Introduction: Brain Plasticity and Beyond

• Brains are not hardwired; they show significant plasticity, adapting to new inputs and even radical structural changes (e.g., tadpoles seeing with tail-eyes, memory persistence through caterpillar metamorphosis).
• Planaria (flatworms) demonstrate extreme regeneration and memory persistence even after brain removal, suggesting body-wide information storage.

Cognition Beyond the Brain

• Cognition (information processing, problem-solving) exists at multiple scales: molecular networks, cells, tissues, organs, and whole organisms.
• Brains evolved by optimizing ancient problem-solving mechanisms originally used in non-behavioral spaces (metabolic, transcriptional, anatomical).
• Intelligence can be seen as navigating various “problem spaces” (physical, transcriptional, morphospace, physiological), avoiding local optima.

Developmental Bioelectricity: The Key

• Developmental bioelectricity (voltage patterns in non-neural tissues) illustrates the origins of neural networks and provides a roadmap for regenerative medicine.
• Anatomical homeostasis is introduced. A non-neural model system for basal cognition demonstrating goal-directed activity, problem-solving, representation, and even counterfactuals.
• Cells and tissues navigate not just 3D space, but also transcriptional space (gene activity), morphospace (anatomical configurations), and physiological space. Planaria navigating to handle the stressors such as Barium is introduced.

Anatomical Homeostasis and Collective Intelligence

• Cells collectively solve problems in “morphospace” (the space of possible anatomical forms) to achieve anatomical homeostasis.
• Large scale anatomical goals, e.g. Kidney Tubule Lumen size, is worked out even if low level mechanics change, example by Polyploidy where fewer large cells do what normally multiple smaller cells used to.
• Embryogenesis is reliable but flexible. Examples include: forming monozygotic twins from a split embryo, adjusting cell behavior to maintain kidney tubule size, limb regeneration.
• This involves Error Reduction and Goal-directedness such that perturbation in any situation is still tried to meet set point goals of the normal pattern such as having tadpole parts incorrectly assembled.
• There is feedback loop which are in Genetics and Physics which gets to these state such as feedback in thermostats and systems that pursure goals. The set point (or target morphology) is interesting is more complex not a single number such as Ph or Hunger.

Bioelectric Circuits: Storing the “Set Point”

• Similar to brains with Hardware(neuron network), Software(electrical activity). Commitment to be able to Decode electrical patterns of Brain. Difference: Brain system uses Output triggers muscles to do the same stuff such as Gene expression but to trigger Shape Changes. So outside neuroscience decoding of the frog and how ion channels create the patterns in electric circuits can teach about all the tricks the brain use and use this to make rational decisions such as Optogenetics.
• Bioelectric circuits (networks of cells communicating via ion channels and gap junctions) store a “pattern memory” or “target morphology” – the “ideal” body plan. This concept, Pattern memory, can be “rewritten”.
• Altering bioelectric patterns can reprogram regeneration (e.g., creating two-headed planaria, changing head shape), even causing planaria to grow heads of *other* species. This is memory rewriting, without changes to genome!
• The bioelectric pattern is *not* simply a reflection of current anatomy; it’s a latent memory guiding future regeneration, a kind of “counterfactual” memory. It’s like Planaria Brains: A single hardware stores memory Target Morphology that can recall and execute the right steps even after perturbation.
• Experiments with manipulating bioelectricity (using ion channels, optogenetics) demonstrate the ability to: induce tumor suppression, direct eye formation in abnormal locations, repair brain defects, stimulate leg regeneration. These manipulations are modular – triggering pre-existing developmental subroutines, such as with Trigger Subroutines such as Trigger to eye.
• This offers a path to regenerative medicine: changing the “set point” of anatomical homeostasis rather than micromanaging genes.

Synthetic Bioengineering and the Future

• Synthetic bioengineering is “engineering by subtraction”. For example, creating xenobots (novel organisms from frog skin cells) with unexpected behaviors (movement, self-replication) simply by isolating the cells, revealing their inherent plasticity.
• Evolution created *problem-solvers* at multiple level of competency; and at higher levels, each level know their job very well to keep resilience.
• Collective intelligence: where to goals from? For example stem cells can create multiple species of heads such as Roundhead Planaria or Flathead Planaria and how is that determined.
• The creation of chimeras (organisms combining parts from different species) and synthetic organisms blurs the lines between natural and artificial, challenging our definitions of “machine,” “organism,” and “robot”. Xenobots behaviors don’t have straightforward evolution story as the do not come about selection pressure. Xenobots will Heal when Cut up showing an amazing engineering force is exhibited, even small number of cell Xenobots articulate the movements of many animals. Sequencing genome cannot easily explain behavior.
• We face an “explosion of unconventional agents,” combining evolved and designed components, requiring new theories of cognition and ethics that go beyond human-centric views. We should think past current distinction like Animal, Robot, Machine and the Contingencies from the frozen accident in evolution.
• This involves questions for Ethicists.

From the Q and A

• Martha brings the point of regulation and Sci-Fi because these concepts are way ahead of regulators and writers.
• Voltage Map with Green Intensity showing more intensity can show tipping point and asymmetry for creating new growth for a specific part. There exist Electrical Circuits that trigger Downstream pathways.
• Involves how new genes evolve even in Weed genomes when we attack weed such that genome changes happen very rapidly to account to environment change faster than evolution because biological pathways aren’t straight pathways. In addition, it involve many Modules at many competencies levels from higher to lower that the organism is resilient.
• Experiments using regenerations in chemical with genes changing and perturbations as to study Homestatic processes.

