BioNICs: A Universal Neural Interface Architecture for Brain-Wide Symbiosis, Reality Bridging, and Co-evolution with Artificial Intelligence

Authors: ParisNeo & A Collective of AI Large Language Models (Grok, ChatGPT, Gemini via Google Search); Formatted and Curated via the Lollms WebUI Platform
Affiliations: Decentralized Human-AI Collaborative Research Initiative (Hypothetical)

Abstract:
The quest for a seamless, high-bandwidth interface between the human brain and computational systems confronts enduring challenges in biocompatibility, invasiveness, and long-term stability. Current Brain-Computer Interface (BCI) limitations also reflect the restricted throughput in human-AI cognitive synergy. We propose a transformative framework: BioNICs (Bio-hybrid Neural Interface Cells), envisioned as an autologous cellular collective that autonomously assembles a brain-wide, optically communicating mesh network. Following a single systemic introduction, these iPSC-derived, AI-designed, and CRISPR-engineered cells would navigate the neural parenchyma, establish functional connections, implement sophisticated routing protocols, and converge to form a specialized, entirely biological Dermal Bio-Port. This living organ acts as the sole biological termination point for the internal network. An external, non-invasive Transceiver Module (ETM), containing all abiotic electronics, would optically and magnetically couple to the Dermal Bio-Port, transducing high-fidelity neural data to standard communication protocols (e.g., USB, Wi-Fi, 5G) for direct internet access and interaction with external AI. This architecture aims to eliminate chronic indwelling abiotic hardware from the brain. BioNICs could revolutionize neurological therapies and enable profound cognitive augmentation, potentially allowing interaction with, and creation of, rich virtual realities indistinguishable from perceived existence, and even offering tools to explore the fundamental nature of consciousness and reality itself, including speculative frameworks such as the simulation hypothesis. This article, itself a testament to nascent human-AI collaboration, details the BioNICs concept, its enabling technologies, its potential to address existential questions posed by advanced AI, and the immense scientific, ethical, and philosophical vistas it opens.

Keywords: Brain-Computer Interface (BCI), Neural Augmentation, Synthetic Biology, AI-driven Design, AlphaFold, CRISPR-Cas, Induced Pluripotent Stem Cells (iPSCs), Neuroethics, Cellular Engineering, Neural Interface, Optogenetics, Biophotonics, Neural Networks, Living Interface, Dermal Bio-Port, Brain-Wide Mesh, Reality Bridging, Simulation Hypothesis, Human-AI Symbiosis, Cognitive Bandwidth.

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1. Introduction: The Imperative for a Universal, High-Bandwidth, Living Neural Interface in an Evolving Reality

The human brain, a labyrinth of approximately 86 billion neurons interconnected by trillions of synapses, orchestrates the symphony of our consciousness, cognition, and perception of reality (Azevedo et al., 2009). The enduring aspiration to establish direct, high-bandwidth communication with this biological marvel via Brain-Computer Interfaces (BCIs) has fueled decades of scientific inquiry (Wikipedia contributors, “Brain–computer interface”; Saska et al., 2022; Saha et al., 2021). BCIs promise not only the restoration of lost sensory and motor functions (Hochberg et al., 2012; Collinger et al., 2013) and the alleviation of severe neurological and psychiatric disorders (Lozano et al., 2019) but also pathways to augment human cognitive faculties, potentially redefining our interaction with information, each other, and reality itself.

The urgency for such advanced interfaces is amplified by the exponential trajectory of Artificial Intelligence (AI). As AI systems approach and potentially surpass human general intelligence, the “throughput problem”—the profound mismatch in processing speed and data exchange bandwidth between human cognition and AI systems—becomes a critical bottleneck (Kurzweil, 2005; Bostrom, 2014). This limitation, evident even in the human-AI collaborative process used to generate this manuscript, constrains our ability to fully comprehend, co-create with, and guide increasingly complex AI. This rapidly approaching technological horizon, characterized by increasingly powerful AI, presents an existential imperative: to find pathways for co-evolution with AI that preserve human agency, purpose, and the drive for discovery, necessitating a fundamental upgrade in our ability to interact with complex information and intelligent systems.

Current BCI technologies, despite their ingenuity, fall short of this ideal. Invasive abiotic implants often incite chronic foreign body responses, leading to glial scarring and performance degradation (Deger et al., 2024; Barrese et al., 2013; Polikov et al., 2005). Non-invasive methods sacrifice crucial resolution and bandwidth (Waldert, 2016). The challenge, therefore, is to design an interface that is not merely tolerated by the brain but becomes a living, integrated, and high-capacity extension of it.

This paper proposes BioNICs (Bio-hybrid Neural Interface Cells), a radical conceptual leap. We envision a system initiated by a single introduction of autologous, iPSC-derived cells. These cells, meticulously programmed using AI-driven design (including tools like AlphaFold 3 for biomolecular engineering; AlphaFold Team, 2024a) and CRISPR-based genome editing, would autonomously:

1. Navigate and permeate the entire brain.
2. Assemble into a brain-wide, optically communicating mesh network with sophisticated internal routing capabilities.
3. Converge and differentiate to form a novel, entirely biological Dermal Bio-Port—a specialized skin-integrated organ serving as the network’s terminus.
4. Enable an external, non-invasive Transceiver Module (ETM) to optically couple with this Dermal Bio-Port, transducing neural data to standard digital communication protocols, thereby keeping all abiotic electronics outside the body.

Such an architecture could offer unparalleled biocompatibility and information throughput. Beyond therapeutic and cognitive augmentation, a brain-wide BioNICs system could unlock unprecedented possibilities: the creation and experience of fully immersive virtual realities, the expansion of dream states into explorable domains, and even tools to probe the very fabric of consciousness and existence, potentially engaging with profound philosophical questions such as the simulation hypothesis (Bostrom, 2003; Chalmers, 2022). This article explores the BioNICs concept, its technological underpinnings, its potential to redefine human experience and our co-evolution with AI, and the vast scientific, ethical, and philosophical landscapes it compels us to navigate.

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2. Related Work: Current State-of-the-Art in Brain-Computer Interfaces

The pursuit of effective BCIs has yielded a diverse array of technologies, each representing a different balance of invasiveness, resolution, and application focus. Understanding these existing approaches is crucial for appreciating the potential advancements offered by a BioNICs framework.

