not. They saved the remaining 20% ​​of the data for testing.

Through this initial test, the team identified six parameters of neural networks to focus on evaluating galaxies for LAE potential. These parameters were the rate of star formation, total star mass, UV brightness, UV emission patterns, age, and dust. They programmed the network to output an estimate of the probability that a particular galaxy is a LAE, and thought that what was above 70% meant that the algorithm classified it as an LAE.

When we created a neural network using training data, the team tested several additional rounds. Using early test data, their networks found that they correctly identified the network in 77% of the time, as there was only a 14% chance of false positives. When they looked at what their network prioritized to make these predictions, they found that the most important factors were the galaxy’s UV emission pattern, its UV brightness, and the mass of its star.

Following this initial success, the team applied the network to another investigation. cosmos2020and a subset of that raise, SC4Kwith fewer details than the training data survey. From these datasets, the team’s neural network identified true Laes for 72% of the time.

The team’s final results came when they applied neural networks to data from NASA’s new telescope. jwst. The ultimate goal in their model is to study the distant past of the universe, and JWST aims to see better-looking sources than ever before, so the success of the test is Already checking the results of LAE from JWST It will be a good sign of future success. They found a true positive rate of 91% in JWST data, showing the validity of their approach and illuminated the path to know more about the history of the universe.


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Source: sciworthy.com

Scientists have discovered a distinct neural signature in chickadees for episodic memory

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Black-capped Chickadee (Poecil atricapillus) This small passerine bird from North America, which lives in deciduous and mixed forests, has an extraordinary memory that allows it to remember thousands of food locations to help it survive the winter. Now, scientists Columbia University Zuckerman Institute for Mind, Brain, and Behavior have discovered how Gala is able to remember so many details. They memorize the location of each food item using brain cell activity similar to a barcode.

Chetty other. We propose that animals recall episodic memories by reactivating barcodes in the hippocampus.Image credit: Chetty other., doi: 10.1016/j.cell.2024.02.032.

“We found that each memory is tagged with a unique pattern of activity in the hippocampus, the part of the brain that stores memories,” said Dr. Dmitry Aronov, senior author of the study.

“We called these patterns 'barcodes' because they are very specific labels for individual memories. For example, the barcodes of two different caches are Even if two caches are next to each other, there is no correlation.

“There are a number of human discoveries that perfectly match the barcode mechanism,” added Dr. Selman Chetty, lead author of the study.

Scientists have known for decades that the brain's hippocampus is necessary for episodic memories, but understanding exactly how those memories are encoded has been much more difficult. was.

Part of the reason is that it's often difficult to know what animals remember at any given time.

To get around this problem in the new study, Dr. Aronoff and colleagues turned to the black-capped chickadee.

Researchers found that chickadees provide a unique opportunity to study episodic memory because they hide food and then have to remember to come back to retrieve it later.

“Each cache is a clear, obvious, easily observable moment in which a new memory is formed,” Dr. Aronoff said.

“By focusing on these special moments, we were able to identify patterns of memory-related activity that we had not noticed before.”

The researchers needed to design an arena that could automatically track the detailed behavior of the gulls as they hide and retrieve food.

They also needed to develop techniques to make large-scale, high-density neural recordings inside the birds' brains as they move freely.

Their brain recordings during caching revealed very sparse and transient barcode-like firing patterns across hippocampal neurons. Each barcode contains only about 7% of the cells in the hippocampus.

“When a bird creates a cache, about 7% of its neurons respond to that cache. When the bird creates another cache, another group of 7% of its neurons responds,” Dr. Aronoff said. Ta.

These neural barcodes occurred simultaneously with the conventional activity of neurons in the brain that are triggered in response to specific locations, aptly called place cells.

Interestingly, however, there were no similarities in the episodic memory barcodes of cache locations close to each other.

“It was widely thought that place cells change when animals form new memories,” Dr. Aronoff says.

“For example, placement cell firings may increase or decrease near the cache location.”

“This was a common hypothesis, but our data did not support it.”

“Place cells do not represent information about caches; rather, they appear to remain relatively stable as the chickadees cache and retrieve food from the environment.”

“Instead, episodic memory is represented by additional activity patterns, or barcodes, that coexist with place cells.”

The authors liken the newly discovered hippocampal barcode to a computer hash code, a pattern that is assigned as a unique identifier to different events.

They suggest that barcode-like patterns may be a mechanism for the rapid formation and storage of many non-interfering memories.

“Perhaps the biggest unanswered question is whether and how the brain uses barcodes to prompt behavior,” Dr. Aronoff said.

“For example, it's not clear whether chickadees activate barcodes and use their memory of food-caching events when deciding where to go next.”

“We plan to address these questions in future studies through more complex settings in the laboratory, recording brain activity while the birds choose which food stores to visit.”

“If you plan on retrieving cached items before you actually retrieve them, that's to be expected,” Dr. Chetty said.

“We wanted to identify the moments when a bird is thinking about a location but haven't gotten there yet, and see if activating the barcode might move the bird to the cache. thinking about.”

“We also want to know whether the barcoding tactics they discovered in chickadees are widely used among other animals, including humans. It might help clarify the core.”

“When you think about how people define themselves, who they think they are, their sense of self, episodic memories of specific events are central to that. That's what we're trying to understand. That is what we are doing.”

a paper The survey results were published in a magazine cell.

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Selman N. Chetty other. Barcoding of episodic memory in the hippocampus of food-storing birds. cell, published online March 29, 2024. doi: 10.1016/j.cell.2024.02.032

Source: www.sci.news

Brain Scan Shows How Neural Network Boosts Creativity

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Practicing mindfulness improves creative thinking

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It's easy to name people who have evolved human thinking, from Jane Austen to Albert Einstein, Zaha Hadid to Ai Weiwei, but why are these people so much more creative than others? It's much more difficult to explain what kind of thinking you do. Are their brains just built that way, or can anyone learn it? The mystery of creativity has long puzzled scientists. Now, researchers are finally making some progress towards closing the curtain. Even better, their insights can help us all exercise a little more original thinking.

Some of them are exciting insights This stems from the “dual process theory” of creativity, which distinguishes between idea generation and idea evaluation. Idea generation involves digging deep into existing knowledge for seeds of inspiration. Perhaps it is done by drawing analogies from completely different areas. Free association is key at this stage, as one thought leads to another, more original insight. The second phase, idea evaluation, requires you to apply a more critical eye to select the ideas that best fit your goals. Novelists must decide whether strange, supernatural plot twists will excite readers or turn them off. Engineers must consider whether a fish-inspired airplane would be practical and efficient. Large projects require these two stages to be repeated many times during the long and winding journey from concept to completion.

Brain scans of people engaged in creative problem solving suggest that idea generation and evaluation relies on…

Source: www.newscientist.com