人体中的生物传感器通信

Great! I will conduct an in-depth investigation into research on the human body's intrinsic communication system, the human meridian system (from both biomedical and alternative medicine perspectives), and the simulation of neurons in the human body.

The research will include:

• Studies on bioelectrical communication, neural signal transmission, and intrinsic body communication pathways.
• Scientific and traditional perspectives on the meridian system, including research on acupuncture, fascia networks, and bioelectric fields.
• Advances in neuron simulation, including computational neuroscience models, artificial neural networks, and bioelectronic applications.
• Both theoretical and applied research, with a greater emphasis on medical applications such as neurostimulation and bioelectronics.
• A mix of recent studies (last 5-10 years) and foundational research to provide historical context.

I'll gather relevant insights and provide a structured summary soon.

Human Intrinsic Communication System

Bioelectrical Signaling in the Body: The human body relies on bioelectrical impulses for rapid internal communication. Neurons generate action potentials – electrical spikes produced by the movement of ions (Na⁺, K⁺, Ca²⁺) across their membranes – as described in classic experiments by Hodgkin and Huxley. These impulses propagate along nerve fibers at high speeds, allowing the brain and body to coordinate functions in real time. Neurons connect at synapses where signals are passed chemically via neurotransmitters or electrically via gap junctions. Electrical synapses (gap junctions) provide direct ionic current flow between cells, enabling nearly instantaneous, bidirectional communication. Such gap junction coupling is vital in the heart (synchronizing cardiac muscle contraction) and in certain brain circuits, illustrating that electrical transmission complements the more common chemical synaptic transmission in our nervous system.

Internal Coupling and Energy Transmission: In addition to well-defined neural pathways, research shows the body may communicate through more diffuse electrical and energetic means. Neurons and other cells can influence each other via local electric fields even without direct contact – a phenomenon known as ephaptic coupling. Furthermore, the whole body forms a volume conductor for electrical currents: for example, the heart’s electrical activity spreads through tissues and can be recorded as an ECG at the skin. The body also generates electromagnetic fields from the movement of charged particles, and it can both radiate and absorb electromagnetic radiation. This has led to the concept of a biofield – a purported endogenous energy field. Though not yet fully quantified, some researchers suggest the biofield could serve as an informational medium, aligning with traditional notions of a life-force (“Qi”). One intriguing line of inquiry is ultra-weak photon emission: living cells emit faint biophotons, and these light signals have been proposed as a form of cell-to-cell communication. Studies indicate neurons can continuously emit biophotons and that brain electrical activity may modulate this emission, hinting that biophotons could carry information within the body. While such biofield interactions are still being explored, they underscore the multi-layered nature of human intrinsic communication – from ionic currents and chemical signals to electromagnetic and possibly photonic cues.

Body-Coupled Communication (Galvanic Coupling): The conductive properties of bodily tissues can be harnessed to transmit signals for medical and technological applications. Intra-body communication (IBC) is a technique that uses the human body itself as a wire-like channel for data transmission. In galvanic coupling IBC, a low-voltage electrical signal is injected into the body through a pair of electrodes, establishing an electric field through the tissues; the signal is then picked up by receiver electrodes elsewhere on the body. This method can securely carry information around or through the body (for example, connecting wearable or implanted devices) with high signal quality and minimal radiation outside the body. Such body-coupled communication has been proposed for wireless body-area networks that link sensors or therapeutic devices on a person. Notably, because the transmission stays mostly within the body’s conductive medium, it is less susceptible to external interference and more secure than radio-wave transmission. Galvanic coupling and related intra-body signaling techniques exemplify how understanding the body’s intrinsic electrical pathways enables new technologies for health monitoring and device networking.