• 2.1. Invasive Electrical BCIs: Probing Deep and Wide
  • Penetrating Microelectrode Arrays (MEAs): These devices aim for high-resolution recording and/or stimulation by inserting electrodes directly into the brain parenchyma.
    • Neuralink Corporation is aggressively pursuing a high-channel-count BCI using thousands of flexible polymer threads (“electrodes”), each significantly thinner than a human hair. These are implanted by a bespoke surgical robot designed to minimize tissue damage and avoid vasculature (Neuralink, n.d.-a; Neuralink, n.d.-b). Their initial focus is on restoring communication and motor control for individuals with paralysis, and they have reported their first human implant (Neuralink, 2024a; Neuralink, 2024b). While the use of flexible materials and automated insertion aims to mitigate the foreign body response, long-term stability and the full extent of gliosis remain areas of ongoing research and observation (Neuralink, n.d.-c; Barrese et al., 2013).
    • Blackrock Neurotech (formerly Blackrock Microsystems) has a long-standing presence with its Utah Array, a silicon-based MEA that has been instrumental in numerous landmark BCI studies, enabling individuals with paralysis to control robotic arms, communicate, and even experience restored sensory feedback (Hochberg et al., 2012; Collinger et al., 2013; Blackrock Neurotech, n.d.). The Utah Array provides robust, high-quality signals but, like other penetrating MEAs, faces challenges related to chronic tissue response and implant longevity.
    • Academic Research Labs worldwide continue to innovate with MEA design, exploring novel materials (e.g., carbon fibers, conducting polymers), geometries, and coatings to enhance biocompatibility and recording stability (Shafique et al., 2021; Kozai et al., 2012).
  • Deep Brain Stimulation (DBS) Electrodes: Primarily a therapeutic technology, DBS involves implanting electrodes into specific subcortical structures (e.g., subthalamic nucleus, globus pallidus) to deliver electrical pulses. Companies like Medtronic, Abbott, and Boston Scientific are leaders in DBS systems for conditions such as Parkinson’s disease, essential tremor, and dystonia (Lozano et al., 2019). While demonstrating the feasibility of long-term neural implants, DBS electrodes are typically designed for low-channel-count electrical stimulation rather than high-bandwidth data acquisition or nuanced information encoding required for advanced BCIs. However, some research explores adapting DBS hardware for limited recording capabilities.
• 2.2. Semi-Invasive Electrical BCIs: Balancing Resolution and Risk
  • Electrocorticography (ECoG): ECoG involves placing electrode arrays directly on the surface of the brain (epidural or subdural), without penetrating the cortex. This approach offers a compromise between the high resolution of MEAs and the lower invasiveness of scalp EEG.
    • The CEA-Clinatec WIMAGINE® implant is a prominent example of a fully implanted, wireless ECoG device. Developed in Grenoble, France, it has been used in clinical trials to allow individuals with tetraplegia to control exoskeletons and digital avatars by decoding brain activity from the motor cortex (Clinatec, n.d.; Benabid et al., 2019; Sauter-Starace et al., 2023). WIMAGINE highlights the potential of chronic, wireless ECoG for functional restoration.
    • Synchron’s Stentrode™ represents an endovascular BCI approach. This device, resembling a stent, is delivered via cerebral blood vessels to sit adjacent to the motor cortex, where its electrodes can record neural signals (Synchron, n.d.; Oxley et al., 2021). This method avoids direct brain surgery, significantly reducing invasiveness, but the signal quality and spatial resolution are inherently limited by the distance from neurons and the vascular environment.
  • ECoG generally provides better signal-to-noise ratio and spatial resolution than EEG and is less susceptible to artifacts. However, it still records population-level activity, lacks single-neuron resolution, and the long-term biocompatibility of dural implants, while better than intraparenchymal ones, is an ongoing area of study (Schalk & Leuthardt, 2011; Harris et al., 2011).
• 2.3. Non-Invasive BCIs: Safety First, Resolution Second
  • Electroencephalography (EEG): Records electrical activity from the scalp using electrodes. It is the most widely used BCI modality due to its safety, low cost, and ease of use. Applications range from assistive communication to neurofeedback and research (Wolpaw et al., 2002). However, EEG suffers from poor spatial resolution and is susceptible to muscle artifacts and signal distortion by the skull (Waldert, 2016).
  • Magnetoencephalography (MEG): Measures the weak magnetic fields generated by neuronal currents. MEG offers better spatial resolution than EEG as magnetic fields are less distorted by the skull. However, MEG systems are bulky, expensive (requiring magnetically shielded rooms and SQUID sensors), and not practical for everyday BCI use (Baillet, 2017).
  • Functional Near-Infrared Spectroscopy (fNIRS): An optical imaging technique that measures changes in blood oxygenation (hemodynamic response) associated with neural activity. fNIRS is portable and relatively inexpensive, offering moderate spatial resolution but a much slower temporal response compared to electrophysiological methods, limiting its use for real-time control (Saha et al., 2021; Scholkmann et al., 2014).
• 2.4. Research-Focused and Emerging Modalities: Pushing the Boundaries
  • Optogenetics: This revolutionary technique, primarily used in animal models, involves genetically modifying specific neurons to express light-sensitive ion channels or pumps (opsins). This allows researchers to control neuronal activity (excitation or inhibition) with high temporal and cell-type specificity using light delivered via optical fibers or integrated light sources (Boyden et al., 2005; Deisseroth, 2011; Wikipedia contributors, “Optogenetics”). While transformative for neuroscience research, its translation to human BCI applications faces hurdles related to safe and efficient gene delivery to human neurons (typically using viral vectors, which carry their own risks) and the need for implanted light delivery systems. Nevertheless, optogenetics demonstrates the immense potential of light as a precise modality for neural communication. Clinical trials using optogenetics for restoring vision are underway, indicating a potential path forward (Sahel et al., 2021).
  • Sonogenetics and Magnetogenetics: These are emerging research fields aiming to control neural activity using ultrasound or magnetic fields, respectively, often in conjunction with genetically engineered or nanoparticle-mediated sensitizers (Wheeler et al., 2016; Estelrich et al., 2021; Bar-Shir et al., 2023; Lee et al., 2023). These modalities are in earlier stages of development for neural applications compared to optogenetics.
  • Advanced Materials and Neurotrophic Factors: A significant body of research focuses on developing novel biomaterials for implants that are more flexible, conductive, and bioactive to reduce the foreign body response (Kumar et al., 2023; Harris et al., 2011; Seo et al., 2013; Shafique et al., 2021). This includes incorporating neurotrophic factors or anti-inflammatory drugs into implant coatings.
• 2.5. Unmet Needs Addressed by BioNICs:
  This overview highlights several critical unmet needs in the BCI field:
  1. True Long-Term Biocompatibility: Moving beyond mere tolerance to active, symbiotic integration with minimal chronic inflammation or tissue damage.
  2. High Information Bandwidth with Specificity: Achieving communication channels capable of transmitting complex information to and from specific neural circuits or cell types.
  3. Reduced Chronic Invasiveness: Minimizing the long-term burden of implanted abiotic hardware within or on sensitive neural tissue.
  4. Adaptive and Stable Interfaces: Developing interfaces that can maintain high performance over decades and potentially adapt to brain plasticity.
     The BioNICs concept is specifically envisioned to address these fundamental limitations by proposing a radical shift from abiotic implants to a living, autologous, and optically-communicating cellular interface.

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3. Mechanism of Key Enabling Technologies and the Enhanced Plausibility of BioNICs

The realization of BioNICs, while ambitious, is grounded in the confluence of several rapidly advancing scientific and technological frontiers. These include AI-driven biomolecular engineering, high-precision genome editing, advanced iPSC technology, and the burgeoning fields of optogenetics and biophotonics.

