Multiscale Competency and Bioelectric Networks

• Bodies are multiscale competency architectures, with problem-solving intelligence at every level (molecular networks, organs, swarms).
• Definitive regenerative medicine requires communicating anatomical goals to cells in “morphospace” (the space of possible forms).
• Cells us a native language called morphoceuticals.
• Endogenous bioelectric networks are a tractable interface for top-down control of cell behavior. Tools can read and write pattern memories in this “protocognitive medium.”

Agential Material and Engineering Approaches

• Traditional engineering uses passive materials; regenerative medicine works with *agential* material (cells with their own goals). This is like building with dogs instead of Legos.
• Different problems require different levels of solution (e.g., orthopedic surgeon vs. psychoanalyst). Bodies exhibit a “spectrum of persuadability.”
• Cellular collectives’ position on this spectrum is unknown; experiments, not assumptions, are needed.
• Higher level processes can often supervene to fix the mistakes and defects found in lower levels of organisms, the ability to train will become useful here.

Scaling of Minds and Biological Plasticity

• Biological development is a continuous scaling of minds, from single cells to complex cognition. No “magic” moment separates physics from mind.
• Turing understood that body self-assembly and mind scaling are the same problem.
• Even gene regulatory networks exhibit basic learning; Intelligence exists pre-nervous system.
• Biological systems exhibit amazing plasticity (e.g., ectopic eyes providing vision, planarian memory regeneration, caterpillar-butterfly memory remapping).

Anatomical Morphospace and the Anatomical Compiler

• “Morphospace” is the multi-dimensional space of all possible shapes.
• The genome encodes molecular *hardware*, not the final body plan. Cells “know” what to build and when to stop.
• The “anatomical compiler” (long-term goal) would translate a desired form into stimuli to guide cells, not micromanage cell placement.
• Current limitations: We cannot predict morphology from genome sequences (e.g., frog-axolotl chimeras). We are at the “hardware” stage of biological programming (like computer science in the 1940s-50s).

Biological Software and Collective Intelligence

• “Biological software” refers to the intelligence that can be exploited, similar to software on reprogrammable hardware.
• Intelligence (William James): The ability to reach the same goal by different means (a cybernetic definition).
• Cellular swarms show collective intelligence, adapting to achieve anatomical goals despite variations (e.g., polyploid newt kidney tubules, embryo splitting, limb regeneration).
• Biological structures exhibit homeostasis which reduce error by correcting when target is deviated.

Pattern Memories and Bioelectric Control

• Developmental biology is a goal-directed process (like a thermostat) moving towards a “target morphology.” This challenges traditional “bottom-up” emergence views.
• Pattern memories are stored, not just in DNA, and can be rewritten, affecting the “target morphology” (e.g., two-headed planaria). This is analogous to brain scans (much scans).
• Bioelectric networks (ion channels, gap junctions) are similar to neural networks, but operate in anatomical morphospace. Tools have been developed to read and write these patterns.
• Early stage patterns form like “the electric face”.
• Voltage patterns are *instructive*, not just disruptive. They can induce organ formation (e.g., ectopic eyes) and reveal hidden cell competency.
• By inducing electrical patterns of potassium, gut cells were able to be converted to functioning eyes.

Applications and Future Directions

• Cells exhibit properties which make them ideal for regeneration; They know where to build, how much to build, and when to stop building.
• Bioelectric manipulations can be used for limb regeneration in frogs (non-regenerating species), and work is underway towards mammal applications (Morphoceuticals).
• Immortal planaria provide key examples where a cell can divide and regenerate a new head, or two new heads depending on biometrical patterns.
• Organisms have limits from their lineage.
• Altering bioelectric circuits in planaria can change head number/shape, demonstrating reprogrammable anatomical memory.
• Biometical networks can be rewired by manipulating existing patterns to create entirely new sturctures; oak leaves and their bio-engineers was cited as a notable exmaple.
• Computational models of bioelectric patterns are being developed, enabling rational interventions (e.g., correcting brain defects in tadpoles even with genetic mutations).
• Cancer can be seen as a failure mode of the scaling of goals, with cells reverting to individualistic behavior. Bioelectric reconnection can suppress tumor formation.

Future Medicine and Protocognitive Capacity

• Anthropods made from humans’ lungs display capacities to assist nearby, hurt tissue, they have intelligent agency despite a very primitive function.
• The capacity of human lungs goes beyond their usual purposes, highlighting the amazing intelligence available.
• Future medicine will likely resemble psychiatry more than chemistry, exploiting the protocognitive capacity of tissues (using tools inspired by neuroscience).
• Future interventions could involve “agential implants” (like anthrobots) and “morphoceuticals” targeting anatomical intelligence.
• Future medicines and the treatment should evolve.
• The speaker believes an apporach focused around sharping patterns found in memory is useful for anti-aging.
• Research needs: In vivo voltage imaging, better ion channel drugs, better physics computational models, mapping voltage states in health/disease, and exploring non-electrical signaling (mitogenic radiation, etc.).
• They believe high-level interventions through biometrical will allow the same rat training principle as rats: no micromanaging the parts (rat neurons, bio-organ electrical states), reward and punish to achieve a general behavior.

