Lifeboat Foundation

BMIs using intracortical electrodes, such as Utah arrays, are particularly adept at sensing fast changing (millisecond-scale) neural activity from spatially localized regions (1 cm) during behavior or stimulation that is correlated to activity in such spatially specific regions, for example, M1 for motor and V1 for vision. Intracortical electrodes, however, struggle to track individual neurons over longer periods of time, for example, between subsequent recording sessions^15,16. Consequently, decoders are typically retrained every day¹⁵. A similar neural population identification problem is also present with an ultrasound device, including from shifts in the field of view between experiment sessions. In the current study, we demonstrated an alignment method that stabilizes image-based BMIs across more than a month and decodes from the same neurovascular populations with minimal, if any, retraining. This is a critical development that enables easy alignment of a previous days’ models to a new day’s data and allows decoding to begin with minimal to no new training data. Much effort has focused on ways to recalibrate intracortical BMIs across days that do not require extensive new data^{18,19,20,21,22,23}. Most of these methods require identification of manifolds and/or latent dynamical parameters and collecting new neural and behavioral data to align to these manifolds/parameters. These techniques are, to date, tailored to each research group’s specific applications with varying requirements, such as hyperparameter tuning of the model²³ or a consistent temporal structure of data²². They are also susceptible to changes in function in addition to anatomy. For example, ‘out-of-manifold’ learning/plasticity alters the manifold²⁴ in ways that many alignment techniques struggle to address. Finally, some of the algorithms are computationally expensive and/or difficult to implement in online use²².

Contrasting these manifold-based methods, our decoder alignment algorithm leverages the intrinsic spatial resolution and field of view provided by fUS neuroimaging to perform decoder stabilization in a way that is intuitive, repeatable and performant. We used a single fUS frame (∼ 500 ms) to generate an image of the current session’s anatomy and aligned a previous session’s field of view to this single image. Notably, this did not require any additional behavior for the alignment. Because we only relied upon the anatomy, our decoder alignment is robust, can use any off-the-shelf alignment tool and is a valid technique so long as the anatomy and mesoscopic encoding of relevant variables do not change drastically between sessions.

It remains an open question as to how much the precise positioning of the ultrasound transducer during each session matters for decoder performance, especially out-of-plane shifts or rotations. In these current experiments, we used linear decoders that assumed a given image pixel is the same brain voxel across all aligned data sessions. To minimize disruptions to this pixel–voxel relationship, we performed image alignment within the 2D plane. As we could only image a 2D recording plane, we did not correct for any out-of-plane brain shifts between sessions that would have disrupted the pixel–voxel mapping assumption. Future fUS-BMI decoders may benefit from three-dimensional (3D) models of the neurovasculature, such as registering the 2D field of view to a 3D volume^25,26,27 to better maintain a consistent pixel–voxel mapping.

Physicists from the Eötvös Loránd University (ELTE) have been conducting research on the matter constituting the atomic nucleus utilizing the world’s three most powerful particle accelerators. Their focus has been on mapping the “primordial soup” that filled the universe in the first millionth of a second following its inception.

Intriguingly, their measurements showed that the movement of observed particles bears resemblance to the search for prey of marine predators, the patterns of climate change, and the fluctuations of stock market.

In the immediate aftermath of the Big Bang, temperatures were so extreme that atomic nuclei could not exists, nor could nucleons, their building blocks. Hence, in this first instance the universe was filled with a “primordial soup” of quarks and gluons.

“While there is so much we’ve come to understand, so much remains unknown in our Ocean–and we are thrilled to continue exploring.”

Ocean explorers from the Schmidt Ocean Institute have unveiled a colossal underwater mountain, challenging our perceptions of the ocean’s mysterious depths.

Schmidt Ocean Institute.

Continue reading “Expedition reveals enormous ocean mountain twice as high as Burj Khalifa” »

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My name is Artem, I’m a computational neuroscience student and researcher. In this video we discuss engrams – fundamental units of memory in the brain. We explore what engrams are, how memory is allocated, where it is stored, and how different memories become linked with each other.

Continue reading “Building Blocks of Memory in the Brain” »

In laser-based manufacturing, accommodating non-flat, or changing surfaces has traditionally been labor-intensive, involving complex focus mapping procedures and or ex-situ characterization. This often results in repositioning errors and extended processing times.

To address these issues, ultra-high-speed auto-focusing in laser processing has been developed. Whereas most auto-focusing techniques still require the mechanical motion of a motorized stage. This mechanical movement in the beam propagation axis can be significantly slower than the lateral speed, slowing down the process of surface detection and re-alignment. Furthermore, it requires feedback, control, and sensing methods in order to determine the optical focal position.

In a new paper published in Light: Science & Applications, a team of researchers, led by Professor Craig B. Arnold from the Department of Mechanical and Aerospace Engineering at Princeton University, U.S., developed a fast method to simultaneously track the specific location of a surface and adjust the focus of an optical system. They employed axial varifocal optics, specifically a TAG lens, which operates at 0.1−1 MHz, bypassing delays from the mechanical motion in the beam propagation direction.

Humankind on the verge of evolutionary traps, a new study: …For the first time, scientists have used the concept of evolutionary traps on human societies at large.

For the first time, scientists have used the concept of evolutionary traps on human societies at large. They find that humankind risks getting stuck in 14 evolutionary dead ends, ranging from global climate tipping points to misaligned artificial intelligence, chemical pollution, and accelerating infectious diseases.

The evolution of humankind has been an extraordinary success story. But the Anthropocene—the proposed geological epoch shaped by us humans—is showing more and more cracks. Multiple global crises, such as the COVID-19 pandemic, climate change, food insecurity, financial crises, and conflicts have started to occur simultaneously in something which scientists refer to as a polycrisis.

Continue reading “New research maps 14 potential evolutionary dead ends for humanity and ways to avoid them” »

Russia and China have both recently developed intercontinental ballistic missiles capable of dropping several nuclear bombs at once.

Google Maps is getting a few new updates, including a social feature that is designed to help people plan and collaborate with their friends. The navigation platform is also adding improved transit directions and emoji reactions.

The new social feature is designed to take planning out of your group chat and into Google Maps. The platform is updating its list feature to make it easier for people to share places, plan with friends and vote on group activities. With this new feature, you’ll be able to create a collaborative list to to start planning a hangout after you share a place in Maps with friends. Everyone in the group can add places they’re interested in visiting, and vote with an emoji, like a heart or a thumbs down.

The new research, part of the NIH BRAIN Initiative, paves the way toward treating, preventing, and curing brain disorders.

Salk Institute researchers, as part of a larger collaboration with research teams around the world, analyzed more than half a million brain cells from three human brains to assemble an atlas of hundreds of cell types that make up a human brain in unprecedented detail.

The research, published in a special issue of the journal Science on October 13, 2023, is the first time that techniques to identify brain cell subtypes originally developed and applied in mice have been applied to human brains.