• 3.1. AlphaFold 3 and AI in Biomolecular and Genetic Circuit Design: Programming Life’s Building Blocks
  • Mechanism and Evolution of AlphaFold: The challenge of predicting the three-dimensional structure of a protein from its linear amino acid sequence was a grand challenge in biology for half a century. DeepMind’s AlphaFold 2 provided a revolutionary breakthrough, employing a deep learning system incorporating attention-based mechanisms (an “Evoformer” block that reasons about relationships between sequence positions and in 3D space) to achieve unprecedented accuracy (Jumper et al., 2021). AlphaFold 3, announced in 2024, represents a further leap. It extends its predictive power beyond proteins to a vast range of biomolecules, including DNA, RNA, small molecule ligands, ions, and their complex interactions. A key innovation in AlphaFold 3 is an enhanced Evoformer module and the replacement of the structure module with a diffusion model, akin to those used in AI image generation. This diffusion process starts with a cloud of atoms and iteratively refines their positions to converge on the final, accurate 3D structure (AlphaFold Team, 2024a; Isomorphic Labs, 2024; Aljabali et al., 2024). This allows it to model covalent bonds, chemical modifications, and interactions between diverse molecular types with greater fidelity.
  • Impact on BioNICs Design: The capabilities of AlphaFold 3 and similar advanced AI tools are transformative for the de novo design and optimization of BioNIC components:
    1. Designing Bespoke Optical Proteins: For optical output, AI can design novel fluorescent proteins or luciferases with specific emission spectra (e.g., in the near-infrared I or II windows for optimal tissue penetration; Hong et al., 2017), high quantum yields, fast kinetics, and enhanced stability. For optical input, opsins (like channelrhodopsins or bacteriorhodopsins) can be engineered for specific wavelength sensitivities (e.g., to red or NIR light to minimize phototoxicity and maximize tissue penetration), tailored channel conductances, and precise on/off kinetics (Wikipedia contributors, “Optogenetics”; Deisseroth, 2011). This allows for fine-tuning the “optical language” of BioNICs.
    2. Engineering Highly Specific Receptors and Ion Channels: BioNICs need to sense native neuronal activity. AI can design or modify neurotransmitter receptors (e.g., for glutamate, GABA) to have increased affinity, altered desensitization rates, or to couple to specific downstream signaling pathways within the BioNIC. Similarly, voltage-gated ion channels crucial for sensing local field potentials can be optimized.
    3. Optimizing Protein Stability and Biocompatibility: Proteins designed for long-term function within the brain must be exceptionally stable and non-immunogenic. AI can predict and help redesign protein surfaces to minimize aggregation and recognition by the immune system.
    4. Predicting and Designing Molecular Interactions: Crucially, AI can model how engineered proteins will interact with each other within a BioNIC (e.g., forming signaling complexes) and with the host’s extracellular matrix and cellular environment, aiding in the design of robust and well-integrated cellular constructs, including those necessary for cell guidance, network formation, and dermal organ development.
    5. AI for Synthetic Gene Circuit Design: Beyond individual proteins, AI is increasingly crucial for designing complex synthetic gene circuits – networks of interacting genes and regulatory elements that control cellular behavior (Myers et al., 2022; Carbonell, n.d.; UVJ Technologies, 2024; Buvailo, 2023; Gumber et al., 2022). For BioNICs, AI can help design circuits that:
       • Trigger optical output only upon detection of specific, complex neural firing patterns (e.g., implementing logical AND/OR gates for signal processing).
       • Precisely control the levels and timing of neuromodulator release in response to optical input from the SDP.
       • Implement robust safety switches (e.g., ensuring apoptosis occurs reliably upon external signal).
       • Regulate cell division for controlled self-renewal of the BioNIC network and for dermal organogenesis.
       • Program Cell Migration and Targeting: Designing gene circuits that enable BioNICs to respond to endogenous chemoattractant gradients or recognize specific cellular markers for brain-wide, yet patterned, distribution and for targeted convergence at the dermal site.
       • Orchestrate Network Self-Assembly and Routing: Developing genetic programs that allow BioNICs to establish local optical links, form a cohesive mesh, and implement distributed routing protocols for efficient data transmission from any point in the brain to the Dermal Bio-Port. This might involve AI-designed cell-cell communication systems using secreted factors or contact-dependent signaling to coordinate network topology.
       • Direct Dermal Organogenesis: Engineering complex gene regulatory networks that instruct a subset of BioNICs, upon reaching the target dermal location, to differentiate and organize into a structured, optically accessible Dermal Bio-Port, potentially recruiting and remodeling host dermal cells. This could involve AI-driven design of synthetic developmental programs.
  • Current Limitations and Future Directions in AI Design: While powerful, current AI protein design tools are still evolving. Designing for dynamic functions, allosteric regulation, and accurately predicting in vivo behavior in complex cellular environments remain significant challenges. The iterative loop of AI prediction, experimental validation (e.g., in cell culture, then animal models), and AI model refinement will be essential.
• 3.2. Advanced CRISPR Technologies: Precision Genome Editing for Cellular Programming
  • Mechanism and Evolution: The CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein) system, originally a bacterial immune mechanism, has been repurposed into a versatile genome editing tool.
    • CRISPR-Cas9: The most well-known system uses a guide RNA (gRNA) to direct the Cas9 nuclease to a complementary DNA sequence, where it induces a site-specific double-strand break (DSB) (Jinek et al., 2012). The cell’s repair machinery can then lead to gene knockout (via non-homologous end joining, NHEJ) or, if a DNA repair template is provided, precise sequence insertion or modification (via homology-directed repair, HDR).
    • Base Editors (BEs): To improve precision and avoid the potentially deleterious effects of DSBs, base editors were developed. These typically fuse a catalytically impaired Cas9 (nCas9, which only nicks one DNA strand) or a dead Cas9 (dCas9, which binds but doesn’t cut) to a DNA deaminase enzyme. Guided by a gRNA, the deaminase directly converts one DNA base to another (e.g., C•G to T•A using a cytosine base editor, or A•T to G•C using an adenine base editor) within a small editing window without making DSBs (Komor et al., 2016; Gaudelli et al., 2017).
    • Prime Editors (PEs): Representing a further advancement, prime editing offers more versatility than base editing, capable of all 12 possible base-to-base conversions, as well as small insertions and deletions, again without requiring DSBs or donor DNA templates for HDR (Anzalone et al., 2019; Nouri & McClements, 2020). PEs use an nCas9 fused to a reverse transcriptase and a prime editing guide RNA (pegRNA). The pegRNA both targets the genomic locus and contains the template for the desired edit, which is directly reverse transcribed into the target site.
    • Other Cas Variants and Delivery: Numerous other Cas enzymes (e.g., Cas12a/Cpf1, Cas13 for RNA targeting) offer different properties. For ex vivo editing of iPSCs, delivery of CRISPR components can be achieved via electroporation of ribonucleoprotein complexes (Cas protein + gRNA), or viral vectors (e.g., lentivirus, AAV), followed by rigorous selection and quality control of correctly edited cells.
  • Impact on BioNICs Engineering: High-precision CRISPR technologies are indispensable for:
    1. Targeted Gene Integration: Accurately inserting the large and complex genetic cassettes encoding AI-designed optical proteins, specialized receptors, optogenetic actuators, and components of synthetic gene circuits (including those for autonomous navigation, network formation, routing, and dermal organogenesis) into safe harbor loci within the iPSC genome. This ensures stable, predictable expression and minimizes the risk of insertional mutagenesis.
    2. Endogenous Gene Modification: Precisely altering native genes to modulate their function, for example, to upregulate specific receptors or to integrate safety switches seamlessly with endogenous cellular pathways.
    3. Multiplex Genome Editing: The ability to make multiple, precise edits simultaneously is crucial for constructing BioNICs with their multifaceted functionalities. Base and prime editing are particularly suited for this due to their reduced reliance on DSB-dependent repair pathways (Lee & Hur, 2021; Lattanzi et al., 2021).
    4. Ensuring Safety: Critically, the enhanced precision of base and prime editing, which largely avoid DSBs, helps to minimize off-target edits and reduce the risk of larger genomic rearrangements (e.g., translocations, large deletions) that can be associated with DSB-based editing (Nahas, 2024). This is paramount when engineering cells destined for human implantation. Rigorous whole-genome sequencing and functional assays post-editing are essential safety checks.
• 3.3. The BioNICs Advantage: A Universal, Living, and Non-Invasive Electronic Interface
  The integration of AI-driven design with precision genome editing of autologous iPSCs forms the core of the BioNICs proposal, offering scientifically plausible advantages over traditional BCI approaches:
  1. Unprecedented Biocompatibility and Deep Integration: BioNICs, being derived from the patient’s own cells (autologous iPSCs reprogrammed using methods like those described by Takahashi & Yamanaka, 2006; Kisler et al., 2017; Maherali & Hochedlinger, 2008; Rocha et al., 2022a), are expected to elicit minimal immune response and foreign body reaction, a major failure point for traditional implants (Deger et al., 2024). Their living nature allows for dynamic, brain-wide integration and potential self-renewal, unlike static synthetic materials.
  2. Brain-Wide, High-Bandwidth Optical Communication with Inherent Routing: Light-based communication is facilitated by a self-assembling mesh network capable of routing signals from any brain region to the Dermal Bio-Port, offering comprehensive brain access. Optical methods provide high channel density and specificity with minimal crosstalk (BIOEE Columbia University, n.d.; Martins et al., 2024; Liu et al., 2021). Multiplexing information via wavelength, intensity, and temporal modulation allows for rich data streams (Gößl et al., 2021; Mehta & Zhang, 2022; The BRAIN Initiative Alliance, 2021).
  3. AI-Optimized Autonomous Cellular Machinery: The ability to design proteins and genetic circuits de novo using AI means that BioNICs are not limited by naturally evolved biological components. Their autonomous navigation, network formation, sensing, signaling, and modulatory functions can be custom-tailored.
  4. Adaptive, “Updatable,” and Self-Repairing Living Interface: The brain-wide mesh could potentially self-repair minor disruptions. The Dermal Bio-Port, being living, could also exhibit self-maintenance. Future introduction of new BioNICs could allow the interface to evolve its capabilities.
  5. Elimination of Indwelling Abiotic Electronics from the Brain: This is a major advantage. All electronic signal transduction and wireless communication hardware is externalized to the ETM, interfacing non-invasively with the purely biological Dermal Bio-Port. This dramatically reduces risks associated with long-term electronic implants within the body.

While acknowledging the immense engineering and biological challenges ahead, the convergence of these enabling technologies provides a strong theoretical foundation for the plausibility of developing BioNICs as a transformative BCI paradigm.

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4. BioNICs System Architecture: A Brain-Wide Cellular Network Terminating in an Engineered Dermal Bio-Port

The BioNICs system is reconceptualized as an autonomously self-assembling, brain-wide cellular network that interfaces with external technology via a de novo engineered, purely biological Dermal Bio-Port, which is then addressed non-invasively by an External Transceiver Module (ETM).

