Explore the vast range of titles available.

The Internet comprises a decentralized global system that serves humanity's collective effort to generate, process, and store data, most of which is handled by the rapidly expanding cloud. A stable, secure, real-time system may allow for interfacing the cloud with the human brain. One promising strategy for enabling such a system, denoted here as a \"human brain/cloud interface\" (\"B/CI\"), would be based on technologies referred to here as \"neuralnanorobotics.\" Future neuralnanorobotics technologies are anticipated to facilitate accurate diagnoses and eventual cures for the ∼400 conditions that affect the human brain. Neuralnanorobotics may also enable a B/CI with controlled connectivity between neural activity and external data storage and processing, via the direct monitoring of the brain's ∼86 × 10 neurons and ∼2 × 10 synapses. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) could traverse the blood-brain barrier (BBB), enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to synapses (synaptobots). They would then wirelessly transmit up to ∼6 × 10 bits per second of synaptically processed and encoded human-brain electrical information via auxiliary nanorobotic fiber optics (30 cm ) with the capacity to handle up to 10 bits/sec and provide rapid data transfer to a cloud based supercomputer for real-time brain-state monitoring and data extraction. A neuralnanorobotically enabled human B/CI might serve as a personalized conduit, allowing persons to obtain direct, instantaneous access to virtually any facet of cumulative human knowledge. Other anticipated applications include myriad opportunities to improve education, intelligence, entertainment, traveling, and other interactive experiences. A specialized application might be the capacity to engage in fully immersive experiential/sensory experiences, including what is referred to here as \"transparent shadowing\" (TS). Through TS, individuals might experience episodic segments of the lives of other willing participants (locally or remote) to, hopefully, encourage and inspire improved understanding and tolerance among all members of the human family.

Neuromodulation is crucial for advancing neuroscience and treating neurological disorders. However, traditional methods using rigid electrodes have been limited by large stimulating currents, low precision, and the risk of tissue damage. In this work, we developed a biocompatible ultraflexible electrode array that allows for both neural recording of spike firings and low‐threshold, high‐precision stimulation for neuromodulation. Specifically, mouse turning behavior can be effectively induced with approximately five microamperes of stimulating current, which is significantly lower than that required by conventional rigid electrodes. The array's densely packed microelectrodes enable highly selective stimulation, allowing precise targeting of specific brain areas critical for turning behavior. This low‐current, targeted stimulation approach helps maintain the health of both neurons and electrodes, as evidenced by stable neural recordings after extended stimulations. Systematic validations have confirmed the durability and biocompatibility of the electrodes. Moreover, we extended the flexible electrode array to a brain‐to‐brain interface system that allows human brain signals to directly control mouse behavior. Using advanced decoding methods, a single individual can issue eight commands to simultaneously control the behaviors of two mice. This study underscores the effectiveness of the flexible electrode array in neuromodulation, opening new avenues for interspecies communication and potential neuromodulation applications. Traditional neuromodulation using rigid electrodes has been limited by low precision, large stimulating currents, and the risk of tissue damage. In this work, we developed a biocompatible flexible electrode array that allows for both neural recording of spike firings and high‐precision, low‐threshold stimulation for neuromodulation. Combined with advanced decoding methods, we created a brain‐to‐brain interface that enables control of mouse behavior using human brain signals.

Brain-computer interfaces (BCIs) attract a lot of attention because of their ability to improve the brain's efficiency in performing complex tasks using a computer. Furthermore, BCIs can increase human's performance not only due to human-machine interactions, but also thanks to an optimal distribution of cognitive load among all members of a group working on a common task, i.e., due to human-human interaction. The latter is of particular importance when sustained attention and alertness are required. In every day practice, this is a common occurrence, for example, among office workers, pilots of a military or a civil aircraft, power plant operators, etc. Their routinely work includes continuous monitoring of instrument readings and implies a heavy cognitive load due to processing large amounts of visual information. In this paper, we propose a brain-to-brain interface (BBI) which estimates brain states of every participant and distributes a cognitive load among all members of the group accomplishing together a common task. The BBI allows sharing the whole workload between all participants depending on their current cognitive performance estimated from their electrical brain activity. We show that the team efficiency can be increased due to redistribution of the work between participants so that the most difficult workload falls on the operator who exhibits maximum performance. Finally, we demonstrate that the human-to-human interaction is more efficient in the presence of a certain delay determined by brain rhythms. The obtained results are promising for the development of a new generation of communication systems based on neurophysiological brain activity of interacting people. Such BBIs will distribute a common task between all group members according to their individual physical conditions.