多尺度能力与生物电网络

• 生物体是多尺度能力架构，每个层次（分子网络、器官、群体）都具有解决问题的智能。
• 明确的再生医学需要在“形态空间”（可能的形态空间）中向细胞传达解剖目标。
• 细胞使用一种称为形态药物的天然语言。
• 内源性生物电网络是自上而下控制细胞行为的可处理接口。 工具可以在这种“原认知介质”中读取和写入模式记忆。

自主材料与工程方法

• 传统工程使用被动材料；再生医学使用*自主*材料（具有自身目标的细胞）。这就像用狗而不是乐高积木建造。
• 不同的问题需要不同层次的解决方案（例如，骨科医生与精神分析师）。生物体表现出“可说服性的范围”。
• 细胞群体的“可说服性范围”是未知数；我们需要实验，而不是假设。
• 较高级别的过程通常可以进行干预，修复生物体中低层级的错误和缺陷。此处“训练”将展现效用。

心智的扩展与生物可塑性

• 生物发育是心智的持续扩展，从单细胞到复杂的认知。没有“神奇”的时刻将物理学与心智分开。
• 图灵明白身体自组装和心智扩展是同一个问题。
• 即使是基因调控网络也表现出基本的学习； 智能存在于神经系统之前。
• 生物系统表现出惊人的可塑性（例如，异位眼睛提供视觉、涡虫记忆再生、毛毛虫-蝴蝶记忆重映射）。

解剖形态空间与解剖编译器

• “形态空间”是所有可能形状的多维空间。
• 基因组编码分子*硬件*，而不是最终的身体计划。 细胞“知道”要构建什么以及何时停止构建。
• “解剖编译器”（长期目标）会将所需的形式转换为指导细胞的刺激，而不是微观管理细胞放置。
• 目前的局限性：我们无法从基因组序列预测形态（例如，青蛙-蝾螈嵌合体）。 我们处于生物编程的“硬件”阶段（就像 20 世纪 40-50 年代的计算机科学）。

生物软件与集体智能

• “生物软件”指的是可以利用的智能，类似于可重新编程硬件上的软件。
• 智能（威廉·詹姆斯）：通过不同方式达到相同目标的能力（控制论定义）。
• 细胞群显示出集体智能，适应以实现解剖目标，尽管存在差异（例如，多倍体蝾螈肾小管、胚胎分裂、肢体再生）。
• 生物结构表现出稳态，当偏离目标时，可以通过纠正来减少错误。

模式记忆与生物电控制

• 发育生物学是一个目标导向的过程（像恒温器一样）朝着“目标形态”前进。 这挑战了传统的“自下而上”涌现观点。
• 模式记忆不仅存储在DNA中，还可以被重写，影响“目标形态”（例如，双头涡虫）。 这类似于大脑扫描（多次扫描）。
• 生物电网络（离子通道、间隙连接）类似于神经网络，但在解剖形态空间中运作。 已经开发出用于读取和写入这些模式的工具。
• 早期阶段的模式形成了“电面孔”。
• 电压模式具有*指导性*，而不仅仅是破坏性的。 它们可以诱导器官形成（例如，异位眼睛）并揭示隐藏的细胞能力。
• 通过诱导钾的电模式，肠道细胞能够转化为功能性眼睛。

应用与未来方向

• 细胞具有使其成为再生的理想选择的特性； 它们知道在哪里构建，构建多少以及何时停止构建。
• 生物电操作可用于青蛙（非再生物种）的肢体再生，并且正在进行哺乳动物应用（形态药物）的研究。
• 不朽的涡虫提供了关键的例子，其中细胞可以分裂并再生一个新头部，或两个新头部，这取决于生物特征模式。
• 生物体的局限来自他们的血统.
• 改变涡虫中的生物电回路可以改变头部数量/形状，证明可重新编程的解剖记忆。
• 生物医学网络可以通过操纵现有模式来重新连接以创建全新的结构； 橡树叶及其生物工程师被引用为一个著名的例子。
• 正在开发生物电模式的计算模型，从而实现合理的干预措施（例如，即使有基因突变，也能纠正蝌蚪的大脑缺陷）。
• 癌症可以被视为目标扩展的失败模式，细胞恢复到个人主义行为。 生物电重新连接可以抑制肿瘤形成。

未来医学与原认知能力

• 由人类肺部制成的人造节肢动物显示出帮助附近的受伤组织的能力，尽管功能非常原始，它们具有智能能动性。
• 人肺的功能超越其平常的目的， 突出非凡的现有智慧。
• 未来医学可能更像精神病学，而不是化学，利用组织的原认知能力（使用受神经科学启发的工具）。
• 未来的干预措施可能涉及“自主植入物”（如人造机器人）和靶向解剖智能的“形态药物”。
• 未来的药物和治疗方法应该不断发展。
• 演讲者认为，一种专注于记忆中发现的尖锐模式的方法对抗衰老是有用的。
• 研究需求：体内电压成像、更好的离子通道药物、更好的物理计算模型、绘制健康/疾病中的电压状态，以及探索非电信号（致有丝分裂辐射等）。
• 他们认为通过生物特征进行高级干预将允许与大鼠训练相同：不需要对零件（大鼠神经元、生物器官电状态）进行微观管理，通过奖惩来实现一般行为。