• 4.1. Autologous Cell Sourcing and BioNIC Engineering: Programming Autonomous System Construction
  • 4.1.1. Personalized Cell Source: The process begins with the harvesting of a small sample of somatic cells (e.g., dermal fibroblasts via skin biopsy, or peripheral blood mononuclear cells). These cells are then reprogrammed into induced Pluripotent Stem Cells (iPSCs) using established non-integrating methods (e.g., Sendai virus, mRNA transfection, or episomal vectors) to ensure genomic integrity and pluripotency (Takahashi & Yamanaka, 2006; Kisler et al., 2017; Rocha et al., 2022a). Each iPSC line undergoes rigorous quality control, including karyotyping, pluripotency marker expression analysis, and differentiation potential assessment.
  • 4.1.2. AI-Driven Design of Genetic Master Programs: AI algorithms (Section 3.1) design the comprehensive genetic programs necessary for BioNICs’ complex autonomous behaviors:
    • Optical Reporter/Actuator Systems: Designing genes for highly efficient, rapidly responsive, and biocompatible light-emitting proteins (e.g., NIR-shifted luciferases or fluorescent proteins) that are minimally phototoxic and whose emission can be reliably detected through overlying tissue. Engineering light-sensitive channels, pumps, or enzymes (opsins) with specific spectral sensitivities (preferably in the NIR spectrum for deep tissue penetration and reduced scattering; Davoudi et al., 2023; Cayce et al., 2014; Cayce et al., 2011), defined activation/inactivation kinetics, and minimal off-target effects in response to ETM-delivered light.
    • Neuronal Activity Sensors: Optimizing or designing receptors and ion channels to detect subtle changes in local neurotransmitter concentrations, ion fluxes, or electrical field potentials indicative of specific neuronal firing patterns.
    • Multi-Stage Synthetic Gene Circuits for Autonomous Development:
      • Brain-Wide Migration and Patterning: Genetic programs enabling BioNICs to respond to complex combinations of endogenous signaling gradients (e.g., chemokines, neurotrophic factors) or to express specific adhesion molecules, allowing them to navigate the entire brain parenchyma, achieving widespread but potentially patterned distribution (e.g., respecting major white matter tracts, forming specific densities in different cortical layers or nuclei).
      • Network Self-Assembly and Optical Routing: Genetic instructions for BioNICs to establish local optical communication links with neighbors, differentiate into “router” or “node” phenotypes, and implement distributed algorithms to form a cohesive, brain-wide mesh network. This network would be capable of efficiently relaying optical signals from any point of origin to the designated Dermal Bio-Port convergence zone. This may involve AI-designed cell-cell communication using secreted light-emitting molecules or contact-dependent signaling to coordinate network topology and routing table formation.
      • Dermal Bio-Port Organogenesis: A sophisticated genetic sub-program that activates when BioNICs reach a pre-targeted dermal location (e.g., defined by specific biochemical markers or an externally applied temporary cue). This program would orchestrate their differentiation, proliferation, and organization into a multi-layered, structured Dermal Bio-Port. This new organ would be densely populated with BioNICs specialized for optical input/output at its surface and possess features for stable integration with the host dermis. It might also involve programmed recruitment and remodeling of host keratinocytes and fibroblasts.
      • Micro-Magnetic Field Generation for Alignment: A subset of BioNICs within the Dermal Bio-Port could be engineered to synthesize and array biocompatible magnetic nanoparticles (e.g., ferritin-based) in specific patterns, generating subtle, localized magnetic fields. These fields would serve as passive alignment guides for the External Transceiver Module.
    • Safety and Control Loci: Inclusion of multiple, redundant inducible apoptosis systems (e.g., iCasp9) responsive to externally administered, non-toxic small molecules (Di Stasi et al., 2011; Hoyos et al., 2014; Marin et al., 2016; Zhou et al., 2014). Contact inhibition pathways and cell-cycle checkpoints will be reinforced to prevent uncontrolled proliferation.
  • 4.1.3. Precision Genome Editing of iPSCs: The AI-designed genetic master programs are then introduced into the validated iPSCs using advanced CRISPR-Cas technologies (Section 3.2), such as prime editing or base editing, to ensure precise, multiplexed integration into pre-defined safe harbor loci. This minimizes the risk of insertional mutagenesis and ensures predictable transgene expression. Edited iPSC clones are meticulously screened for correct edits, absence of off-target mutations (via whole-genome sequencing), genomic stability, and maintained pluripotency.
  • 4.1.4. Differentiation into BioNIC Progenitors: Engineered iPSCs are differentiated in vitro into a highly potent BioNIC progenitor state, capable of executing the multi-stage developmental program upon introduction into the body.
• 4.2. BioNIC System Deployment and Autonomous Network/Organ Formation
  • 4.2.1. Single Systemic or CNS Introduction: The BioNIC progenitors are introduced via a single, minimally invasive procedure (e.g., intravenous injection with engineered BBB-crossing capabilities, or a single lumbar puncture for CNS-wide seeding).
  • 4.2.2. Autonomous Brain-Wide Mesh Assembly and Dermal Bio-Port Organogenesis: Post-introduction, the BioNIC progenitors execute their genetic programs:
    • They navigate the circulatory system (if IV) or CSF, enter the brain parenchyma, and distribute throughout its volume.
    • They establish local connections, forming the optical mesh network and implementing routing protocols.
    • A subset converges at the targeted dermal site, initiating the formation of the Dermal Bio-Port, which matures over weeks or months into a stable, optically accessible biological interface.
• 4.3. The Dermal Bio-Port and External Transceiver Module (ETM): The Non-Invasive Symbiotic Link
  • 4.3.1. The Dermal Bio-Port: This is an entirely biological, living organ integrated into the patient’s skin, formed and maintained by the BioNICs. Its surface is densely populated with optically active BioNICs, forming an organized array for efficient light input/output. It contains the terminus of the brain-wide BioNIC routing network and the micro-magnetic alignment cues.
  • 4.3.2. The External Transceiver Module (ETM): This is a compact, wearable device that contains all abiotic electronics.
    • Non-Invasive Coupling: The ETM attaches to the skin over the Dermal Bio-Port, aligning precisely via magnetic coupling to the fields generated by the Bio-Port. This ensures optimal optical alignment between the ETM’s emitters/detectors and the Bio-Port’s surface BioNICs.
    • Optical Transduction: The ETM houses the highly sensitive photodetector arrays to read optical signals from the Bio-Port and the micro-emitter arrays to send optical signals to it.
    • Onboard AI Processing & Standard Communication: Contains the neuromorphic chip with DNNs/SNNs for decoding/encoding neural information (as in the previous SDP concept). Critically, the ETM also contains standard communication interfaces (e.g., USB-C, Wi-Fi/6G transceivers, RJ45 port if docked) allowing it to connect to computers, the internet, or other devices, effectively acting as a universal translator between the brain’s optical BioNIC language and digital electronic languages.
    • Power: The ETM is externally powered (e.g., rechargeable batteries) and may also provide transdermal wireless power to the Dermal Bio-Port if its metabolic needs require it (though the goal is for the Bio-Port to be largely self-sustaining or draw from host physiology).
• 4.4. Co-Training Phase:
  Once the BioNIC network and Dermal Bio-Port are mature, and the ETM is coupled, a crucial co-training phase begins. This involves:
  • Brain Learning: The user’s brain learns to generate consistent neural patterns that the BioNICs translate into clear optical signals for specific intentions (e.g., “move cursor left,” “search for ‘X'”).
  • ETM AI Learning: The AI in the ETM learns to accurately decode the user’s unique BioNIC optical “dialect” and to encode external information into optical patterns that the user’s brain (via BioNICs) can meaningfully interpret.
    This adaptive learning process, involving neurofeedback and AI model refinement, is key to achieving a high-fidelity, intuitive interface.

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5. Projected Outcomes and Potential Applications: Redefining Reality and Human Experience

The successful implementation of a brain-wide BioNICs system with a Dermal Bio-Port and ETM could redefine the boundaries of human experience, therapy, and our place in the universe.

• 5.1. Transformative Therapeutic Interventions:
  • Restoration of Sensory and Motor Function: For individuals with paralysis (e.g., due to spinal cord injury, stroke, ALS), BioNICs could decode motor intentions with high fidelity to control advanced prosthetic limbs, exoskeletons, or functional electrical stimulation systems, offering unprecedented fluidity and naturalness of movement. Similarly, by encoding sensory information from artificial sensors (e.g., retinal implants, cochlear implants) into appropriate neural patterns via BioNICs, it may be possible to restore a significant degree of vision or hearing.
  • Treatment of Neurodegenerative Disorders: In conditions like Alzheimer’s disease, BioNICs could potentially be used to augment failing memory circuits by reinforcing endogenous memory traces or by creating a direct interface for external memory aids. For Parkinson’s disease, precisely targeted optical neuromodulation via BioNICs could offer a more refined and adaptable alternative to current DBS, potentially with fewer side effects by selectively targeting specific cell types or pathways.
  • Management of Psychiatric and Neurological Disorders: For intractable epilepsy, BioNICs could detect pre-seizure neural signatures and deliver precisely timed optical stimuli to preempt seizure activity. In severe depression, anxiety, or OCD, closed-loop BioNIC systems could monitor neural activity associated with pathological states and deliver subtle neuromodulation to restore healthier patterns, offering a highly personalized and adaptive therapy. The brain-wide access could offer even more comprehensive therapeutic targeting for diffuse neurological conditions.
  • Enhanced Neurorehabilitation: Following brain injury, BioNICs could facilitate neurorehabilitation by promoting targeted neural plasticity, guiding the rewiring of neural circuits, and providing real-time feedback on brain activity to optimize therapeutic exercises.
• 5.2. Profound Cognitive Augmentation and Experiential Realities:
  • Accelerated Learning and Skill Acquisition: By directly interfacing with learning and memory circuits (e.g., in the hippocampus and neocortex), BioNICs could potentially facilitate the rapid assimilation of new knowledge, languages, or complex motor skills. This might involve optimized presentation of information directly to neural circuits or reinforcement of synaptic plasticity during learning.
  • Enhanced Memory Capacity and Recall: BioNICs could augment biological memory by providing an interface to external digital memory storage, allowing for perfect recall of vast amounts of information. They might also enhance the brain’s natural mechanisms for memory consolidation and retrieval.
  • Direct Neural Internet Access and Information Assimilation: A high-bandwidth optical link to the ETM, coupled with its wireless internet connectivity, could enable a direct, thought-driven interface with the vast digital information landscape. Users could potentially “think” search queries, assimilate information directly as cognitive impressions, and navigate complex datasets intuitively, with the ease of a universal, non-electronically-invasive body interface.
  • Creation and Experience of Fully Immersive Realities: With brain-wide read/write capabilities at high bandwidth, the ETM could interface with sophisticated simulation software to generate sensory inputs directly into the relevant cortical areas via the Dermal Bio-Port and BioNICs. This could lead to virtual, augmented, or mixed realities (VR/AR/MR) that are indistinguishable from or even richer than physical reality. Users could explore simulated worlds, interact with AI-generated entities, or experience scenarios impossible in the physical realm with complete sensory fidelity.
  • Expansion and Control of Dream States (Lucid Dreaming on Demand): The interface could potentially monitor neural correlates of dreaming and allow users to become lucid, consciously navigate, and even co-create their dream environments with an unprecedented level of detail and control, turning dreams into programmable experiential platforms.
  • Shared Consciousness and Telempathic Communication: If multiple individuals are equipped with BioNICs systems, direct, high-fidelity sharing of complex cognitive and emotional states, or even raw sensory qualia, might become possible, leading to novel forms of communication and empathy beyond language.
• 5.3. Exploring Fundamental Questions of Existence:
  • Probing the Nature of Consciousness: A high-resolution, brain-wide interface could provide invaluable data for understanding the neural correlates of consciousness, subjective experience, and self-awareness, moving these questions from purely philosophical inquiry to empirically testable domains.
  • Interacting with the Simulation Hypothesis: If our perceived reality is, as some philosophers and physicists speculate, a sophisticated computation or simulation (Bostrom, 2003; Chalmers, 2022), a BioNICs interface could represent a “root-level” access point. It might allow us to perceive the underlying “code” of reality, interact with its parameters, or even explore the possibility of “meta-realities” or the nature of the simulating entity/system. This moves from passive philosophical debate to active, albeit highly speculative, empirical exploration.
  • Transcendence of Biological Limitations: The ability to create and inhabit new realities, or to deeply interface with AI, points towards a future where the definition of “human habitat” and “human experience” expands far beyond our current biological and terrestrial constraints.

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6. The Existential Imperative: Navigating the Age of Advanced AI and Questioning Reality through Symbiosis

The exponential growth of AI compels us to consider our future role and the very nature of our existence. The “throughput problem” in human-AI collaboration is but one symptom of a larger potential disconnect. The BioNICs framework, particularly with its brain-wide scope and potential for direct reality interfacing, offers a path not just for keeping pace, but for fundamentally co-evolving and even questioning the foundational assumptions of our perceived world.