This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications. Experimental papers primarily used transcranial magnetic stimulation (tMS) for the CBI portion of their B2BI. Most targeted the visual cortex to produce phosphenes. In terms of study design, 73.3% (11) are unidirectional and 86.7% (13) use only a 1:1 collaboration model (subject to subject). Limitations are apparent, as the CBI method varied greatly between studies indicating no agreed upon neurostimulatory method for transmitting information. Furthermore, only 12.4% (2) studies are more complicated than a 1:1 model and few researchers studied direct bidirectional B2BI. These studies show B2BI can offer advances in human communication and collaboration, but more design and experiments are needed to prove potential. B2BIs may allow rehabilitation therapists to pass information mentally, activating a patient's brain to aid in stroke recovery and adding more complex bidirectionality may allow for increased behavioral synchronization between users. The field is very young, but applications of B2BI technology to neuroergonomics and human factors engineering clearly warrant more research.

(2019) mention that they are exploring the use of functional magnetic resonance imaging (fMRI) as an avenue to overcome this limitation in information complexity which would increase the bandwidth of brain-to-brain communication. While the authors of the study do not reflect on the opportunities and risks of possible future uses of multi-person non-invasive direct BBIs, it is essential to widen the perspective beyond purely technical aspects to also consider possible future applications of this research and the ethical and social implications (Specker Sullivan and Illes, 2018). The ethical issues arising in multi-person BBIs considerably overlap with those in two-person BBIs and BCIs, and involve aspects related to safety, agency, shared control, accountability, privacy, identity, self-concept, and extended mind (Fenton and Alpert, 2008; Trimper et al., 2014; Hildt, 2015; Burwell et al., 2017; Pais-Vieira and Pais-Vieira, 2018; Cinel et al., 2019; Steinert and Friedrich, 2019). In order to protect individuals from unknowingly giving away sensitive information in BBIs and similar systems, there is a clear need to increase awareness of autonomy and privacy issues related to brain signals, to be transparent about what data is recorded and used and what the implications of this may be.

The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 ± 8.1 billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. ‘Computational neuroscience’ which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by ‘neuroinformatics’ approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-of-the-art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain–computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.

Advances in brain–brain interface technologies raise the possibility that two or more individuals could directly link their minds, sharing thoughts, emotions, and sensory experiences. This paper explores conceptual and ethical issues posed by such mind-merging technologies in the context of clinical neuroethics. Using hypothetical examples along a spectrum from loosely connected pairs to fully merged minds, the authors sketch out a range of factors relevant to identifying the degree of a merger. They then consider potential new harms like loss of identity, psychological domination, loss of mental privacy, and challenges for notions of autonomy and patient benefit when applied to merged minds. While radical technologies may seem to necessitate new ethical paradigms, the authors suggest the individual-focus underpinning clinical ethics can largely accommodate varying degrees of mind mergers so long as individual patient interests remain identifiable. However, advanced decisionmaking and directives may have limitations in addressing the dilemmas posed. Overall, mind-merging possibilities amplify existing challenges around loss of identity, relating to others, autonomy, privacy, and the delineation of patient interests. This paper lays the groundwork for developing resources to address the novel issues raised, while suggesting the technologies reveal continuity with current healthcare ethics tensions.