Biomedical Applications (Bioelectronic Medicine and Neurostimulation): Knowledge of the body’s electrical communication underpins a revolution in medicine – often termed bioelectronic medicine or electroceuticals. This approach treats disorders by modulating neural signals instead of (or in addition to) drugs. For example, implanted vagus nerve stimulators send electrical pulses into a major nerve to adjust immune responses in inflammatory diseases and improve organ function. Pacemakers and defibrillators correct aberrant electrical rhythms in the heart, and deep brain stimulators deliver targeted pulses in the brain to relieve Parkinson’s disease tremors – all illustrations of therapeutic neurostimulation. The principle of these therapies is that neurons act as transducers, converting electrical stimuli from a device into biochemical changes in organs, thereby restoring healthy control signals. Because the peripheral nervous system reaches all organs, it provides a rich interface to treat many conditions by electrical means. Advances in materials and miniaturized electronics now allow for electrodes that can be placed on peripheral nerves or even within the brain with precision. Each bioelectronic therapy system comprises sensors (to read bodily signals), stimulators (to send new signals), power sources, and biocompatible packaging. All these must work seamlessly to adjust the body’s intrinsic communication patterns. Emerging research emphasizes rigorous testing and computational modeling of such systems, which helps in understanding and predicting their effects. By leveraging the body’s own signaling networks, bioelectronic medicine offers targeted interventions – for example, electrical stimulation of the vagus nerve to reduce epileptic seizures or treat depression – that can complement or replace pharmaceuticals. This fusion of engineering with physiology represents a direct practical application of human intrinsic communication pathways to healing.

Human Meridian System

Traditional Chinese Medicine Perspective: In Traditional Chinese Medicine (TCM), the body’s vitality is governed by an energy called Qi that flows through an interconnected network of channels known as meridians. There are 12 primary meridians corresponding to organ systems (Lung, Heart, Kidney, etc., plus additional vessels), forming a meridian map that covers the entire body (front and back) as depicted below. Acupuncture meridians are conceived as longitudinal pathways (often illustrated on anatomical charts) through which Qi circulates to maintain balance and health. Acupuncture and acupressure practices target specific points (acupoints) along these meridians to influence the flow of Qi and thereby affect organ function and pain pathways. TCM texts describe meridians in functional and energetic terms – for example, blockages or imbalances of Qi in a certain meridian might manifest as pain or illness in that meridian’s associated organ or region. While these concepts arose from empirical observations thousands of years ago, they provide a holistic framework: the body is seen as an integrated system of energy circulation. Practices like acupuncture, Qigong, and herbal medicine aim to harmonize Qi flow in meridians. In modern terms, Qi is sometimes described as a “vital force” or even loosely equated to neurohumoral regulation, but it also aligns with the notion of an intrinsic biofield. Notably, external Qi Gong healers and Reiki practitioners claim to affect a patient’s Qi or biofield without touch, suggesting meridians and energy can interact beyond the physical body. This traditional view, while not framed in Western scientific language, has guided acupuncture anesthesia, pain management, and wellness practices effectively, motivating researchers to search for the physiological underpinnings of the meridian system.

Traditional Chinese medicine identifies a system of meridians (energy channels) interconnecting points across the body (anterior and posterior views). These meridians, such as the lung meridian in the arm or the bladder meridian along the back, are believed to carry “Qi” and link internal organs with surface acupoints. Modern research is investigating how these meridian lines might correspond to anatomical structures like nerves or fascia.

Anatomy and Fascia Correlates: A growing body of evidence suggests that meridians may have a basis in anatomy – particularly in the connective tissue network. Fascia, the continuous web of collagenous connective tissue wrapping muscles, bones, and organs, is now thought to form low-resistance pathways through which signals (mechanical, electrical, or fluid) can propagate. A comprehensive review concluded that the body’s fascia network is the likely anatomical substrate of TCM meridians, noting that mapped meridian paths closely correspond to connective tissue planes visible in body scans. For example, the longitudinal fascial planes in the limbs and trunk align with where acupuncturists locate meridians. High-resolution imaging and dissection show that many acupuncture points lie at intersections of fascia or near neurovascular bundles. This implies that needling those points could influence deeper tissues or signaling pathways.

