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From: Lukas Bresser <82394590+lksbrssr@users.noreply.github.com>
Date: Tue, 18 Aug 2026 10:57:25 -0400
Subject: [PATCH 1/2] Unlisted previews: refresh Neurotech to latest, add DHR
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+---
+title: "Securing Fundamental Rights in the Digital Realm: The Infrastructure Freedom Now Runs On"
+date: 2026-08-30
+summary: "The digital foundations our fundamental rights now depend on — censorship-resistant communication, portable identity, verifiable public knowledge, and sovereign infrastructure for AI and agents — the four opportunity spaces we are backing and the inflection points we believe are within reach."
+authors:
+ - will-scott
+cover_image: "/images/blog/preview-dhr-f12c6df6/hero.webp"
+areas:
+ - digital-human-rights
+# Live at its URL, but kept out of the carousel, insights/blog listings,
+# sitemap, RSS feed, and search index (and set to noindex). Flip to false
+# (or remove) to publish it publicly.
+unlisted: true
+---
+
+
+*Part of a series introducing the focus areas of PL R&D.*
+
+Our information environment is being reshaped in real time by algorithms, networks, protocols, platforms, and adversaries that move faster than the institutions meant to govern them. Digital infrastructure, like core internet protocols, already shapes how we speak, gather, and keep our lives private. We now need to build the infrastructure that protects those freedoms faster, and more wisely, than that same infrastructure can be turned against them.
+
+The types of freedoms we mean are specific: freedom of speech (to seek, receive, and impart information regardless of frontiers); freedom of peaceful assembly and association; the right to privacy; recognition everywhere as a person before the law; and the freedom of thought and self-determination that underwrites them all. Far from new, each was hard-won through long debate.
+
+Juan Benet, CEO of Protocol Labs, discussing the importance of embedding fundamental rights into software at the Web3 Summit in 2019.
+
+At PL R&D, we know that the infrastructure we build today will determine which of these freedoms can be exercised tomorrow. To preserve and extend them, we invest in research, funding, and product development across four interconnected opportunity spaces. These are the frontiers where a technical breakthrough can become a durable protection for a specific right, rather than one more tool for eroding it.
+
+## The framework: four layers of sovereign infrastructure
+
+We organize our work into four layers, each sustaining specific rights:
+
+
+
1Censorship-Resistant Communication = keeping connectivity alive in low-connectivity, partitioned, or adversarial environments — sustaining freedom of expression, assembly, and association
+
2Portable Identity, Credentials & Trust = verifiable credentials and portable reputation owned by the individual, not the platform — sustaining recognition as a person before the law, and privacy
+
3Verifiable Public Knowledge & Provenance = durable, tamper-evident records and standards for non-intermediated trust — sustaining the right to seek, receive, and share accurate information
+
4Agency & Governance = privacy-preserving systems through which humans and their agents coordinate, transact, and reach consensus — sustaining self-determination and freedom of thought
+
+
+These layers stack. Resilient communication is the base every other right assumes; on top of it, portable identity establishes who you are without renting that standing from a platform or state; verifiable knowledge lets a public record prove its own integrity; and open agency and governance decide whether the AI acting on your behalf extends your control or concentrates it in a few hands.
+
+
+
+
+
Each layer rests on the one below it — resilient communication at the base, up through identity, verifiable knowledge, and open agency & governance.
+
+
Opportunity Space 1 Censorship-Resistant Communication
+
+
+
+### What it is
+
+Keeping communication and connectivity alive even in low-connectivity, partitioned, or adversarial environments. The frontier has moved past simple encryption toward metadata-resistance (hiding not just what you say but whom you say it to) and partition-tolerance (staying connected when the network is deliberately fragmented).
+
+### Why it matters
+
+Speech and assembly are the first freedoms to go in any adversarial environment, and both assume you can reach other people — an assumption that residential and cellular networks, controlled by a few incumbents and the states that license them, can revoke at will.
+
+### Progress so far
+
+Nation-state-independent connectivity is appearing: low-earth-orbit satellite networks such as [Starlink](https://www.starlink.com/) decouple a person's ability to get online from the institutions that govern local access. Metadata-resistant tooling such as [Kohaku](https://github.com/ethereum/kohaku) (Ethereum Foundation) is productionizing Private Information Retrieval and mix networks inside a wallet, so that messages — and the patterns of who contacts whom — leak as little as possible. And modular networking stacks such as [libp2p](https://libp2p.io/) let applications find one another and stay connected even when traditional rails fail.