导言：大脑可塑性及其他

• 大脑并非硬连接的；它们表现出显著的可塑性，能够适应新的输入，甚至是剧烈的结构变化（例如，蝌蚪用尾巴上的眼睛看东西，毛毛虫变态后记忆的持久性）。
• 涡虫（扁虫）即使在移除大脑后仍表现出极强的再生能力和记忆持久性，表明信息存储于全身。

超越大脑的认知

• 认知（信息处理、解决问题）存在于多个尺度：分子网络、细胞、组织、器官和整个生物体。
• 大脑是通过优化最初用于非行为空间（代谢、转录、解剖）的古老问题解决机制进化而来的。
• 智能可以被视为在各种“问题空间”（物理、转录、形态空间、生理）中导航，避免局部最优。

发育生物电：关键所在

• 发育生物电（非神经组织中的电压模式）阐明了神经网络的起源，并为再生医学提供了路线图。
• 介绍了“解剖稳态”。这是一个非神经模型系统，展示了基础认知，包括目标导向活动、问题解决、表征，甚至是反事实推理。
• 细胞和组织不仅在三维空间中导航，还在转录空间（基因活性）、形态空间（解剖结构）和生理空间中导航。介绍了涡虫如何通过导航来应对诸如钡之类的应激源。

解剖稳态与集体智能

• 细胞集体在“形态空间”（可能的解剖形式的空间）中解决问题，以实现解剖稳态。
• 即使低级机制发生变化，大型解剖目标，例如肾小管管腔大小，也会被解决。例如多倍体，其中较少数量的大细胞完成通常由多个较小细胞完成的工作。
• 胚胎发生是可靠但灵活的。例子包括：从分裂的胚胎形成同卵双胞胎，调整细胞行为以维持肾小管大小，肢体再生。
• 这涉及误差减少和目标导向，以至于在任何情况下，都会尝试满足正常模式的设定点目标，例如蝌蚪的部位组装错误。
• 存在反馈回路，这些反馈回路存在于遗传学和物理学中，它们可以达到这些状态，例如恒温器中的反馈和追求目标的系统。 设定点（或目标形态）很有趣，它比单个数字（如 Ph 值或饥饿感）复杂得多。

生物电路：存储“设定点”

• 类似于具有硬件（神经元网络）、软件（电活动）的大脑。承诺能够解码大脑的电模式。区别：大脑系统使用输出来触发肌肉做同样的事情，例如基因表达，但是是触发形状变化。因此，在神经科学之外，对青蛙的解码以及离子通道如何在电路中创建模式，可以教会我们大脑使用的所有技巧，并使用这些技巧做出合理的决定，例如光遗传学。
• 生物电路（通过离子通道和间隙连接进行通信的细胞网络）存储“模式记忆”或“目标形态”——即“理想”的身体计划。这个“模式记忆”的概念，可以被“重写”。
• 改变生物电模式可以重新编程再生（例如，创造双头涡虫，改变头部形状），甚至导致涡虫长出*其他*物种的头部。这是记忆重写，没有基因组的改变！
• 生物电模式*不*仅仅是当前解剖结构的反映；它是一种指导未来再生的潜在记忆，一种“反事实”记忆。这就像涡虫的大脑：单个硬件存储可以回忆并执行正确步骤的记忆目标形态，即使在扰动之后。
• 操纵生物电（使用离子通道、光遗传学）的实验证明了以下能力：诱导肿瘤抑制、引导眼睛在异常位置形成、修复脑部缺陷、刺激腿部再生。这些操作是模块化的——触发预先存在的发育子程序，例如触发眼睛子程序。
• 这为再生医学提供了一条途径：改变解剖稳态的“设定点”，而不是对基因进行微观管理。

合成生物工程与未来

• 合成生物工程是“通过减法进行工程设计”。例如，仅通过分离青蛙皮肤细胞，就可以创建具有意想不到的行为（运动、自我复制）的异种机器人（新型生物体），从而揭示它们固有的可塑性。
• 进化在多个能力水平上创造了*问题解决者*；在更高的层次上，每个层次都非常清楚自己的工作，以保持弹性。
• 集体智能：目标来自哪里？例如，干细胞可以创建多个物种的头部，例如圆头涡虫或扁头涡虫，这是如何确定的。
• 嵌合体（结合来自不同物种的部分的生物体）和合成生物体的创造模糊了自然与人工之间的界限，挑战了我们对“机器”、“生物体”和“机器人”的定义。 异种机器人的行为没有直接的进化故事，因为它们不是由选择压力产生的。异种机器人在被切割时会愈合，显示出令人惊叹的工程力量，即使是少量细胞的异种机器人也能表达出许多动物的运动。测序基因组不能轻易解释行为。
• 我们面临着“非常规行动者的大爆发”，它结合了进化和设计的组件，需要超越以人为中心的观点的新认知和伦理学理论。我们应该超越当前的区分，例如动物、机器人、机器以及进化中意外冻结事件的偶然性。
• 这涉及伦理学家需要考虑的问题。

问答环节

• Martha 提出了监管和科幻小说的问题，因为这些概念远远领先于监管者和作家。
• 显示更高强度的绿色强度电压图可以显示用于为特定部分创建新增长的临界点和不对称性。 存在触发下游通路的电路。
• 即使在我们攻击杂草时，杂草基因组中也会出现新基因，从而使基因组变化比进化更快地适应环境变化，因为生物通路不是直线通路。此外，它涉及从较高到较低的许多能力级别的许多模块，从而使生物体具有弹性。
• 使用化学物质中的再生进行基因改变和扰动的实验，以研究稳态过程。