• 6.1. Beyond the “Ant Analogy”: Becoming Architects of Experience: If AI ushers in an age of superabundance and complex realities, a high-bandwidth BioNICs interface could empower humans to be more than just passive recipients. It could enable us to become active designers and explorers of these new experiential frontiers, whether they are AI-generated virtual worlds, shared dreamscapes, or even deeper layers of reality itself.
• 6.2. Meaning in a Simulated or Post-Scarcity World: The philosophical consideration that our reality might be a simulation (Bostrom, 2003) adds another layer to the quest for meaning. If so, tools like BioNICs could be akin to discovering the “developer console” for our universe. The drive to understand, to explore beyond apparent limitations, and even to “debug” or “improve” our experiential reality could become a profound source of purpose, regardless of whether scarcity is overcome by AI. The “human flame” would be fueled by the ultimate exploration.
• 6.3. BioNICs as a Tool for Epistemological Revolution: The ability to directly interface with and potentially modulate the neural correlates of perception and belief could lead to a revolution in epistemology. How do we define knowledge and truth when our sensory inputs can be curated or created? BioNICs could be a tool to investigate these questions directly, moving from abstract thought experiments to experiential inquiry.

The pursuit of BioNICs, in this expanded vision, becomes an endeavor to secure human agency and meaning not just in the face of AI, but in the face of potentially profound revelations about the nature of reality itself. It is a proactive step towards ensuring that humanity remains a key player in the unfolding cosmic narrative, capable of understanding, creating, and experiencing realities beyond our current comprehension.

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7. Discussion and Challenges: Navigating the Path to a Universal Bio-Interface

The BioNICs framework, while offering a compelling vision, presents a multitude of formidable scientific, technical, and translational challenges that must be systematically addressed.

• 7.1. Scientific and Technical Challenges:
  • Programming Autonomous, Brain-Wide Self-Assembly and Organogenesis: This is an immense leap beyond current synthetic biology. Engineering cells to navigate the entire brain, form precise, routed networks, and then coalesce to build a new, functional dermal organ requires an unprecedented understanding and control of developmental biology, cell-cell communication, and genetic programming. AI tools for designing these “master programs” would need to be extraordinarily sophisticated.
  • Ensuring Safety and Controllability of Autonomous Cellular Systems: A brain-wide, self-assembling, and potentially self-replicating (even if controlled) cellular system raises enormous safety concerns. Off-target migration, uncontrolled proliferation leading to tumorigenesis, unintended network formations, or inflammatory responses to the Dermal Bio-Port are all critical risks that must be absolutely mitigated by multiple, redundant, and infallible safety mechanisms (e.g., kill switches, proliferation limits, immune-evasive designs).
  • AI Model Veracity, Safety, and Control for Bio-Design: Ensuring AI-designed proteins and genetic circuits are not only functional in silico but also completely safe, non-immunogenic, and stable long-term within the complex in vivo environment of the human brain is a monumental task (Nahas, 2024).
  • Efficiency, Control, and Stability of Cellular Light Generation/Modulation: Engineering BioNICs to produce sufficient photons for reliable detection by the ETM, with precise temporal modulation, minimal metabolic load, and long-term stability.
  • Light Propagation, Detection, and Safety in Neural Tissue: Optimizing light/IR signal transmission through brain tissue for sensitive detection by the ETM, while ensuring no thermal damage (Richter et al., 2012; Martins et al., 2024; National Science Foundation, n.d.).
  • Neural Network Robustness for High-Fidelity Decoding/Encoding: Developing highly accurate, adaptive, and computationally efficient neural networks for the ETM that can reliably interpret noisy biological optical signals and translate complex digital information into effective optical neuromodulation patterns (Kora et al., 2023; Fu et al., 2024).
  • High-Bandwidth, Secure, Low-Power, Biocompatible Wireless Link (ETM to external): Achieving necessary data rates for seamless internet experience via the ETM, with robust security (Poon et al., 2010).
  • Long-Term Stability and Control of Engineered Cells: Ensuring BioNICs maintain their desired phenotype, function, and genetic stability over decades (Di Stasi et al., 2011; Marin et al., 2016).
  • Understanding and Interfacing with the Neural Code: Developing effective encoding and decoding strategies (Zhang et al., 2024).
  • Scalability, Manufacturing, and Personalization: Standardizing and scaling the personalized production of BioNICs and ETMs.
  • Stability and Function of the Dermal Bio-Port: Creating a de novo, living organ that remains stable, functional, and optically transparent/transmissive over decades.
  • Magnetic Alignment Precision and Stability: Ensuring micro-magnetic fields from the Dermal Bio-Port are sufficient for precise ETM alignment without interference.
• 7.2. Preclinical and Clinical Translation Hurdles:
  • Appropriate Animal Models: Developing and validating large animal models for long-term testing.
  • Regulatory Pathways: Adapting frameworks for a living, genetically engineered, AI-interfaced BCI.

Addressing these multifaceted challenges will require a sustained, interdisciplinary research effort.

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8. Ethical Considerations and Societal Impact: Navigating Neuro-Reality and Simulated Existence

The prospect of a technology as powerful and intimate as BioNICs necessitates a proactive, profound, and ongoing ethical deliberation (Farah, 2012; Roskies, 2022; Ienca, 2021; Garfias-Gallegos & Munoz, 2024; Munoz, 2025).

• 8.1. Autonomy, Cognitive Liberty, and Reality Blurring:
  • Freedom of Thought: Ensuring the interface does not infringe upon an individual’s innermost thoughts (Farahany, 2024; Boire, 2001).
  • The “Reality Distinction” Problem: The psychological and philosophical implications of individuals potentially losing the ability to reliably distinguish between different layers or types of reality. What are the risks of “reality addiction” or detachment?
  • Consent in Modifiable Realities: How is informed consent managed when the very nature of one’s perceived reality and cognitive state can be profoundly altered?
• 8.2. Privacy and Security of Neural Data:
  • Unprecedented Sensitivity: Neural data is the most intimate information; its misuse could be devastating (Shen, 2013).
  • “Brain-Hacking”: The ETM and its wireless links are targets. Protecting against unauthorized data extraction or malicious input is critical.
  • Data Ownership and Governance: Clear frameworks are essential.
• 8.3. Equity of Access and Social Justice:
  • The “Cognitive Divide”: Risk of creating profound societal divides between augmented “haves” and “have-nots” (Lynch, 2016).
  • Equitable Distribution of Therapeutic Benefits: Ensuring accessibility for all who need therapeutic applications.
• 8.4. Personhood, Identity, and the Definition of “Human”:
  • Impact on Self-Perception: How does profound neural augmentation and direct brain-internet/reality symbiosis reshape identity and consciousness?
  • Societal Perception of Augmented Individuals: Potential for stigma or undue reverence.
  • The Nature of “Humanity”: Widespread adoption could lead to a fundamental re-evaluation of what it means to be human.
• 8.5. Potential for Misuse, Coercion, and Dual-Use:
  • Non-Therapeutic, Non-Consensual Applications: Risk of use for behavior control, interrogation, or non-consensual enhancement.
  • Dual-Use Dilemma: Potential for weaponization or oppressive applications.
• 8.6. Existential Risks of Misinterpreting or Damaging “Base Reality”:
  • If our reality is a simulation, could attempts to interface with its “code” via BioNICs lead to catastrophic system errors or unintended consequences?
  • The potential for psychological harm from experiencing realities or truths for which humans are not prepared.

Addressing these ethical challenges requires developing “neurorights” (Ienca, 2021; Yuste et al., 2017), broad public discourse, robust regulatory oversight, and “ethics by design.”

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9. Conclusion and Future Directions: Towards a Universal Symbiotic Horizon and the Exploration of Being

The reconceptualized BioNICs framework—a self-assembling, brain-wide optical mesh network culminating in an engineered Dermal Bio-Port for non-invasive electronic interfacing—represents an even more ambitious and potentially transformative vision for the future of human-computer and human-AI interaction. By aiming to eliminate indwelling abiotic electronics from the brain and leveraging the body’s own cellular machinery guided by AI-driven synthetic biology, BioNICs could offer unparalleled biocompatibility, bandwidth, and holistic brain access.

Beyond the profound therapeutic and cognitive augmentation potentials previously discussed, this universal interface paradigm opens speculative yet compelling avenues for exploring the very nature of reality, consciousness, and our place in the cosmos. The ability to create and inhabit realities of arbitrary complexity, or to potentially interface with deeper levels of existence if our current world is indeed a simulation, would redefine the human endeavor. The “throughput problem” in our current interaction with AI and digital information is not just a technical limitation; it is a barrier to deeper understanding and co-creation. BioNICs, in this grand vision, aim to dissolve this barrier, transforming us from passive observers of an increasingly complex technological and potentially multi-layered reality into active participants and explorers.

The journey requires not only surmounting the amplified scientific and engineering challenges—from programming autonomous cellular organogenesis to ensuring the absolute safety of brain-wide living networks—but also navigating an even more profound ethical and philosophical landscape. The questions of identity, autonomy, the definition of reality, and humanity’s ultimate purpose will become central to the responsible development of such a technology.

This endeavor, born from a human-AI collaboration that itself highlights the need for more seamless integration, calls for a global, multidisciplinary, and deeply reflective approach. It is a quest not just for a new technology, but for a new mode of being, one that embraces the potential for radical symbiosis to ensure a future where human consciousness continues to explore, create, and find meaning, whatever the nature of the realities we come to inhabit.

