Lifeboat Foundation

Organoids are carefully grown collections of cells in a dish, designed to mimic organ structures and composition better than conventional cell cultures and give researchers a unique view into how organs such as the brain grow and develop. To make them experimentally useful, scientists need to determine how faithfully these models reproduce the behavior of cells in the body.

Now, researchers at the Broad Institute of MIT and Harvard and Harvard University have found that human brain organoids replicate many important cellular and molecular events of the developing human cortex, the part of the brain responsible for movement, perception, and thought. Their findings appear today in Cell.

The team grew brain organoids from stem cells and closely studied their growth over a six-month period, using tools that map cell position, gene expression, and chromatin accessibility — which determines how gene activity is regulated — at a single-cell level and over time. They then constructed an “atlas” characterizing more than 600,000 cells from organoids that were sampled as they developed and matured. The team found that after the first month, in each organoid they made, the same types of cells developed in the same order and expressed the same genes as cells in the developing human embryo.

Ancient Chinese society was dominated by feudalism. The economy was dominated by agriculture, and the development of science and technology was slow or even suppressed. The main achievements of this era were the four major inventions of China: papermaking, gunpowder, the compass, and printing. Why was this so? For an ancient civilization with a history of several thousand years, why was the development of science and technology so backward? The fundamental reason was the idea of imperial power. Ancient China was centered on the emperor, and everything on the Chinese land was owned by the emperor, including the farmers on that land. The emperor was afraid of a peasant revolution and was afraid that others would take the emperor’s place, and as a result successive emperors would use the policy of fools. Instead of allowing farmers to read books, the emperors just wanted the farmers to plant the land every day, like slaves, so that the farmers would have no ability to overthrow the rulers. This idea of imperial power had greatly suppressed the development of science and technology.

In 1949, Mao Zedong established the first democratic, self-improving, unified China in Chinese history: The People’s Republic of China, a stable country; a country without feudal ideas; and a country that serves the people. Only then did China begin to truly develop its own education, technology, and industry. It was aimed for ordinary people to have food to eat, houses to live in, and books to read, and it was also intended for them to be more involved in technology and democracy. However, Chinese politics had hindered the development of science and technology (superhuman science), such as the Great Leap Forward, which severely reduced China’s productivity and starved many people; the Cultural Revolution had destroyed China’s economic development, education, and technology, bringing China back to pre-liberation overnight. These events were relatively unfortunate. Political struggles have severely hindered the development of science and technology (superhuman science) in China.

In 1978, China began reform and opening up. This phase of reform and opening up was China’s greatest era. China has changed from a closed country to an open country. Deng Xiaoping formulated a basic national policy centered on economic construction, which has enabled China’s economy to develop rapidly. At this time, China attaches great importance to the development of education, science and technology, and the economy. At the same time, special attention is also paid to foreign exchanges, and advanced education and technology have been introduced from abroad. In education, a large number of international students are sent to study in developed countries such as the United States, which has cultivated a large number of scientific and technological talents for China; economically, a large number of foreign companies have been introduced to optimize state-owned enterprises and support for private enterprises, so China’s economy has developed rapidly.

Modern computer chips can have features built on a nanometer scale. Until now it has been possible to form such small structures only on top of a silicon wafer, but a new technique can now create nanoscale features in a layer below the surface. The approach has promising applications in both photonics and electronics, say its inventors, and could one day enable the fabrication of 3D structures throughout the bulk of the wafer.

The technique relies on the fact that silicon is transparent to certain wavelengths of light. This means the right kind of laser can travel through the surface of the wafer and interact with the silicon below. But designing a laser that can pass through the surface without causing damage and still carry out precise nanoscale fabrication below is not simple.

Researchers from Bilkent University in Ankara, Türkiye, achieved this by using spatial light modulation to create a needlelike laser beam that gave them greater control over where the beam’s energy was deposited. By exploiting physical interactions between the laser light and the silicon, they were able to fabricate lines and planes with different optical properties that could be combined to create nanophotonic elements below the surface.

Experiments show that effect doesn’t always follow cause in the weird world of subatomic particles, offering fresh clues about the quantum origins of space-time.

By Michael Brooks

Artificial intelligence (AI) isn’t just performing with high accuracy; for the first time, new research suggests that it is “thinking” very much like humans.

Work on AI models has long focused on the scale of tasks or accuracy, but a group of researchers is looking more closely at how AI makes decisions. By developing a process more similar to the human mind, troubling tendencies for AI “hallucinations” may be mitigated.