Physiologically, meridian pathways have been associated with distinctive electrical properties. Studies have found that skin along certain meridians exhibits lower electrical impedance (higher conductance) compared to adjacent non-meridian areas. In one controlled study, researchers measured skin impedance at locations on the arm, leg, and torso: an acupuncture line on the arm (Large Intestine meridian) showed significantly lower impedance than nearby control points. Ultrasound imaging of those sites revealed underlying collagenous bands of connective tissue, supporting the idea that conductive fluids in fascia might facilitate an electrical pathway. In contrast, no impedance difference was found on a meridian that lacked a clear fascial cleavage plane. These findings reinforce a fascia-meridian connection: the collagen fibers and ground substance of fascia could provide channels for ion flow or interstitial fluid movement, thus explaining both the classical meridian maps and their electrical characteristics. Additionally, some researchers have identified threadlike microanatomical structures (previously called Bonghan ducts or primo-vascular system) running along blood vessels and organs, which they speculate might correspond to meridians, although this remains controversial.

Empirical Studies and Acupuncture Effects: Modern techniques have visualized meridian pathways in vivo. In a landmark 2021 study, scientists injected fluorescent dye into acupuncture points and observed its migration through tissue in real time. Remarkably, when dye was introduced at a Pericardium meridian point on the forearm (acupoint PC6), it slowly traveled upward under the skin, tracing a line closely matching the documented path of that meridian and eventually pooling at a point near the elbow (PC3). Control injections at non-meridian points did not produce any such directed pathway. Ultrasound and vein imaging confirmed that the dye’s path was not following a known vein or artery, but rather an intermuscular fascia plane. This provides direct visual evidence of a distinguishable conduit in human tissue that coincides with a meridian pathway. Such findings echo earlier radioactive tracer studies in animals, which showed migration of isotopes along meridian lines independent of blood or lymph vessels. Another measurable effect of stimulating meridians comes from functional brain imaging: fMRI studies have shown that acupuncture can modulate activity in specific brain regions related to the acupuncture point’s traditional function (for instance, stimulating a vision-related acupoint can selectively activate occipital lobe regions). These neuroimaging results imply that meridian points have reproducible effects on the central nervous system. Moreover, clinical trials have recorded changes in levels of neurotransmitters, endogenous opioids, and inflammatory markers in response to acupuncture, offering biochemical evidence of systemic effects.

From a biophysical perspective, acupuncture needle insertion generates local signals (stretching connective tissue, triggering sensory nerves) that propagate far beyond the needle site. The “fascia pull” from twirling a needle may send a mechanotransductive signal along the connective tissue plane, much like tugging on a taut fabric. This could explain, for example, why an acupuncture needle in the foot (along the liver meridian) might affect tissue tension or organ function in the abdomen. Some studies also report changes in electrodermal activity and skin conductance at distal acupuncture points during treatment, supporting the idea of an interconnected electrical network. While mainstream science does not fully endorse the classical meridian model, the convergence of fascial anatomy, bioelectric measurements, and tracer flow studies is gradually bridging TCM theory with physiology. Meridians may well represent functional channels in the neuro-fascial network through which various forms of communication (electrical impulses, cellular signals, or even pressure waves) can travel.

Integration of Meridian Concepts in Medicine: The pursuit of a scientific basis for meridians has practical motivations, as acupuncture is widely used for pain management, neurological conditions, and more. If meridian pathways correspond to real anatomical or functional networks, they could be leveraged in novel therapies. For instance, understanding that a chain of connective tissue links an ankle acupoint to neck muscles (as the Superficial Back Line/Bladder meridian suggests) can inform physiotherapists and surgeons about distant effects of local interventions. Some contemporary medical approaches, like fascial manipulation and dry needling, intersect with acupuncture by targeting myofascial trigger points that often lie on or near traditional meridians. Moreover, the concept of a body-wide integrative network resonates with systems biology and holism in medicine. Researchers are also exploring if the meridian system relates to embryological development or tissue organization – for example, electrical gradients during development might “pre-pattern” preferred pathways (a hypothesis in line with developmental bioelectricity studies). While meridians are still sometimes viewed as “pseudoscientific” in strict biomedical circles, the accumulated evidence of fascial connections, distinct electrical properties, and measurable physiological responses keeps the dialogue open. In summary, the human meridian system, as understood in TCM, appears to map onto a composite of anatomical structures (nerves, vessels, fascia) and functional pathways. Ongoing research aims to fully decipher these links – validating ancient insights with modern science – so that acupuncture and related therapies can be optimized and integrated into evidence-based medical practice.

