Introduction: Body and Brain Plasticity

• Biology has been computing at many scales long before brains. Decision-making in all body tissues is mediated by pre-neural electrical networks (not just neurons). This is an untapped frontier for new AI.
• Caterpillars retain memories through metamorphosis (radical nervous system restructuring). Planaria (flatworms) regenerate entire bodies, including brains, and retain memories after head amputation, demonstrating non-brain memory storage.

Somatic Cognition and Anatomical Self-Editing

• Cognition exists on a ladder, from simple to complex. Living creatures, even single-celled organisms (e.g., Lacrymaria), show complex behaviors, structural control, and physiology without brains.
• Multicellular bodies retain cellular decision-making. Embryos and regenerating organisms (salamanders, planaria) demonstrate large-scale anatomical control and homeostasis, even with massive perturbations (e.g., cutting embryos, grafting tails onto limbs).
• Regeneration isn’t just about starting; it’s about *stopping* at the correct anatomy. Human livers, deer antlers, and (in children) fingertips also show regeneration.
• The genome doesn’t encode rote movements. Organs can remodel to reach a “normal” morphology even from incorrect starting positions.
• DNA specifies proteins, *not* 3D shape directly. Cell groups “know” what to build and when to stop. A major knowledge gap exists in understanding this anatomical control.

Bioelectric Mechanisms and Goal-Directed Remodeling

• Patterning control is a *closed-loop* system, not open-loop. It has error detection and correction. The goal is to target the anatomical *set point* for therapeutic purposes.
• Somatic tissues form electrical networks that make decisions about *anatomy*, not behavior. These can be targeted for pattern editing.
• Neurons evolved from ancient cells already performing computations. Synaptic machinery, ion channels, and neurotransmitters predate brains. This explains why fungal compounds (e.g., hallucinogens) affect human cognition – shared ancient mechanisms.
• All cells (not just neurons) have ion channels and make electrical synapses. They produce electrical patterns analogous to brain activity. “Cracking the bioelectric code” is crucial for understanding development and memory.
• Bioelectric patterns come in two flavors: *endogenous* (normal development) and *pathological* (e.g., early cancer signs).
• Endogenous ion channels can be manipulated (genetically, pharmacologically, optogenetically) to control electrical states and network topology – *not* by applying external electrical fields.
• Examples of bioelectric control:
  • Inducing ectopic organs (eyes in the gut) by altering electrical state.
  • Creating two-headed (or no-headed) planaria by manipulating gap junctions and electrical pre-patterns. This is *modular* control, not cell-by-cell micromanagement.
  • Changing planarian head shape to that of other species (across 150 million years of evolution) *without* genomic editing, demonstrating physiological control over anatomy.
    • Creating novel planarian body plans not seen in evolution, showing untapped morphospace.
    • Rewriting planarian “pattern memory”: creating two-headed worms from one-headed worms, *persistently* – a stable, rewritable, latent memory.
• Computational models map electrical circuit dynamics to anatomical outcomes. The long-term goal: a biological “anatomical compiler”.
• Regenerating frog legs (which normally don’t regenerate) by altering bioelectric state at the wound – *without* stem cells or genomic editing.
• Reversing birth defects (brain malformations) in tadpoles using bioelectric simulations and targeted drug treatments to restore normal electrical patterns, even with underlying genetic mutations.

Future Directions: Regenerative Medicine and AI

• The long term aim is to develope a type of an “Anatomical Compiler”, this translates a described anatomy (similar to cad) into what it is that needs to happen for it to build.
• Evolution exploited electrical circuits for information processing early on. Cracking the bioelectric code has implications for regenerative medicine, synthetic bioengineering, and morphological computation.
• Limitations of current machine learning may stem from focusing on brains. Non-neural architectures (cells, tissues) offer a new approach.
• Other cells (bone, heart, pancreas) also show learning and adaptation. Living systems are incredibly robust to novel circumstances, a key feature for robotics and AI.
• The time-scales are distinct: electrical states of relevance for this take time to stabilize, taking from minutes to days. Muscle and nerves function far quicker.

Ethical Concerns (briefly mentioned)

• Concerns exist but it is lesser of all ethical concerns for medicine as of now since biolectricity deals mostly in guiding of cell behavior, unlike virus injections.

QnA

• Methods to modify signals is not through directly providing electricity or frequency but though gene mod and small molecule modification.
• Basal cognition is present in most early stages of living entities.