The dramatic advances in efferent neural interfaces over the past decade are remarkable, with cortical signals used to allow paralyzed patients to control the movement of a prosthetic limb or even their own hand. However, this success has thrown into relief, the relative lack of progress in our ability to restore somatosensation to these same patients. Somatosensation, including proprioception, the sense of limb position and movement, plays a crucial role in even basic motor tasks like reaching and walking. Its loss results in crippling deficits. Historical work dating back decades and even centuries has demonstrated that modality-specific sensations can be elicited by activating the central nervous system electrically. Recent work has focused on the challenge of refining these sensations by stimulating the somatosensory cortex (S1) directly. Animals are able to detect particular patterns of stimulation and even associate those patterns with particular sensory cues. Most of this work has involved areas of the somatosensory cortex that mediate the sense of touch. Very little corresponding work has been done for proprioception. Here we describe the effort to develop afferent neural interfaces through spatiotemporally precise intracortical microstimulation (ICMS). We review what is known of the cortical representation of proprioception, and describe recent work in our lab that demonstrates for the first time, that sensations like those of natural proprioception may be evoked by ICMS in S1. These preliminary findings are an important first step to the development of an afferent cortical interface to restore proprioception.

Keywords: Intracortical microstimulation (ICMS); Prosthesis; Somatosensation; Somatosensory cortex.

PubMed Disclaimer

Intracortical microstimulation (ICMS) in human primary somatosensory cortex (S1) has been used to successfully evoke naturalistic sensations. However, the neurophysiological mechanisms underlying the evoked sensations remain unknown. To understand how specific stimulation parameters elicit certain sensations we must first understand the representation of those sensations in the brain. In this study we record from intracortical microelectrode arrays implanted in S1, premotor cortex, and posterior parietal cortex of a male human participant performing a somatosensory imagery task. The sensations imagined were those previously elicited by ICMS of S1, in the same array of the same participant. In both spike and local field potential recordings, features of the neural signal can be used to classify different imagined sensations. These features are shown to be stable over time. The sensorimotor cortices only encode the imagined sensation during the imagery task, while posterior parietal cortex encodes the sensations starting with cue presentation. These findings demonstrate that different aspects of the sensory experience can be individually decoded from intracortically recorded human neural signals across the cortical sensory network. Activity underlying these unique sensory representations may inform the stimulation parameters for precisely eliciting specific sensations via ICMS in future work.

SIGNIFICANCE STATEMENT
Electrical stimulation of human cortex is increasingly more common for providing feedback in neural devices. Understanding the relationship between naturally evoked and artificially evoked neurophysiology for the same sensations will be important in advancing such devices. Here, we investigate the neural activity in human primary somatosensory, premotor, and parietal cortices during somatosensory imagery. The sensations imagined were those previously elicited during intracortical microstimulation (ICMS) of the same somatosensory electrode array. We elucidate the neural features during somatosensory imagery that significantly encode different aspects of individual sensations and demonstrate feature stability over almost a year. The correspondence between neurophysiology elicited with or without stimulation for the same sensations will inform methods to deliver more precise feedback through stimulation in the future.

Keywords: brain-machine interface; human; intracortical microstimulation; sensation; somatosensation.

Consciousness is a heavy quest that has puzzled philosophers for over two thousand years. Because of its subjectivity and elusiveness, it was not a subject for scientific study until recent decades. With the unprecedented advances of artificial intelligence (AI), in particular, the remarkable performance of large language models (LLM), understanding consciousness becomes pragmatic and pressing beyond the philosophical and academic debates — how can we tell if ChatGPT has consciousness, and how can humankind be prepared if “artificial” consciousness arises in the foreseeable future?

For the last three decades, neuroscientists have made initial strides in theorizing the inner workings of consciousness in human brains based on vast experimental data, as triggered primarily by two factors.

First, the advances in scientific methods have empowered scientists to study the activities of neural cell assemblies in awake-behaving primates and humans. These techniques include brain imaging technologies, neurophysiological recording of hundreds of neurons simultaneously, and neural network modeling propelled by AI.

Beyond the hype surrounding artificial intelligence (AI) in the enterprise lies the next step—artificial consciousness. The first piece in this practical AI innovation series outlined the requirements for this technology, which delved deeply into compute power—the core capability necessary to enable artificial consciousness. This piece looks at the control and storage technologies and requirements that are not only necessary for enterprise AI deployment but also essential to achieve the state of artificial consciousness.

Controlling unprecedented compute power

While artificial consciousness is impossible without a dramatic rise in compute capacity, that is only part of the challenge. Organizations must harness that compute power with the proper control plane nodes—the familiar backbone of the high availability server clusters necessary to deliver that power. This is essential for managing and orchestrating complex computing environments efficiently.