Previous studies have indicated that the expression of CCN3, a member of the CCN family of proteins, was tightly regulated during central nervous development and was associated with acquisition of cognitive functions in rats (Perbal, Mol Pathol 54(2):57–79, 2001; Su et al. Sheng Li Xue Bao 52(4):290–294, 2000) therefore suggesting that CCN3 might be involved in higher levels of physiological communication in the brain. In spite of the considerable amount of progress made into the understanding of neuronal organization and communication, reducing the knowledge gap between brain cellular biology and behavioral studies remains a huge challenge. Mind-to-mind communication has been the subject of numerous science fiction writings, intense research and emotional debates for many years. Scientists have tried for a long time to achieve transmission of messages between living subjects via non intrusive protocols. Thanks to the great progress made in imagery and neurosciences, another dimension of neuronal function in communication has now been documented. Two recent experimental demonstrations of direct brain to brain communication without physical contact (Grau et al. ( 2014 ) Conscious brain-to-brain communication in humans using non-invasive technologies. PLoS One. Aug 19;9(8)- - Rao et al. ( 2014 ) A direct brain-to-brain interface in humans. PLoS One. Nov 5;9(11)) pave the road to more sophisticated applications that could profoundly affect communications of humans with other humans, animals and machines. Although the wide use of such applications might seem a long way off, they raise quite a number of ethical, legal and societal issues.

Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. Therefore, appropriate conversion and translation of brain information is essential when decoding neural information using a model trained using another person’s data or to achieving brain-to-brain communication. We propose a brain representation transfer method that involves transforming a data representation obtained from one person’s brain into that obtained from another person’s brain, without relying on corresponding label information between the transferred datasets. We defined the requirements to enable such brain representation transfer and developed an algorithm that distills the assumption of common similarity structure across the brain datasets into a rotational and reflectional transformation across low-dimensional hyperspheres using encoders for non-linear dimensional reduction. We first validated our proposed method using data from artificial neural networks as substitute neural activity and examining various experimental factors. We then evaluated the applicability of our method to real brain activity using functional magnetic resonance imaging response data acquired from human participants. The results of these validation experiments showed that our method successfully performed representation transfer and achieved transformations in some cases that were similar to those obtained when using corresponding label information. Additionally, we reconstructed images from individuals’ data without training personalized decoders by performing brain representation transfer. The results suggest that our unsupervised transfer method is useful for the reapplication of existing models personalized to specific participants and datasets to decode brain information from other individuals. Our findings also serve as a proof of concept for the methodology, enabling the exchange of the latent properties of neural information representing individuals’ sensations.

When we feel connected or engaged during social behavior, are our brains in fact “in sync” in a formal, quantifiable sense? Most studies addressing this question use highly controlled tasks with homogenous subject pools. In an effort to take a more naturalistic approach, we collaborated with art institutions to crowdsource neuroscience data: Over the course of 5 years, we collected electroencephalogram (EEG) data from thousands of museum and festival visitors who volunteered to engage in a 10-min face-to-face interaction. Pairs of participants with various levels of familiarity sat inside the Mutual Wave Machine—an artistic neurofeedback installation that translates real-time correlations of each pair's EEG activity into light patterns. Because such inter-participant EEG correlations are prone to noise contamination, in subsequent offline analyses we computed inter-brain coupling using Imaginary Coherence and Projected Power Correlations, two synchrony metrics that are largely immune to instantaneous, noise-driven correlations. When applying these methods to two subsets of recorded data with the most consistent protocols, we found that pairs’ trait empathy, social closeness, engagement, and social behavior (joint action and eye contact) consistently predicted the extent to which their brain activity became synchronized, most prominently in low alpha (~7–10 Hz) and beta (~20–22 Hz) oscillations. These findings support an account where shared engagement and joint action drive coupled neural activity and behavior during dynamic, naturalistic social interactions. To our knowledge, this work constitutes a first demonstration that an interdisciplinary, real-world, crowdsourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions. Additionally, it is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory. Institutions such as museums, galleries, or any other organization where the public actively engages out of self-motivation, can help facilitate this type of citizen science research, and support the collection of large datasets under scientifically controlled experimental conditions. To further enhance the public interest for the out-of-the-lab experimental approach, the data and results of this study are disseminated through a website tailored to the general public (wp.nyu.edu/mutualwavemachine).