Simulation of Neurons in the Human Body

Computational Models of Neurons (Past and Present): Neuroscience has a rich history of modeling how neurons behave, dating back to the pioneering mathematical model by Hodgkin and Huxley in 1952. Their quantitative model of the squid giant axon – expressed as differential equations for ion channel kinetics – successfully reproduces the generation of action potentials, establishing the foundation for computational neuroscience. Since then, many neuron models have been developed, from simple integrate-and-fire neurons (which approximate the neuron as a leaky capacitor that spikes when threshold is reached) to detailed multi-compartment models that simulate dendrites, axons, and thousands of ion channels. Early artificial neurons (e.g. McCulloch-Pitts binary neurons and Rosenblatt’s perceptron in the 1940s–50s) were vastly simplified abstractions inspired by biological neurons. They led to the field of artificial neural networks (ANNs), which attempt to mimic brain information processing in a simplified form. Modern artificial intelligence still largely relies on a basic neuron model – essentially a weighted sum and threshold activation – first conceived in the mid-20th century. However, actual biological neurons are far more complex: they exhibit dendritic computations, feedback connections, and non-linear dynamics that simple ANN nodes do not capture. Researchers today are revisiting neuron models to incorporate more biological realism in simulations. For example, a 2024 study proposed a new computational model treating neurons as adaptive controllers rather than passive summators, meaning a neuron can modulate how it receives input based on its own activity and context. This model allows simulated neurons to feed back and influence earlier network layers, more akin to how real neural circuits work (where recurrent connections and modulatory feedback are abundant). By accounting for features like dendritic segregation of signals and activity-dependent plasticity, such advanced models aim to narrow the gap between in silico neurons and their biological counterparts. The field has also seen the rise of large-scale brain simulations – notably the Blue Brain/Human Brain Project, which seeks to digitally reconstruct cortical microcircuits in detail. “Simulation neuroscience” has emerged as a discipline focused on building comprehensive digital brains. These efforts leverage supercomputers to solve equations for millions of coupled neurons and synapses, enabling experiments that probe emergent behavior (e.g. how oscillations or memory might arise from network structure). While full brain simulation at the neuron level remains computationally intense, progress in this area is accelerating, offering a powerful complementary approach to empirical neuroscience.

An abstract illustration symbolizing the convergence of biological and artificial neurons: a human hand and a digital hand drawing each other. Advances in computational neuroscience increasingly inform AI (and vice versa), as new neuron models inspired by biology can improve artificial neural networks. Likewise, machine learning helps neuroscientists simulate and understand complex neural processes.

Artificial Neural Networks and Biological Simulation: The intersection of AI and neuroscience is a hotbed of innovation. Traditional AI neural networks were inspired by a crude analogy to brain neurons; now the flow of inspiration is reversing, with AI research drawing from neuroscientific insights. The 2024 model mentioned above is one example where incorporating biological neuron features could make AI networks more powerful. Conversely, neuroscientists use artificial neural networks as computational tools to simulate cognitive functions and even to emulate parts of the brain for hypothesis testing. Deep learning networks have been used to model visual processing, yielding predictions about how real neurons respond to images. There is also growing interest in spiking neural networks (SNNs), which simulate neurons that communicate via discrete spikes rather than continuous outputs, much closer to real brain dynamics. SNNs and neuromorphic chips (hardware modeled on neural architectures) allow researchers to simulate brain-like computation with energy-efficient, event-driven processing. Another frontier is brain-machine hybrids: for instance, using living neural cultures interfaced with software (“wetware” + hardware) to study learning and memory. As our computational models become more realistic, we start to tackle phenomena like consciousness, attention, and plasticity in silico. This has practical payoff in medicine – e.g., realistic models of neural circuits can help decode brain signals for brain-machine interfaces, or predict how a drug or stimulation might affect neural activity. In summary, the synergy between artificial networks and biological simulation is creating a virtuous cycle: better brain models lead to better AI, and AI tools enable deeper simulations of brain function.

