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Archive for the ‘computing’ category: Page 128

Dec 12, 2023

Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs

Posted by in categories: computing, entertainment, neuroscience

Introduction and objective: Video games are crucial to the entertainment industry, nonetheless they can be challenging to access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have the potential to help these individuals by allowing them to control video games using their brain signals. Furthermore, multiplayer BCI-based video games may provide valuable insights into how competitiveness or motivation affects the control of these interfaces. Despite the recent advancement in the development of code-modulated visual evoked potentials (c-VEPs) as control signals for high-performance BCIs, to the best of our knowledge, no studies have been conducted to develop a BCI-driven video game utilizing c-VEPs. However, c-VEPs could enhance user experience as an alternative method. Thus, the main goal of this work was to design, develop, and evaluate a version of the well-known ‘Connect 4’ video game using a c-VEP-based BCI, allowing 2 users to compete by aligning 4 same-colored coins vertically, horizontally or diagonally.

Methods: The proposed application consists of a multiplayer video game controlled by a real-time BCI system processing 2 electroencephalograms (EEGs) sequentially. To detect user intention, columns in which the coin can be placed was encoded with shifted versions of a pseudorandom binary code, following a traditional circular shifting c-VEP paradigm. To analyze the usability of our application, the experimental protocol comprised an evaluation session by 22 healthy users. Firstly, each user had to perform individual tasks. Afterward, users were matched and the application was used in competitive mode. This was done to assess the accuracy and speed of selection. On the other hand, qualitative data on satisfaction and usability were collected through questionnaires.

Results: The average accuracy achieved was 93.74% ± 1.71%, using 5.25 seconds per selection. The questionnaires showed that users felt a minimal workload. Likewise, high satisfaction values were obtained, highlighting that the application was intuitive and responds quickly and smoothly.

Dec 12, 2023

Toby Cubitt: why algorithms will speed up applications of quantum computers

Posted by in categories: computing, information science, quantum physics

Toby Cubitt explains why algorithms are vital for the development of quantum computers.

Dec 12, 2023

New space-saving RAM sticks that jam up to 128GB of memory in a laptop get industry’s stamp of approval — CAMM2 standard ratified by JEDEC

Posted by in categories: computing, space

SO-DIMM’s successor is official.

Dec 12, 2023

SCALE YOUR QUBITS

Posted by in categories: computing, media & arts, quantum physics

How do we go from 100 to 200 to 1000? PASQAL, a quantum computing startup, is using LASERS. They’ve demonstrated 100 and 200 qubit systems, now they’re talking about making 1000. Here’s the mockup of their system.

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Dec 12, 2023

Moving entangled atoms in quantum processor

Posted by in categories: computing, finance, particle physics, quantum physics

Building a plane while flying it isn’t typically a goal for most, but for a team of Harvard-led physicists that general idea might be a key to finally building large-scale quantum computers.

Described in a new paper in Nature, the research team, which includes collaborators from QuEra Computing, MIT, and the University of Innsbruck, developed a new approach for processing quantum information that allows them to dynamically change the layout of atoms in their system by moving and connecting them with each other in the midst of computation.

This ability to shuffle the qubits (the fundamental building blocks of quantum computers and the source of their massive processing power) during the computation process while preserving their quantum state dramatically expands processing capabilities and allows for self-correction of errors. Clearing this hurdle marks a major step toward building large-scale machines that leverage the bizarre characteristics of quantum mechanics and promise to bring about real-world breakthroughs in material science, communication technologies, finance, and many other fields.

Dec 12, 2023

Harvard researchers create first logical quantum processor

Posted by in categories: computing, quantum physics

Key step toward reliable, game-changing quantum computing.

Dec 12, 2023

Intel shows off backside power, stacked transistor research

Posted by in category: computing

Chip giant claims demo tech could ‘significantly’ improve device density.

Dec 12, 2023

Quantum-computing Approach Uses Single Molecules as Qubits for First Time

Posted by in categories: computing, quantum physics

The Quantum Insider (TQI) is the leading online resource dedicated exclusively to Quantum Computing.

Dec 12, 2023

The computational capacity of the universe

Posted by in categories: computing, space

Shared with Dropbox.

Dec 11, 2023

Mixtures of strategies underlie rodent behavior during reversal learning

Posted by in categories: computing, information science

Humans and animals can use diverse decision-making strategies to maximize rewards in uncertain environments, but previous studies have not investigated the use of multiple strategies that involve distinct latent switching dynamics in reward-guided behavior. Here, using a reversal learning task, we showed that mice displayed a much more variable behavior than would be expected from a uniform strategy, suggesting that they mix between multiple behavioral modes in the task. We develop a computational method to dissociate these learning modes from behavioral data, addressing the challenges faced by current analytical methods when agents mix between different strategies. We found that the use of multiple strategies is a key feature of rodent behavior even in the expert stages of learning, and applied our tools to quantify the highly diverse strategies used by individual mice in the task. We further mapped these behavioral modes to two types of underlying algorithms, model-free Q-learning and inference-based behavior. These rich descriptions of underlying latent states form the basis of detecting abnormal patterns of behavior in reward-guided decision-making.

Citation: Le NM, Yildirim M, Wang Y, Sugihara H, Jazayeri M, Sur M (2023) Mixtures of strategies underlie rodent behavior during reversal learning. PLoS Comput Biol 19: e1011430. https://doi.org/10.1371/journal.pcbi.

Editor: Alireza Soltani, Dartmouth College, UNITED STATES