Lifeboat Foundation

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.

The American public intellectual and creator of the television series Closer to Truth, Robert Lawrence Kuhn has written perhaps the most comprehensive article on the landscape of theories of consciousness in recent memory. In this review of the consciousness landscape, Àlex Gómez-MarÃn celebrates Robert Kuhn €™s rejection of the monopoly of materialism and uncovers the radical implications of these new accounts of consciousness for meaning, artificial intelligence, and human immortality.

The scientific study of consciousness was not sanctioned by the mainstream until the nineties. Let us not forget that science stands on the shoulders of giants but also on the three-legged stool of data, theory, and socio-political wants. Thirty years later, the field has grown into a vibrant milieu of approaches blessed and burdened by covert assumptions, contradictory results, and conflicting implications. If the study of behaviour and cognition has become the Urban East, consciousness studies are the current Wild West of science and philosophy.

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}.

Remarks by David Chalmers in a memorial session for Daniel Dennett, at ASSC 27 (the 27th meeting of the Association for the Scientific Study of Consciousness) in Tokyo on July 3, 2024. Filmed by Van Royko and Marie-Philippe Gilbert for EyeSteelFilm.

“We discovered that the glass beads in the Chang’e-5 lunar soil can preserve iron particles of different sizes, from about 1 nanometer to 1 micrometer,” said Prof. Bai.

“It is generally difficult to distinguish npFe0 of different origins observed together in single samples. Here we used the rotation feature of the impact glass beads to clearly distinguish npFe0 formed before and after the solidification of the host glass beads.”

In this study, the scientists found numerous discrete large npFe0, tens of nanometers in size, which tended to concentrate towards the extremities of the glass beads. This concentration effect can cause ultralarge npFe0 to protrude from the extremities.

Researchers from the University of California, Berkeley have developed cutting-edge nanoscale optical imaging techniques to provide unprecedented insights into the ultrafast carrier dynamics in advanced materials. Two recent studies, published in Advanced Materials (“Transient Nanoscopy of Exciton Dynamics in 2D Transition Metal Dichalcogenides”) and ACS Photonics (“Near-Field Nanoimaging of Phases and Carrier Dynamics in Vanadium Dioxide Nanobeams”), showcase significant progress in understanding the carrier behaviors in two-dimensional and phase-change materials, with implications for next-generation electronic and optoelectronic devices.

The research team, led by Prof. Costas P. Grigoropoulos, Dr. Jingang Li, and graduate student Rundi Yang, employed a novel near-field transient nanoscopy technique to probe the behavior of materials at the nanoscale with both high spatial and temporal resolution. This approach overcomes the limitations of traditional optical methods, allowing researchers to directly visualize and analyze phenomena that were previously difficult to observe.

Schematic of the near-field transient nanoscopy. (Image: Adapted from DOI:10.1002/adma.202311568, CC BY-NC-ND 4.0)