Neuroprosthetics and Brain–Machine Interfaces: One of the most direct applications of neuron simulation is in the design of neuroprosthetics – devices that replace or augment nervous system functions. Neuroprosthetics range from cochlear implants that restore hearing, to artificial limbs controlled by brain signals, to electrodes that stimulate the spinal cord to enable paralyzed patients to walk. Designing such devices requires understanding and predicting how real neurons will respond to stimulation or generate signals. Computational modeling has become a crucial aide in this process, allowing researchers to test ideas in silico before in vivo trials. For example, finite element models of peripheral nerves can simulate how electrical currents spread from an electrode through tissue and recruit nerve fibers. By modeling the electrode geometry, pulse waveform, and nerve fiber properties, engineers can optimize electrode designs and stimulation parameters to target specific neural populations with precision. This approach was highlighted in a 2024 review, which noted that modeling and simulation save time and cost in neuroprosthesis development by quantitatively assessing targeting efficiency and guiding design choices. As a result, we see faster iteration of innovations like high-density electrode arrays, novel stimulation patterns (e.g. kilohertz-frequency alternating current for blocking pain signals), and closed-loop controllers that adjust stimulation based on feedback.

Brain–machine interfaces (BMIs), which decode neural activity to drive external devices or encode sensory information back into the brain, also benefit enormously from computational models. Researchers have created models of how motor cortex neurons fire in relation to intended movement, which then inform machine learning algorithms that translate brain signals into prosthetic arm movements. Likewise, to provide sensory feedback via a prosthetic, models of somatosensory neurons help determine what patterns of electrical stimulation would feel “natural” to the user. An emerging trend is virtual patients – detailed simulations of a patient’s neural pathways used to personalize neurostimulation therapy. For instance, before implanting a spinal cord stimulator for pain relief, a patient-specific model can be run (using MRI of the spine and known neuron properties) to predict which electrode configuration will best cover the painful area. The same goes for deep brain stimulation: patient brain models can estimate how stimulation will spread and what structures will be affected, minimizing side effects. Thus, neuron and network simulations act as a design and calibration sandbox for neuroengineers, making brain–machine technologies safer and more effective.

Neurostimulation Modeling Tools (NRV Framework): To support these efforts, researchers have developed sophisticated software platforms for simulating neurons and their interaction with electrical devices. One recent example is the NRV framework, an open-source simulation toolkit tailored for peripheral nerve stimulation research. NRV (an acronym derived from “nerve simulation”) provides a high-level Python-based environment to model nerves, electrodes, and stimulation protocols in silico. It is object-oriented, mapping physiological elements (like nerve fibers, fascicles, tissue media) and electrical components (currents, voltages) into a unified simulation space. With NRV, users can perform multi-scale simulations – from a single neuron’s membrane dynamics up to an entire nerve trunk with thousands of fibers. Complex stimulation scenarios can be tested, such as using multiple electrodes simultaneously or novel waveform shapes (biphasic pulses, high-frequency bursts, etc.), to see how they recruit nerve activity. Impressively, the framework supports automated optimization, allowing researchers to let the software find the best stimulation parameters for a given goal (for example, maximize activation of pain-suppressing fibers while minimizing activation of motor fibers). The developers validated NRV by comparing its simulation outputs to published experimental results, ensuring its accuracy. Such tools dramatically lower the barrier to entry for biomedical engineers and neuroscientists working on electroceuticals: instead of needing multiple software packages (for tissue EM fields, neuron excitability, etc.), NRV offers a one-stop, user-friendly platform. Beyond NRV, there are other well-known neuron simulators like NEURON and Brian for detailed brain circuit modeling, and OpenEMS or COMSOL for bioelectric field modeling – but the trend is toward integrating these capabilities. By uniting biophysics and engineering, simulation frameworks accelerate the development of therapies like vagus nerve stimulation for inflammatory diseases or closed-loop cortical stimulators for epilepsy. They enable rapid prototyping of “virtual electrodes” and “virtual surgeries,” refining approaches before applying them to living subjects. In essence, neuron simulation tools are now indispensable in translating bioelectrical science into real-world medical innovations. Together with advances in imaging and data science, they are helping fulfill the promise of precision neuromodulation – delivering the right electrical message to the right neurons to heal the body.