+
+
+
Inflection point
+
Communication that cannot be switched off
+
The step-change comes when nation-state-independent connectivity and metadata-resistant messaging reach consumer scale, so that during a deliberate shutdown a measurable share of a population stays connected and able to organize. Observable, and not yet true: a global connectivity provider offers consumer-scale service without state licensing or identity gating, and a metadata-resistant messenger crosses tens of millions of users under real adversarial conditions. At that point, censoring who may speak or gather online becomes impractical rather than merely illegal.
+
+
+
Opportunity Space 2 Portable Identity, Credentials & Trust
+
+
+
+### What it is
+
+Verifiable credentials and portable reputation owned by the individual rather than the platform — for humans and, increasingly, for the agents acting on their behalf — without depending on a centralized government ID or on expert-level key management.
+
+### Why it matters
+
+Identity is how a person is recognized, and today that recognition is mostly rented from platforms and states: lose the account or the document and you lose the standing. Without portability, people are trapped in whatever platform first issued their identity; without privacy, identity becomes a surveillance dossier. This sustains recognition as a person before the law, and privacy.
+
+### Progress so far
+
+Open social protocols such as the [Authenticated Transfer (AT) Protocol](https://atproto.com/) behind [Bluesky](https://bsky.app/) give people a credible exit: identity and data bind to a portable identifier the user controls, so a whole social graph can move between providers without loss. Sybil-resistant systems such as [World](https://world.org/) establish that someone is a unique human without tying that proof to a state document. And zero-knowledge credential systems — spanning age verification, passkeys, and verifiable credentials — let a person prove one specific claim while disclosing as little as possible.
+
+
+
Inflection point
+
Personhood without the state in the loop
+
The step-change comes when a service at real scale — more than 100 million people — verifies unique humans for everyday services without anchoring them to a nation-state identity or KYC. Observable, and not yet true: today's proof-of-personhood systems operate well below that scale and outside mainstream services. When one crosses it, recognition as a person need no longer be rented from a state or a platform, and privacy-preserving personhood becomes safe to build on.
+
+
+
Opportunity Space 3 Verifiable Public Knowledge & Provenance
+
+
+
+### What it is
+
+Durable, tamper-evident records and standards for non-intermediated trust: systems that can prove a knowledge artifact has not been altered, and keep proving it over time.
+
+### Why it matters
+
+The rights to seek, receive, and share information depend on a shared public record people can trust. The ability to prove a piece of information is authentic has always underpinned that record, and as AI-generated content floods the internet that ability gets far more fragile and far more urgent. As global truth gets harder to verify, trust retreats into small private circles — a dark forest in which a common, checkable account of what happened ceases to exist.
+
+### Progress so far
+
+Content addressing, as in [IPFS](https://ipfs.tech/), gives every file a verifiable identifier derived from its contents, so a reader can confirm data is exactly what was published. Provenance frameworks such as [Starling Lab](https://www.starlinglab.org/) establish verifiable chains of custody for journalism and historical records. Large-scale archives such as the [Internet Archive](https://archive.org/) preserve digital history at scale. And formal-verification systems such as [Lean](https://lean-lang.org/), with zero-knowledge proofs of execution, extend provenance from documents to [the computations that produced them](https://blog.atlascomputing.org/p/a-refinement-based-paradigm-for-code).
+
+
+
Inflection point
+
Provenance becomes the default for truth
+
The step-change comes when content authenticity stops being optional. Observable, and not yet true: two consecutive generations of frontier AI models ship attested provenance tooling by default, and at least one major platform or archive adopts content-addressed provenance as the default for its public record. As synthetic content becomes indistinguishable from the real thing, a public record that can prove its own integrity becomes the precondition for accurate information meaning anything at all.
+
+
+
Opportunity Space 4 Sovereign Infrastructure for AI & Agents
+
+
+
+### What it is
+
+The open environment — compute, storage, and identity — that lets AI agents coordinate and transact on our behalf: open enough to be permissionless, accountable enough to keep humans in charge.
+
+### Why it matters
+
+AI is becoming a powerful extension of each of us and of the institutions around us, and the open question is whether that capability stays under individual human control or concentrates in a few hands. An agent that acts on your behalf only extends your agency if you, and not a single platform, govern the compute, storage, and identity it runs on. This is where self-determination and freedom of thought are won or lost.