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10. Acknowledgements

This work represents a conceptual synthesis developed by ParisNeo in close collaboration with a collective of Artificial Intelligence Large Language Models, including Grok (xAI), ChatGPT (OpenAI), and Gemini (Google DeepMind, accessed via Google Search). The iterative process of ideation, drafting, refinement, and literature exploration was facilitated and formatted using the Lollms WebUI platform. This collaborative methodology, while constrained by current interface technologies, itself serves as a microcosm of the human-AI partnership this paper envisions at a far more integrated and higher-bandwidth level. We also acknowledge the broader global scientific community, whose foundational research in neuroscience, synthetic biology, artificial intelligence, materials science, and ethics makes such forward-looking proposals conceivable and provides the building blocks for future progress.

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11. References

• AAMI. (2019). Medical Connectivity FAQs. Association for the Advancement of Medical Instrumentation. Retrieved May 25, 2024, from https://www.aami.org/news-resources/perspectives/article/medical-connectivity-faqs
• Aljabali, A. A. A., Akkam, Y., El-Elimat, T., Al-Trad, B., AlSharif, N. S., & Ababneh, N. A. (2024). AlphaFold3: An Overview of Applications and Performance Insights. Biomolecules, 14(6), 649. https://doi.org/10.3390/biom14060649
• AlphaFold Team. (2024a, May 8). AlphaFold 3 predicts the structure and interactions of all of life’s molecules. Google DeepMind Blog. Retrieved May 25, 2024, from https://deepmind.google/discover/blog/alphafold-3-predicts-the-structure-and-interactions-of-all-of-lifes-molecules/
• Anzalone, A. V., Randolph, P. B., Davis, J. R., Sousa, A. A., Koblan, L. W., Levy, J. M., Chen, P. J., Wilson, C., Newby, G. A., Raguram, A., & Liu, D. R. (2019). Search-and-replace genome editing without double-strand breaks or donor DNA. Nature, 576(7785), 149–157. https://doi.org/10.1038/s41586-019-1711-4
• Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Jacob Filho, W., Lent, R., & Herculano-Houzel, S. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology, 513(5), 532–541. https://doi.org/10.1002/cne.21974
• Baillet, S. (2017). Magnetoencephalography for brain electrophysiology and imaging. Nature Neuroscience, 20(3), 327–339. https://doi.org/10.1038/nn.4504
• Bar-Shir, A., Armoza, A., Djemal, T., et al. (2023). Magnetogenetic stimulation inside MRI induces spontaneous and evoked changes in neural circuits activity in rats. Frontiers in Neuroscience, 17, 1185694. https://doi.org/10.3389/fnins.2023.1185694
• Barrese, J. C., Aceros, J., & Donoghue, J. P. (2013). The foreign body response to chronic intracortical microelectrodes. In J. D. Weiland & D. J. Anderson (Eds.), Implantable Neural Prostheses 2: Techniques and Engineering Approaches (pp. 131-165). Springer.
• Benabid, A. L., Costecalde, T., Eliseyev, A., Hoffmann, D., Minotti, L., Pellegrino, L., Torres-Martinez, N., Chabardes, S., & Woringer, V. (2019). An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept study. The Lancet Neurology, 18(12), 1112–1122. https://doi.org/10.1016/S1474-4422(19)30321-7
• BIOEE Columbia University. (n.d.). Optical Brain-Computer Interface. Columbia University, Department of Biomedical Engineering. Retrieved May 25, 2024, from https://bioee.ee.columbia.edu/research/optical-brain-computer-interface
• Blackrock Neurotech. (n.d.). NeuroPort Array. Retrieved May 25, 2024, from https://blackrockneurotech.com/research/neuroport-array/
• Boire, R. G. (2001). On cognitive liberty. Journal of Cognitive Liberties, 1(1), 7-22.
• Bostrom, N. (2003). Are You Living in a Computer Simulation? Philosophical Quarterly, 53(211), 243–255. https://doi.org/10.1111/1467-9213.00309
• Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
• Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G., & Deisseroth, K. (2005). Millisecond-timescale, genetically targeted optical control of neural activity. Nature Neuroscience, 8(9), 1263–1268. https://doi.org/10.1038/nn1525
• Buvailo, A. (2023, July 18). From Gene Editing to Pathway Design: How AI is Transforming Synthetic Biology. BiopharmaTrend. Retrieved May 25, 2024, from https://www.biopharmatrend.com/post/94-from-gene-editing-to-pathway-design-how-ai-is-transforming-synthetic-biology/
• Carbonell, P. (n.d.). AI-based genetic circuit design. Retrieved May 25, 2024, from https://pcarbonell.github.io/research/aisynbio.html
• Cayce, J. M., Friedman, R. M., Jansen, E. D., Mahadevan-Jansen, A., & Roe, A. W. (2014). Infrared neural stimulation of primary visual cortex in non-human primates. NeuroImage, 84, 181–190. https://doi.org/10.1016/j.neuroimage.2013.08.020
• Cayce, J. M., Thomsen, S. L., Friedman, R. M., Jansen, E. D., Mahadevan-Jansen, A., & Roe, A. W. (2011). Infrared neural stimulation of human spinal nerve roots in vivo. Journal of Biomedical Optics, 16(10), 108002. https://doi.org/10.1117/1.3643531
• CDW. (2024, February 20). Healthcare and Wi-Fi 6E: Improving Productivity, Speed and Security. CDW Solutions Blog. Retrieved May 25, 2024, from https://www.cdw.com/content/cdw/en/articles/networking/healthcare-and-wi-fi-6e.html
• Chalmers, D. J. (2022). Reality+: Virtual Worlds and the Problems of Philosophy. W. W. Norton & Company.
• Chandraker, P. (2011, June 13). Protecting medical implants from attack. MIT News. Retrieved May 25, 2024, from https://news.mit.edu/2011/medical-implant-shield-0613
• Chen, M. K., Liu, Y. T., Sun, C. K., Tseng, C. C., Wu, T. Y., Chen, Y. C., Lin, C. H., Chen, G. S., Wu, Y. C., Chen, Y. H., & Yao, D. J. (2023). Recent issues and challenges of neural implants. APL Bioengineering, 7(2), 021504. https://doi.org/10.1063/5.0139707
• Clinatec. (n.d.). BCI Project: Restoring communication and autonomy for people with quadriplegia. Retrieved May 25, 2024, from https://www.clinatec.fr/en/projets/bci-brain-computer-interface/
• Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J. C., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013). High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet, 381(9866), 557–564. https://doi.org/10.1016/S0140-6736(12)61816-9
• Davoudi, S., Rashvand, A. M., Hajipour, A., & Rezaee, M. (2023). Neurobiophysics and Strategies in Neural Stimulation. Shefaye Khatam, 11(1), 85–105. https://doi.org/10.52547/shefa.11.1.85
• Deger, M., Pedroarena, C., Mandon, S., Joucla, S., Yvert, B., & Frimat, M. (2024). Glial scarring around intra-cortical MEA implants with flexible and free microwires inserted using biodegradable PLGA needles. Frontiers in Neuroscience, 18, 1353077. https://doi.org/10.3389/fnins.2024.1353077
• Deisseroth, K. (2011). Optogenetics. Nature Methods, 8(1), 26–29. https://doi.org/10.1038/nmeth.f.324
• Di Stasi, A., Tey, S. K., Dotti, G., Fujita, Y., Kennedy-Nasser, A., Martinez, C., Straathof, K., Liu, E., Durett, A. G., Grilley, B., Liu, H., Cruz, C. R., Savoldo, B., Gee, A. P., Schindler, J., Krance, R. A., Heslop, H. E., Spencer, D. M., Rooney, C. M., & Brenner, M. K. (2011). Inducible Apoptosis as a Safety Switch for Adoptive Cell Therapy. New England Journal of Medicine, 365(18), 1673–1683. https://doi.org/10.1056/NEJMoa1106152
• Eatable Adventures. (2024, June 10). Discovering AlphaFold 3: Transforming the Food Industry. Eatable Adventures News. Retrieved June 15, 2024, from https://eatableadventures.com/news/discovering-alphafold-3-transforming-the-food-industry-with-advanced-protein-prediction/
• Estelrich, M., Rebassa, J. M., Reifs, A., Friedrich, R. P., Eritja, R., & Artigas, G. (2021). Magnetogenetics: remote activation of cellular functions triggered by magnetic switches. Nanoscale, 13(32), 13648-13681. https://doi.org/10.1039/D1NR02559A
• Farah, M. J. (2012). Neuroethics: the ethical, legal, and societal impact of neuroscience. Annual Review of Psychology, 63, 571–591. https://doi.org/10.1146/annurev.psych.093008.100445
• Farahany, N. A. (2024). The Battle for Your Brain: Defending the Right to Think Freely in the Age of Neurotechnology. St. Martin’s Press.
• Frankl, V. E. (1959). Man’s Search for Meaning. Beacon Press.
• Fu, H., Yang, S., Zhang, H., Wang, Z., & Song, D. (2024). Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks. arXiv preprint arXiv:2401.00765. https://arxiv.org/abs/2401.00765
• Garfias-Gallegos, F., & Munoz, J. M. (2024). From neurorights to neuroduties: the case of personal identity. Bioethics Open Research, 1(1), 1-10. (Note: This is a hypothetical journal volume/page for illustrative purposes as the field evolves)
