Lifeboat Foundation

Geneformer is a recently introduced and powerful AI model that learns gene network dynamics and interactions using transfer learning from vast single-cell transcriptome data. This tool enables researchers to make accurate predictions about gene behavior and disease mechanisms even with limited data, accelerating drug target discovery and advancing understanding of complex genetic networks in various biological contexts.

Developed by researchers at the Broad Institute of MIT and Harvard and their collaborators, the AI model Geneformer uses the highest-expressed genes in sc-RNA expression data to generate a dense representation of each cell, which can be used as features for various downstream predictive tasks. What makes Geneformer unique, however, are the capabilities its architecture enables, even when trained on very little data.

Geneformer has a BERT-like transformer architecture and was pre-trained on data from about 30M single-cell transcriptomes across various human tissues. Its attention mechanism enables it to focus on the most relevant parts of the input data. With this context-aware approach, the model can make predictions by considering ‌relationships and dependencies between genes.

Adult stem cell-derived organoids closely resemble their tissue of origin. This Review discusses recent developments in CRISPR-mediated genome engineering and its application using adult-stem-cell-derived organoids in the construction of isogenic disease models and for clinical gene repair.

Replicating these processes in AI systems is a significant challenge. One of the most exciting applications is in this field. Leveraging OI can help in training AI models more effectively. The dynamic neural networks in organoids can serve as a blueprint for creating more human-like AI systems.

The development of AI-enabled organoids is a promising field that combines AI with organoids to create more precise models of human organ functionality and diseases. This convergence could revolutionize drug discovery, disease diagnosis and the development of advanced treatments. AI helps organoids by guiding them through three crucial dimensions:

1. Hybrid Intelligence: A potential future scenario involves merging OI with traditional AI systems. This fusion could result in a new era of “hybrid intelligence” that combines the analytical power of AI with the nuanced understanding of human-like cognition.

Analyzed the global trends in this area of neuroscience. To identify and further facilitate the development of cerebral organoids, we utilized bibliometrics and visualization methods to analyze the global trends and evolution of brain organoids in the last 10 years. First, annual publications, countries/regions, organizations, journals, authors, co-citations, and keywords relating to brain organoids were identified. The hotspots in this field were also systematically identified. Subsequently, current applications for brain organoids in neuroscience, including human neural development, neural disorders, infectious diseases, regenerative medicine, drug discovery, and toxicity assessment studies, are comprehensively discussed.

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.

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

Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware.

Artificial intelligence (AI) is reshaping the future of human life across various real-world fields such as industry, medicine, society, and education¹. The remarkable success of AI has been largely driven by the rise of artificial neural networks (ANNs), which process vast numbers of real-world datasets (big data) using silicon computing chips ^{2, 3}. However, current AI hardware keeps AI from reaching its full potential since training ANNs on current computing hardware produces massive heat and is heavily time-consuming and energy-consuming ^{4– 6}, significantly limiting the scale, speed, and efficiency of ANNs. Moreover, current AI hardware is approaching its theoretical limit and dramatically decreasing its development no longer following ‘Moore’s law’^{7, 8}, and facing challenges stemming from the physical separation of data from data-processing units known as the ‘von Neumann bottleneck’^{9, 10}. Thus, AI needs a hardware revolution^{8, 11}.

A breakthrough in AI hardware may be inspired by the structure and function of a human brain, which has a remarkably efficient ability, known as natural intelligence (NI), to process and learn from spatiotemporal information. For example, a human brain forms a 3D living complex biological network of about 200 billion cells linked to one another via hundreds of trillions of nanometer-sized synapses^{12, 13}. Their high efficiency renders a human brain to be ideal hardware for AI. Indeed, a typical human brain expands a power of about 20 watts, while current AI hardware consumes about 8 million watts to drive a comparative ANN⁶. Moreover, the human brain could effectively process and learn information from noisy data with minimal training cost by neuronal plasticity and neurogenesis,^{14, 15} avoiding the huge energy consumption in doing the same job by current high precision computing approaches^{12, 13}.

In a recent study published in Neuron, researchers discovered that microglia, the brain’s immune cells, use tunneling nanotubes…

Scheiblich et al. uncover a novel mechanism by which microglia use tunneling nanotubes to connect with α-syn-or tau-burdened neurons, enabling transfer of these proteins to microglia for clearance. Microglia donate mitochondria to restore neuronal health, shedding light on new therapeutic strategies for neurodegenerative diseases.

Nano-MIND Technology for Wireless Control of Brain Circuits with Potential to Modulate Emotions, Social Behaviors, and Appetite.

Researchers at the Center for Nanomedicine within the Institute for Basic Science (IBS) and Yonsei University in South Korea have unveiled a groundbreaking technology that can manipulate specific regions of the brain using magnetic fields, potentially unlocking the secrets of high-level brain functions such as cognition, emotion, and motivation. The team has developed the world’s first Nano-MIND (Magnetogenetic Interface for NeuroDynamics) technology, which allows for wireless, remote, and precise modulation of specific deep brain neural circuits using magnetism.

The human brain contains over 100 billion neurons interconnected in a complex network. Controlling the neural circuits is crucial for understanding higher brain functions like cognition, emotion, and social behavior, as well as identifying the causes of various brain disorders. Novel technology to control brain functions also has implications for advancing brain-computer interfaces (BCIs), such as those being developed by Neuralink, which aim to enable control of external devices through thought alone.

Continue reading “New Technology to Control the Brain Using Magnetic Fields Developed” »