+
+### Progress so far
+
+Open storage markets such as [Filecoin](https://www.filecoin.io/) run on independent providers rather than one centralized host, so no single party can deny, alter, or lose your data. Fully homomorphic encryption environments such as [Zama](https://www.zama.org/) let computation run directly on encrypted data, so agents can coordinate in public without exposing what they hold. And open compute protocols such as [Gensyn](https://www.gensyn.ai/) and [Prime Intellect](https://www.primeintellect.ai/) train models across independent, globally distributed hardware rather than inside a single lab.
+
+
+
Inflection point
+
Agents run on open rails
+
The step-change comes when serious AI capability no longer requires a single provider's stack. Observable, and not yet true: a frontier-scale model is trained across independent, decentralized hardware rather than one company's cluster, or a meaningful share of agent-to-agent economic activity settles on open, permissionless compute, storage, and identity rather than inside one platform. At that point the rights architecture of the agent economy is set in the open rather than by whoever owns the cluster.
+
+
+## Building the field
+
+The next few years will decide whether these freedoms are quietly curtailed or deliberately extended. Human freedom can be hollowed out when infrastructure is centralized, surveilled, or switched off, and PL R&D focuses on the root system a free digital society stands on. We do not build every piece; we connect them, and aim our toolkit — field-building communications, convenings, grants, venture support, and policy and standards work — at the blockers specific to this field. (That toolkit is described in the [PL R&D Overview](/blog/how-pl-rd-accelerates-breakthroughs/).) PL's foundational primitives here — IPFS, libp2p, and content-addressed data — are among the strongest in the portfolio.
+
+## Get involved
+
+If you are working on any of these four layers, we want to hear from you:
+
+* **Founders and researchers** building censorship-resistant communication, portable identity, verifiable provenance, or agent infrastructure: tell us what you are building and where you are stuck.
+* **Investors and funders** who want to co-fund this frontier or respond to a specific Request for Startups.
+* **Policymakers, journalists, and civil-society groups** who depend on these guarantees in practice: partner with us on real-world deployments and the evidence that comes from them.
+
+We invite you to explore the [projects already contributing to this vision](/areas/digital-human-rights/). Reach us at [research@protocol.ai](mailto:research@protocol.ai).
+
+The rights we refuse to lose will be defended in the infrastructure, or not at all. Join us.
+
+*This post is for informational purposes only. It is not an offer, solicitation, or recommendation of any security or investment product, and nothing here is a commitment or guarantee of any future performance or any outcome.*
diff --git a/content/blog/preview-neurotech-ea88a298/index.md b/content/blog/preview-neurotech-ea88a298/index.md
index e1838757..3182226b 100644
--- a/content/blog/preview-neurotech-ea88a298/index.md
+++ b/content/blog/preview-neurotech-ea88a298/index.md
@@ -1,6 +1,6 @@
---
title: "Neurotechnology: Bridging Minds and Machines for Human Flourishing"
-date: 2026-07-27
+date: 2026-08-22
summary: "An overview of PL R&D's Neurotechnology focus area — brain-computer interfaces, biologically inspired AI, and whole-organism emulation — the three opportunity spaces we are backing, the inflection points we believe are within reach, and the 2030 milestones we are working toward."
authors:
- sean-escola
@@ -15,23 +15,45 @@ unlisted: true
---
-At PL R&D, we focus on fields that have the potential to unlock transformative new capabilities for humanity. The Neurotechnology Focus Area aims to accelerate computing breakthroughs across neuroscience and neurotechnology.
+At PL R&D, we focus on fields that have the potential to unlock transformative new capabilities for humanity. The PL Neuro Focus Area aims to accelerate computing breakthroughs within the fields of neuroscience and neurotechnology.
-Advances in neuroscience, brain-computer interfaces (BCIs), biologically inspired AI, and whole-organism emulation are accelerating rapidly. Together, they open a path toward understanding intelligence, restoring and expanding human capabilities, and building new forms of human-machine interaction. The field today remains fragmented: technical, regulatory, infrastructure, and capital bottlenecks continue to slow progress. We aim to change that.
+Advances in neuroscience, brain-computer interfaces (BCIs), biologically inspired AI, and whole-organism emulation are accelerating rapidly. Together, they unlock a future where we can better understand intelligence, restore and expand human capabilities, and build entirely new forms of human-machine interaction. However, the field today remains fragmented: key technical, regulatory, infrastructure, and capital bottlenecks continue to slow progress. We aim to change that.
