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Category: mathematics – Page 3

The collective motion of bacteria—from stable swirling patterns to chaotic turbulent flows—has intrigued scientists for decades. When a bacterial swarm is confined in small circular space, stable rotating vortices are formed. However, as the radius of this confined space increases, the organized swirling pattern breaks down into a turbulent state.

This transition from ordered to chaotic flow has remained a long-standing mystery. It represents a fundamental question not only in the study of bacterial behavior but also in classical fluid dynamics, where understanding the emergence of turbulence is crucial for both controlling and utilizing complex flows.

In a recent study published in Proceedings of the National Academy of Sciences on March 14, 2025, a research team led by Associate Professor Daiki Nishiguchi from the Institute of Science Tokyo, Japan, has revealed in detail how bacterial swarms transition from organized movement to chaotic flow. Combining large-scale experiments, computer modeling, and , the team observed and explained previously unknown intermediate states that emerge between order and turbulence.

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James Fodor discusses what he is researching, mind uploading etc.

As of 2020, James Fodor, is a student at the Australian National University, in Canberra, Australia. James’ studies at university have been rather diverse, and have at different times included history, politics, economics, philosophy, mathematics, computer science, physics, chemistry, and biology. Eventually he hopes to complete a PhD in the field of computational neuroscience.

James also have a deep interest in philosophy, history, and religion, which he periodically writes about on his blog, which is called The Godless Theist. In addition, James also has interests in and varying levels of involved in skeptical/atheist activism, effective altruism, and transhumanism/emerging technologies. James is a fan of most things sci-fi, including Star Trek, Dr Who, and authors such as Arthur C. Clarke and Isaac Asimov.

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Continue reading “James Fodor — Exploring the Frontiers of Computational Neuroscience” | >

In this video, Dr. Ardavan (Ahmad) Borzou will discuss a rising technology in constructing bio-computers for AI tasks, namely Brainoware, which is made of brain organoids interfaced by electronic arrays.

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Comprehensive Python Checklist (machine learning and more advanced libraries will be covered on a different page):
https://compu-flair.com/blogs/program… — Introduction 02:16 — Von Neumann Bottleneck 03:54 — What is brain organoid 05:09 — Brainoware: reservoir computing for AI 06:29 — Computing properties of Brainoware: Nonlinearity & Short-Memory 09:27 — Speech recognition by Brainoware 12:25 — Predicting chaotic motion by Brainoware 13:39 — Summary of Brainoware research 14:35 — Can brain organoids surpass the human brain? 15:51 — Will humans evolve to a body-less stage in their evolution? 16:30 — What is the mathematical model of Brainoware?

00:00 — Introduction.
02:16 — Von Neumann Bottleneck.
03:54 — What is brain organoid.
05:09 — Brainoware: reservoir computing for AI
06:29 — Computing properties of Brainoware: Nonlinearity & Short-Memory.
09:27 — Speech recognition by Brainoware.
12:25 — Predicting chaotic motion by Brainoware.
13:39 — Summary of Brainoware research.
14:35 — Can brain organoids surpass the human brain?
15:51 — Will humans evolve to a body-less stage in their evolution?
16:30 — What is the mathematical model of Brainoware?

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A team of quantum computer researchers at quantum computer maker D-Wave, working with an international team of physicists and engineers, is claiming that its latest quantum processor has been used to run a quantum simulation faster than could be done with a classical computer.

In their paper published in the journal Science, the group describes how they ran a quantum version of a mathematical approximation regarding how matter behaves when it changes states, such as from a gas to a liquid—in a way that they claim would be nearly impossible to conduct on a traditional computer.

Over the past several years, D-Wave has been working on developing quantum annealers, which are a subtype of quantum computer created to solve very specific types of problems. Notably, landmark claims made by researchers at the company have at times been met with skepticism by others in the field.

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In research inspired by the principles of quantum mechanics, researchers from Pompeu Fabra University (UPF) and the University of Oxford reveal new findings to understand why the human brain is able to make decisions quicker than the world’s most powerful computer in the face of a critical risk situation. The human brain has this capacity despite the fact that neurons are much slower at transmitting information than microchips, which raises numerous unknown factors in the field of neuroscience.

The research is published in the journal Physical Review E.

It should be borne in mind that in many other circumstances, the human brain is not quicker than technological devices. For example, a computer or calculator can resolve mathematical operations far faster than a person. So, why is it that in critical situations—for example, when having to make an urgent decision at the wheel of a car—the human brain can surpass machines?

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Quantum systems hold the promise of tackling some complex problems faster and more efficiently than classical computers. Despite their potential, so far only a limited number of studies have conclusively demonstrated that quantum computers can outperform classical computers on specific tasks. Most of these studies focused on tasks that involve advanced computations, simulations or optimization, which can be difficult for non-experts to grasp.

Researchers at the University of Oxford and the University of Sevilla recently demonstrated a over a classical scenario on a cooperation task called the odd-cycle game. Their paper, published in Physical Review Letters, shows that a team with can win this game more often than a team without.

“There is a lot of talk about quantum advantage and how will revolutionize entire industries, but if you look closely, in many cases, there is no mathematical proof that classical methods definitely cannot find solutions as efficiently as quantum algorithms,” Peter Drmota, first author of the paper, told Phys.org.

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Mathematicians from New York University and the University of British Columbia have resolved a decades-old geometric problem, the Kakeya conjecture in 3D, which studies the shape left behind by a needle moving in multiple directions.

The research is published on the arXiv preprint server.

The Kakeya conjecture was inspired by a problem asked in 1917 by Japanese mathematician Sōichi Kakeya: What is the region of smallest possible area in which it is possible to rotate a needle 180 degrees in the plane? Such regions are called Kakeya needle sets.

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Scientists at the Okinawa Institute of Science and Technology (OIST), the National Institute of Information and Communications Technology, and the University of Tokyo have found a mathematical connection between spatial navigation and language processing, creating a model called “Disentangled Successor Information” (DSI).

This model generates patterns that closely resemble the activity of actual brain cells involved in both spatial awareness (place cells and grid cells) and concept recognition (concept cells).

The DSI model shows that the hippocampus and entorhinal cortex— previously known primarily for —likely use comparable computational processes to handle both physical spaces and meaningful ideas or words. Using this shared framework, both types of information can be processed through similar mathematical computations, which could be achieved in the brain by partial activation of specific groups of neurons.

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