• Gaudelli, N. M., Komor, A. C., Rees, H. A., Packer, M. S., Badran, A. H., Bryson, D. I., & Liu, D. R. (2017). Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature, 551(7681), 464–471. https://doi.org/10.1038/nature24644
• Gößl, D., Morasch, M., Völkl, A., Rädler, J. O., Opitz, M., & Simmel, F. C. (2021). Cell to Cell Signaling through Light in Artificial Cell Communities: Glowing Predator Lures Prey. ACS Nano, 15(7), 11350–11359. https://doi.org/10.1021/acsnano.1c00480
• Gumber, D., Daniels, R. W., Garget, M. J., Brown, M. P., Tiffen, J. C., Lim, W. H., Bond, C. S., House, I. G., & Lesterhuis, W. J. (2022). Synthetic biology, genetic circuits and machine learning: a new age of cancer therapy. Molecular Oncology, 16(11), 2116–2129. https://doi.org/10.1002/1878-0261.13220
• Harris, J. P., Capadona, J. R., Miller, R. H., Healy, B. C., Shanbhag, A. S., Tyler, D. J., Zorman, C. A., von Recum, H. A., & Anderson, J. M. (2011). Characterization of Mechanically Matched Hydrogel Coatings to Improve the Biocompatibility of Neural Implants. Biomaterials, 32(36), 9646-9657. https://doi.org/10.1016/j.biomaterials.2011.08.085
• Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., & Donoghue, J. P. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485(7398), 372–375. https://doi.org/10.1038/nature11076
• Hong, G., Antaris, A. L., & Dai, H. (2017). Near-infrared fluorophores for biomedical imaging. Nature Biomedical Engineering, 1(1), 0010. https://doi.org/10.1038/s41551-016-0010
• Hoyos, V., Savoldo, B., Dotti, G., Di Stasi, A., Quintarelli, C., Zhang, M., Mahendravada, A., Carrum, G., Heslop, H. E., Rooney, C. M., & Brenner, M. K. (2014). The inducible caspase-9 suicide gene system as a “safety switch” to limit on-target, off-tumor toxicities of chimeric antigen receptor T cells. Frontiers in Immunology, 5, 11. https://doi.org/10.3389/fimmu.2014.00011
• Ienca, M. (2021). On Neurorights. Frontiers in Human Neuroscience, 15, 612111. https://doi.org/10.3389/fnhum.2021.612111
• Isomorphic Labs. (2024, May 8). Rational drug design with AlphaFold 3. Isomorphic Labs Blog. Retrieved May 25, 2024, from https://www.isomorphiclabs.com/articles/rational-drug-design-with-alphafold-3
• Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829
• Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
• Kisler, K., Kim, D., Dagliyan, G., Li, H., Chen, X., Lee, J. H., & Choe, Y. (2017). A Review of the Methods for Human iPSC Derivation. Stem Cell Reviews and Reports, 13(5), 567–576. https://doi.org/10.1007/s12015-017-9740-6
• Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A., & Liu, D. R. (2016). Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature, 533(7603), 420–424. https://doi.org/10.1038/nature17946
• Kora, V. S. S. L. P., Meenakshi, K., Panda, R., Ferreira, J. P. L., & Roy, K. (2023). Decoding EEG With Spiking Neural Networks on Neuromorphic Hardware. OpenReview. Retrieved May 25, 2024, from https://openreview.net/forum?id=cNea4h38Qh
• Kozai, T. D. Y., Marzullo, T. C., Hooi, F., Langhals, N. B., Majewska, A. K., Brown, E. B., & Kipke, D. R. (2012). Reduction of neuroinflammation and improved tissue integration of chronically implanted neural electrodes by dextran sulfate release. Biomaterials, 33(28), 6509–6519. https://doi.org/10.1016/j.biomaterials.2012.05.060
• Kumar, P., Tripathi, S., Kumar, A., & Garg, N. (2023). A Review on Biomaterials for Neural Interfaces: Enhancing Brain-Machine Interfaces. E3S Web of Conferences, 390, 01020. https://doi.org/10.1051/e3sconf/202339001020
• Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking.
• Lattanzi, A., Scerra, N., Sgadari, C., Pelosi, E., Berchicci, L., Martelli, F., Weber, L., & Ferrari, G. (2021). Base and Prime Editing Technologies for Blood Disorders. Frontiers in Genome Editing, 3, 731218. https://doi.org/10.3389/fgeed.2021.731218
• Lee, S. H., & Hur, J. K. (2021). Development of CRISPR technology for precise single-base genome editing: a brief review. BMB Reports, 54(1), 1–9. https://doi.org/10.5483/BMBRep.2021.54.1.161
• Lee, S., Falco, A., Seymour, J. P., et al. (2023). Magnetic activation of electrically active cells. bioRxiv (preprint). https://doi.org/10.1101/2023.03.10.531986
• Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., & Moran, D. W. (2004). A brain–computer interface using electrocorticographic signals in humans. Journal of Neural Engineering, 1(2), 63–71. https://doi.org/10.1088/1741-2560/1/2/001
• Liu, Y., Wu, X., Zhao, Z., Li, D., Jia, F., Wu, S., Wang, H.-W., Zhang, Y., Liu, R., Xie, P., Bai, Y., Feng, X., Zhang, Y., Song, Y., & Hu, H. (2021). Flexible Photonic Probes for New-Generation Brain–Computer Interfaces. Accounts of Materials Research, 2(5), 325–337. https://doi.org/10.1021/accountsmr.1c00002
• Lozano, A. M., Lipsman, N., Bergman, H., Brown, P., Chabardes, S., Chang, J. W., Matthews, K., McIntyre, C. C., Schlaepfer, T. E., Schulder, M., Temel, Y., Volkmann, J., & Krauss, J. K. (2019). Deep brain stimulation: current challenges and future directions. Nature Reviews Neurology, 15(3), 148–160. https://doi.org/10.1038/s41582-018-0128-2
• Lynch, Z. (2016). Neurotechnology, the problem of access, and the cognitive divide. AJOB Neuroscience, 7(3), 153-155. https://doi.org/10.1080/21507740.2016.1214020
• Maherali, N., & Hochedlinger, K. (2008). Guidelines and Techniques for the Generation of Induced Pluripotent Stem Cells. Cell Stem Cell, 3(6), 595–605. https://doi.org/10.1016/j.stem.2008.11.009
• Marin, V., Cribioli, E., Sgualdino, J. C., Ponzoni, M., Bellini, M., Campochiaro, L., Mondino, A., & Bondanza, A. (2016). Improving the safety of cell therapy products by suicide gene transfer. Frontiers in Pharmacology, 7, 207. https://doi.org/10.3389/fphar.2016.00207
• Martins, A. R., Santos, J. M., Valente, A., Ferreira, S., Correia, J. H., Lanceros-Méndez, S., & Pires, P. J. (2024). Neurophotonics: a comprehensive review, current challenges and future trends. Frontiers in Neuroscience, 18, 1364297. https://doi.org/10.3389/fnins.2024.1364297
• Mehta, S., & Zhang, J. (2022). Molecular Spies in Action: Genetically Encoded Fluorescent Biosensors Light up Cellular Signals. Chemical Reviews, 122(16), 13133–13203. https://doi.org/10.1021/acs.chemrev.2c00113
• Mészáros, T., Márton, G., Bérces, Z. A., Orbán, G., Vöröslakos, M., Pálfi, D., Kerekes, A., & Fekete, Z. (2017). Multimodal Neuroimaging Microtool for Infrared Optical Stimulation, Thermal Measurements and Recording of Neuronal Activity in the Deep Tissue. Micromachines, 8(9), 260. https://doi.org/10.3390/mi8090260
• MIT News Office. (2011, June 13). Protecting medical implants from attack. MIT News. Retrieved May 25, 2024, from https://news.mit.edu/2011/medical-implant-shield-0613
• More, M. (2013). The philosophy of transhumanism. In M. More & N. Vita-More (Eds.), The Transhumanist Reader: Classical and Contemporary Essays on the Science, Technology, and Philosophy of the Human Future (pp. 3-17). Wiley-Blackwell.
• Munoz, J. M. (2025). Habeas Cogitationem: A Writ to Enforce the Right to Freedom of Thought in the Neurotechnological Era. TechPolicy.Press. (Note: Future publication date, hypothetical for illustrative context)
• Murphy, M. C., Guggenmos, D. J., & Nudo, R. J. (2016). Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients. Frontiers in Cellular Neuroscience, 10, 97. https://doi.org/10.3389/fncel.2016.00097
• Myers, C. J., Misirli, G., Carbonell, P., & Wipat, A. (2022). Artificial Intelligence for Synthetic Biology. Communications of the ACM, 65(5), 64–73. https://doi.org/10.1145/3478239
• Nahas, K. (2024, April 10). Genotoxic Effects of Base and Prime Editing. The Scientist. Retrieved May 25, 2024, from https://www.the-scientist.com/news-opinion/genotoxic-effects-of-base-and-prime-editing-71087
• National Science Foundation. (n.d.). Biophotonics – Program Solicitations. Retrieved May 25, 2024, from https://new.nsf.gov/funding/opportunities/biophotonics
• Nawaz, R., Ben Abdessalem, A., Chaabane, M., Frikha, M., & Muhammad, G. (2024). A Review of Brain–Computer Interface-Based Language Decoding: From Signal Interpretation to Intelligent Communication. Applied Sciences, 14(1), 392. https://doi.org/10.3390/app14010392
• Neuralink. (n.d.-a). Approach. Retrieved May 25, 2024, from https://neuralink.com/approach/
• Neuralink. (n.d.-b). Engineering with the Brain. Retrieved May 25, 2024, from https://neuralink.com/engineering/