PL Neuro's mission is to help secure a future of human flourishing by bridging minds and machines in ways that expand human potential while preserving autonomy, dignity, and individual agency.
-## What We Do
-
We focus on three opportunity spaces that could reshape both neuroscience and computing over the coming decade:
-
1Neural Augmentation = high-bandwidth, bidirectional interfaces between brains and computers (BCI)
-
2Biologically Inspired Intelligence = AI systems that learn from how brains work (NeuroAI)
-
3Whole-Organism Emulation = computational models that reproduce the behavior of biological organisms (WOE)
+
1Neural Augmentation (Brain-Computer Interfaces)
+
2Biologically Inspired Intelligence (NeuroAI)
+
3Whole Organism Emulation (WOE)
-These areas form a stack. Advances in neuroscience generate new data and understanding. Those insights enable more capable AI systems and neurotechnologies. Progress across the stack opens new possibilities for augmenting human cognition, and each layer feeds the next: better recording produces better data, better data trains better models, and better models improve both the interfaces we build and the emulations we can run.
+Together, these areas form a loop: advances in neuroscience generate new data and understanding; those insights enable more capable AI systems and neurotechnologies; and progress across both creates entirely new possibilities for augmenting human cognition.
+
+
+
+
Opportunity Space 1 Neural Augmentation (BCI)
@@ -39,13 +61,13 @@ These areas form a stack. Advances in neuroscience generate new data and underst
Neural augmentation focuses on building high-bandwidth, bidirectional interfaces between brains and computers.
-Near-term applications are therapeutic: restoring communication, movement, and independence for people living with paralysis or neurological conditions. Longer-term, these same technologies may enable new forms of interaction, communication, and cognition.
+Near-term applications are therapeutic: restoring communication, movement, and independence for people living with paralysis or neurological conditions. Longer-term, these same technologies may enable entirely new forms of interaction, communication, and cognition.
### Why it matters
BCIs have already demonstrated life-changing benefits in clinical settings. The next challenge is moving from isolated medical devices to scalable platforms that support broad innovation.
-Areas we find particularly promising include:
+Key areas of interest include:
* Higher-bandwidth neural interfaces
* Less invasive and more scalable devices
@@ -54,29 +76,34 @@ Areas we find particularly promising include:
### Progress so far / case studies of momentum
-Higher data-rate interfaces are advancing toward the clinic: [Paradromics received FDA approval for the Connect-One clinical study](https://paradromics.com/news/paradromics-receives-fda-approval-for-the-connect-one-clinical-study-with-the-connexus-brain-computer-interface/) of its Connexus® brain-computer interface, designed to restore speech and computer control for people with severe motor impairment. Less invasive devices are clearing regulatory review in parallel: [Precision Neuroscience received FDA 510(k) clearance](https://www.precisionneuro.io/articles/company-news/precision-neuroscience-receives-fda-clearance-for-high-resolution-cortical-electrode-array) for its Layer 7 Cortical Interface, a high-resolution cortical electrode array roughly one-fifth the thickness of a human hair. For a fuller picture of where the clinical BCI market is heading, see PL's [strategic vision for the future of brain–computer interfaces](/publications/pl-bci-roadmap-overview/) and its analysis of the [path to clinical revenue](/publications/models-to-markets-bci-neural-data/).
+* **Bandwidth** — [Paradromics Receives FDA Approval for the Connect-One Clinical Study with the Connexus® Brain-Computer Interface](https://paradromics.com/news/paradromics-receives-fda-approval-for-the-connect-one-clinical-study-with-the-connexus-brain-computer-interface/)
+* **Less Invasive** — [FDA clears Precision Neuroscience's minimally invasive brain-computer interface implant](https://www.precisionneuro.io/articles/company-news/precision-neuroscience-receives-fda-clearance-for-high-resolution-cortical-electrode-array)
-
Inflection point
-
Clinical BCI superpower
-
BCIs will demonstrate major improvements to the quality of life and capabilities of clinical patients, some of which will exceed the capabilities of healthy individuals.
+
Inflection point #1
+
Clinical BCI Superpower
+
Brain-computer interfaces will demonstrate major improvements to the quality of life and capabilities of clinical patients — some of which will exceed the capabilities of healthy individuals.