导言：身体和大脑的可塑性

• 早在脑出现之前，生物学就已经在多个尺度上进行计算了。所有身体组织中的决策都是由前神经电网络（不仅仅是神经元）介导的。这是人工智能一个尚未开发的新前沿。
• 毛毛虫在变态（神经系统彻底重组）过程中保留记忆。涡虫（扁虫）再生整个身体，包括大脑，并在头部截肢后保留记忆，表明非脑记忆存储。

躯体认知和解剖自我编辑

• 认知存在于一个阶梯上，从简单到复杂。即使是单细胞生物（例如，泪腺虫），也能在没有大脑的情况下表现出复杂的行为、结构控制和生理机能。
• 多细胞生物体保留了细胞决策。胚胎和再生生物（蝾螈、涡虫）展示了大规模的解剖控制和稳态，即使受到巨大干扰（例如，切割胚胎，将尾巴移植到四肢上）。
• 再生不仅仅是开始；关键在于在正确的解剖结构处*停止*。人的肝脏、鹿角以及（儿童的）指尖也显示出再生能力。
• 基因组不编码死记硬背的运动。器官可以重塑以达到“正常”形态，即使是从不正确的起始位置。
• DNA 指定蛋白质，而不是直接指定三维形状。细胞群“知道”要构建什么以及何时停止。在理解这种解剖控制方面存在一个重大的知识空白。

生物电机制和目标导向的重塑

• 模式控制是一个*闭环*系统，而不是开环系统。它具有错误检测和纠正功能。目标是针对解剖学的*设定点*，以用于治疗目的。
• 躯体组织形成电网络，这些网络做出关于*解剖*而不是行为的决定。这些可以作为模式编辑的目标。
• 神经元是从已经进行计算的古老细胞进化而来的。突触机制、离子通道和神经递质早在脑出现之前就存在了。这解释了为什么真菌化合物（例如，致幻剂）会影响人类的认知——共享古老的机制。
• 所有细胞（不仅仅是神经元）都有离子通道并形成电突触。它们产生类似于大脑活动的电模式。“破解生物电密码”对于理解发育和记忆至关重要。
• 生物电模式有两种：*内源性*（正常发育）和*病理性*（例如，早期癌症征兆）。
• 内源性离子通道可以通过基因、药理学、光遗传学手段进行操纵，以控制电状态和网络拓扑——而不是通过施加外部电场。
• 生物电控制的例子：
  • 通过改变电状态诱导异位器官（肠道中的眼睛）。
  • 通过操纵间隙连接和电预模式创建双头（或无头）涡虫。这是*模块化*控制，而不是逐个细胞的微观管理。
  • 在不进行基因组编辑的情况下，将涡虫头部形状改变为其他物种的形状（跨越 1.5 亿年的进化），证明了解剖结构的生理控制。
    • 创造进化中未见的新的涡虫身体计划，显示了尚未开发的形态空间。
    • 重写涡虫“模式记忆”：从单头涡虫创建双头涡虫，*持久地*——一种稳定的、可重写的、潜在的记忆。
• 计算模型将电路动力学映射到解剖学结果。长远目标：生物“解剖编译器”。
• 通过改变伤口处的生物电状态来再生青蛙腿（通常不再生）——*无需*干细胞或基因组编辑。
• 即使存在潜在的基因突变，也可以使用生物电模拟和有针对性的药物治疗来恢复正常的电模式，从而逆转蝌蚪的出生缺陷（大脑畸形）。

未来方向：再生医学和人工智能

• 长远目标是开发一种“解剖编译器”，它将描述的解剖结构（类似于 CAD）转换为实现它所需的步骤。
• 进化早期就利用电路进行信息处理。破解生物电密码对再生医学、合成生物工程和形态计算都有影响。
• 当前机器学习的局限性可能源于专注于大脑。非神经结构（细胞、组织）提供了一种新方法。
• 其他细胞（骨骼、心脏、胰腺）也表现出学习和适应能力。生命系统对新环境具有难以置信的鲁棒性，这是机器人和人工智能的关键特征。
• 时间尺度是不同的：与此相关的电状态需要时间来稳定，从几分钟到几天不等。肌肉和神经的功能要快得多。

伦理问题（简要提及）

• 存在担忧，但就目前而言，这是医学上所有伦理问题中较轻的，因为生物电主要涉及引导细胞行为，而不像病毒注射。

问答环节

• 修改信号的方法不是通过直接提供电力或频率，而是通过基因改造和小分子修饰。
• 基础认知存在于大多数早期生命体中。