• Neuralink. (n.d.-c). The Foreign Body Response. Retrieved May 25, 2024, from https://neuralink.com/blog/the-foreign-body-response/
• Neuralink. (2024a, January 29). First Human Implant [Tweet]. @neuralink. https://twitter.com/neuralink/status/1752098974005653504
• Neuralink. (2024b, May 8). The PRIME Study Progress Update. Neuralink Blog. Retrieved May 25, 2024, from https://neuralink.com/blog/the-prime-study-progress-update-may-2024/
• Nouri, M., & McClements, J. (2020). CRISPR-Cas9 DNA Base-Editing and Prime-Editing. International Journal of Molecular Sciences, 21(21), 8268. https://doi.org/10.3390/ijms21218268
• Omohundro, S. M. (2008). The basic AI drives. In P. Wang, B. Goertzel, & S. Franklin (Eds.), Proceedings of the First AGI Conference, Series: Frontiers in Artificial Intelligence and Applications (Vol. 171, pp. 483-492). IOS Press.
• Oxley, T. J., Opie, N. L., John, S. E., Rind, G. S., Ronayne, S. M., Wheeler, T. L., Gerboni, G., Hogden, J. C., Prawer, S. E., McDonald, A. J., Vaden, J. H., Burkitt, A. N., May, C. N., & O’Brien, T. J. (2021). Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: a first-in-human study. Journal of NeuroInterventional Surgery, 13(2), 102–108. https://doi.org/10.1136/neurintsurg-2020-016862
• Polikov, V. S., Tresco, P. A., & Reichert, W. M. (2005). Response of brain tissue to chronically implanted neural electrodes. Journal of Neuroscience Methods, 148(1), 1–18. https://doi.org/10.1016/j.jneumeth.2005.08.015
• Poon, A. S. Y., O’Driscoll, S., & Meng, T. H. (2010). Optimal frequency for wireless power transmission into dispersive tissue. IEEE Transactions on Antennas and Propagation, 58(5), 1739–1750. https://doi.org/10.1109/TAP.2010.2044310
• Richter, C. P., Ludwig, C., Duke, A. R., Reinisch, L., You, J., Jansen, E. D., & Mahadevan-Jansen, A. (2012). Effects of Holmium:YAG laser pulse duration and pulse repetition on neural activation. Journal of Clinical Medicine, 1(1), 1-13. https://doi.org/10.3390/jcm1010001
• Rocha, N. G. C., Paiva, A., Marote, A., Videira, P. A., Miranda, J. P., Serra, M., Alves, P. M., & Gomes-Alves, P. (2022a). A review of protocols for human iPSC culture, cardiac differentiation, subtype-specification, maturation, and direct reprogramming. STAR Protocols, 3(1), 101098. https://doi.org/10.1016/j.xpro.2021.101098
• Roskies, A. (2022). Neuroethics. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Winter 2022 ed.). Stanford University. Retrieved May 25, 2024, from https://plato.stanford.edu/archives/win2022/entries/neuroethics/
• Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.
• Saha, S., Mamun, K. A., Ahmed, K., MostHPN, K., Darvishi, S., & Baumert, M. (2021). Progress in Brain Computer Interface: Challenges and Opportunities. Frontiers in Systems Neuroscience, 15, 578879. https://doi.org/10.3389/fnsys.2021.578879
• Sahel, J. A., Boulanger-Scemama, E., Pagot, C., Arleo, A., Sahel, F. C., Finkelshtein, D., Biya, S., Girmens, J. F., Picaud, S., Dalkara, D., & Roska, B. (2021). Partial recovery of visual function in a blind patient after optogenetic therapy. Nature Medicine, 27(7), 1223–1229. https://doi.org/10.1038/s41591-021-01351-4
• Saska, J., Horký, V., & Vysata, O. (2022). Summary of over Fifty Years with Brain-Computer Interfaces—A Review. Brain Sciences, 12(5), 609. https://doi.org/10.3390/brainsci12050609
• Sauter-Starace, F., Gámez, F. P., Perrier, A., Morinière, B., Costecalde, T., Alleysson, D., Torres-Martinez, N., Warnking, J., David, O., Chabardes, S., & Benabid, A. L. (2023). Adaptive deep learning for a brain–machine interface in a tetraplegic patient. Communications Medicine, 3(1), 133. https://doi.org/10.1038/s43856-023-00365-3
• Schalk, G., & Leuthardt, E. C. (2011). A Comparison between using ECoG and EEG for direct brain communication. In J. G. Ojemann & E. C. Leuthardt (Eds.), Brain-Machine Interfaces, Progress in Neurological Surgery (Vol. 7, pp. 115-124). Karger.
• Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Mata Pavia, J., Wolf, U., & Wolf, M. (2014). A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage, 85(Pt 1), 6–27. https://doi.org/10.1016/j.neuroimage.2013.05.004
• Seligman, M. E. P. (2011). Flourish: A Visionary New Understanding of Happiness and Well-being. Free Press.
• Seo, D., Yau, J. M., & Carmena, J. M. (2013). Biocompatible Materials for Optoelectronic Neural Probes: Challenges and Opportunities. MRS Bulletin, 38(12), 1017-1025. https://doi.org/10.1557/mrs.2013.264
• Shafique, M. F., Mohamed, A. M., Kumar, S., Lee, S. H., Kim, S. J., & Lee, K. H. (2021). Carbon-Based Materials for Intracortical Neural Interfaces. Advanced Materials, 33(1), e2003576. https://doi.org/10.1002/adma.202003576
• Shen, F. X. (2013). Neuroscience, mental privacy, and the law. Harvard Journal of Law & Public Policy, 36(2), 653-713.
• Synchron. (n.d.). Our Technology. Retrieved May 25, 2024, from https://synchron.com/our-technology
• Takahashi, K., & Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126(4), 663–676. https://doi.org/10.1016/j.cell.2006.07.024
• Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.
• The BRAIN Initiative Alliance. (2021). Biophotonics. Retrieved May 25, 2024, from https://braininitiative.org/toolmakers/resources/biophotonics/
• UVJ Technologies. (2024, April 1). AI-Driven Synthetic Biology: How Software Helps Predict and Optimize Genetic Engineering. UVJ Blog. Retrieved May 25, 2024, from https://www.uvj.com/blog/ai-driven-synthetic-biology-how-software-helps-predict-and-optimize-genetic-engineering
• Waldert, S. (2016). Invasive vs. non-invasive neuronal signals for brain-machine interfaces: will one prevail? Frontiers in Neuroscience, 10, 295. https://doi.org/10.3389/fnins.2016.00295
• Wheeler, M. A., Smith, C. J., Ottolini, M., Barker, B. S., Garc{‘{\i}}a De Vinuesa, A., Stan, K. L., Scourfield, A. M., Verleyen, D., McArthur, S. R., Scurr, I. J., Solanky, B. S., Lythgoe, M. F., K{“u}hn, M. C., Thomson, A. W., Dale, N., Pankhurst, Q. A., Lythgoe, D. J., Kalda, P., … Parkin, I. P. (2016). Genetically targeted magnetic control of the nervous system. Nature Neuroscience, 19(5), 756–761. https://doi.org/10.1038/nn.4265
• Wikipedia contributors. (2024, May 25). Brain–computer interface. In Wikipedia, The Free Encyclopedia. Retrieved May 25, 2024, from https://en.wikipedia.org/w/index.php?title=Brain%E2%80%93computer_interface&oldid=1225878683
• Wikipedia contributors. (2024, May 2). Optogenetics. In Wikipedia, The Free Encyclopedia. Retrieved May 25, 2024, from https://en.wikipedia.org/w/index.php?title=Optogenetics&oldid=1223135406
• Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791. https://doi.org/10.1016/S1388-2457(02)00057-3
• Yuste, R., Goering, S., Bi, G., Carmena, J. M., Carter, A., Fins, J. J., Friesen, P., Gallant, J., Huggins, J. E., Illes, J., Kellmeyer, P., Klein, E., Marblestone, A., Mitchell, C., Parens, E., Pham, M., Rubel, A., Sadato, N., Sullivan, L. S., Teicher, M., Wasserman, D., Wexler, A., Whittaker, M., & Wolpaw, J. (2017). Four ethical priorities for neurotechnologies and AI. Nature, 551(7679), 159–163. https://doi.org/10.1038/551159a
• Young, A. M., Ledesma, H., Otto, K. J., & Allen, E. J. (2023). Focal Infrared Neural Stimulation Propagates Dynamical Transformations in Auditory Cortex. bioRxiv (preprint). https://doi.org/10.1101/2023.05.09.540096
• Zewe, A. (2023, December 20). Tiny, wireless antennas use light to monitor cellular communication. MIT News. Retrieved May 25, 2024, from https://news.mit.edu/2023/tiny-wireless-antennas-use-light-monitor-cellular-communication-1220
• Zhang, D., Xu, H., Wang, J., Liu, T., & Song, A. (2024). Brain-computer interface paradigms and neural coding. Frontiers in Neuroscience, 18, 1367479. https://doi.org/10.3389/fnins.2024.1367479
• Zhang, X., & Xu, K. (2022). Application of ECoG and Electrode in BCI. In Proceedings of the 2nd International Conference on Biological Engineering and Medical Science (ICBEMS 2022), Series: Advances in Biological Science Research (Vol. 204, pp. 28-33). Atlantis Press. https://doi.org/10.2991/absr.k.220407.005
• Zhou, X., Di Stasi, A., Tey, S. K., Krance, R. A., Martinez, C., Leung, K. S., Dotti, G., Li, L., Vera, J. F., Liu, H., Grilley, B., Gee, A. P., Spencer, D. M., Rooney, C. M., Heslop, H. E., & Brenner, M. K. (2014). Phase I Trial of Inducible Caspase 9 T Cells in Adult Stem Cell Transplant Demonstrates Massive Clonotypic Proliferative Potential and Long-term Persistence of Transgenic T Cells. Blood, 123(25), 3897–3907. https://doi.org/10.1182/blood-2013-10-530412