Conclusion: The three topics explored – intrinsic bioelectrical communication, the meridian system, and neural simulations – are deeply interrelated by the theme of connectivity in the human body. Our physiology operates through multiscale communication networks: from ions crossing a channel pore to global electromagnetic oscillations, from ancient maps of Qi flow to high-tech neural interfaces. Modern science is gradually validating and leveraging these communication pathways. Bioelectrical medicine taps into neural circuits to treat disease, just as acupuncture has aimed to rebalance the body’s energy along meridians. Meanwhile, computational models and simulations bridge understanding between the qualitative insights of traditional medicine and the quantitative rigor of neuroscience and engineering. By integrating knowledge from Eastern and Western perspectives and old and new research, we move toward a more holistic understanding of the human body’s communication systems – ultimately improving our ability to heal, connect with, and even emulate the remarkable network that is the human organism.

Sources:

1. Levin M. (2012). Electrical signals in development and regeneration: bioelectricity as a morphogenetic regulator. (Discusses bioelectric signaling, gap junctions, and ephaptic coupling in neural networks)
2. Shang et al. (2012). A finite-element simulation of galvanic coupled intra-body communication. (Defines intra-body communication using galvanic coupling and its advantages for body-area networks)
3. Muehsam et al. (2022). Perspectives on measuring the human biofield. (Covers the concept of the biofield, Qi as a subtle energy, and electromagnetic aspects of human physiology)
4. Tang et al. (2014). Biophoton signal transmission and processing in the brain. (Reports that neurons emit ultra-weak photons and explores their role in cell communication, relating to meridian theories)
5. Bai et al. (2011). Evidence that fascia is the anatomical basis of acupuncture meridians. (Reviews anatomical data showing correspondence between fascia planes and meridian maps)
6. Ahn et al. (2010). Electrical impedance of acupuncture meridians and the role of subcutaneous collagenous bands. (Demonstrates lower electrical impedance at some meridian locations correlating with connective tissue)
7. Li et al. (2021). In vivo visualization of the pericardium meridian with fluorescent dyes. (Human study injecting fluorescent dye at acupoints, showing dye migrating along fascial pathways matching meridians)
8. Tom Myers (2014). Anatomy Trains – Myofascial Meridians. (Correlates acupuncture meridians with myofascial continuity lines in the body)
9. Simons Foundation News (2024). New model of real neurons could improve AI. (Describes a more biologically accurate neuron model treating neurons as adaptive controllers and its implications for AI)
10. The 2020 Roadmap for Bioelectronic Medicine (Lowe & Thakor, 2023). (Overview of bioelectronic medicine, neural interfaces, and the need for models/simulations in developing these therapies)
11. Yang et al. (2024). Neuroprosthetic electrode design optimized by simulation. (Reviews how modeling techniques assist in designing and improving neuroprosthetic devices and neural stimulation strategies)
12. Couppey et al. (2024). NRV: An open framework for in silico evaluation of peripheral nerve stimulation. (Introduces the NRV simulation tool and its capabilities for multi-scale nerve modeling and optimization)
13. Ienca et al. (2018); Fan & Markram (2019). Simulation neuroscience paradigm. (Highlights the concept of building comprehensive digital brain models as a new approach in neuroscience)