-
Inflection point
-
The BCI app store
-
We believe a major shift will occur when BCIs move from vertically integrated medical products to open platforms. A standardized software layer that lets third-party developers build applications on top of approved BCI hardware could increase the utility of neural interfaces by orders of magnitude. Smartphones became more valuable once app ecosystems formed on top of standard hardware; BCIs could follow the same path once many developers can deploy neural augmentations through scalable software.
+
Inflection point #2
+
The BCI App Store
+
We believe a major catalyst will occur when BCIs transition from vertically integrated medical products into open platforms. A standardized software layer that allows third-party developers to build applications on top of approved BCI hardware could dramatically increase the utility of neural interfaces. Just as smartphones became more valuable through app ecosystems, BCIs could unlock a wave of innovation once many developers can deploy neural augmentations through scalable software deployments.
Opportunity Space 2 Biologically Inspired Intelligence (NeuroAI)
-NeuroAI uses insights from biological intelligence to build more capable, efficient, and accessible AI systems. It treats the brain not only as an object of study but as a source of architectural, representational, and algorithmic ideas.
+NeuroAI seeks to use insights from biological intelligence to build more capable, efficient, and accessible AI systems.
+
+Rather than treating the brain as merely an object of study, NeuroAI treats it as a source of architectural, representational, and algorithmic inspiration.
### Why it matters
-Modern AI has achieved remarkable capabilities, often at enormous computational and energy cost. Brains show that intelligence can emerge from systems that are dramatically more efficient than today's machine learning architectures. Understanding how biological intelligence works may help unlock the next generation of AI.
+Modern AI carries enormous computational and energy cost.
+
+Brains demonstrate that intelligence can emerge from systems that are dramatically more efficient than today's machine learning architectures. Understanding how biological intelligence works may help unlock the next generation of AI.
Areas we find particularly promising include:
@@ -87,31 +114,36 @@ Areas we find particularly promising include:
### Progress so far / case studies of momentum
-Large-scale neural data is reaching new resolution: the [MICrONS project](https://www.microns-explorer.org/cortical-mm3) mapped a cubic millimeter of mouse visual cortex, resolving roughly 200,000 cells and 523 million synapses in a single functional connectome. Neural foundation models are learning to predict brain activity directly: Meta's [TRIBE v2](https://ai.meta.com/blog/tribe-v2-brain-predictive-foundation-model/) predicts human brain responses to naturalistic video, audio, and text, trained on more than 1,000 hours of fMRI across 720 subjects. These two threads point in the same direction: more data at higher resolution, feeding models that treat neural activity as a first-class training signal.
+* **Large-scale neural data** — [The MICrONS Project](https://www.microns-explorer.org/)
+* **Neural foundation models** — [Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli](https://ai.meta.com/blog/tribe-v2-brain-predictive-foundation-model/)
-
Inflection point
-
Neural distillation
-
One potential breakthrough is the emergence of methods that directly align AI systems with human neural activity. If neural recordings help models learn more efficiently, or reason in ways that better reflect human cognition, neural data could become a foundational resource for AI development.
+
Inflection point #1
+
Neural Distillation
+
One potential breakthrough is the emergence of methods that directly align AI systems with human neural activity. If neural recordings can help models learn more efficiently, or think in ways that better reflect human cognition, neural data could become a foundational resource for AI development. A specific inflection point would be 100,000,000 hours of human neural data recorded across cognitive tasks and recording device types.
-
Inflection point
-
The neuromorphic energy pivot
-
A second possibility is that energy constraints push the AI industry toward biologically inspired hardware and algorithms. If brain-inspired systems achieve orders-of-magnitude improvements in efficiency, neuroscience could become a core driver of future AI progress.
+
Inflection point #2
+
The Neuromorphic Energy Pivot
+
A second possibility is that energy constraints push the AI industry toward biologically inspired hardware and algorithms. If brain-inspired systems achieve orders-of-magnitude improvements in efficiency vs current hardware or software designs, neuroscience could become a core driver of future AI progress.
-
Opportunity Space 3 Whole-Organism Emulation (WOE)
+
Opportunity Space 3 Whole Organism Emulation (WOE)
-Whole-organism emulation seeks to build computational models that reproduce the behavior of biological organisms using detailed neural and biological data. Often discussed as science fiction, it is increasingly an engineering challenge shaped by advances in connectomics, imaging, simulation, and neuroscience.
+Whole organism emulation seeks to create computational models that reproduce the behavior of biological organisms using detailed neural and biological data.
+
+While often discussed as science fiction, the field is increasingly becoming an engineering challenge shaped by advances in connectomics, imaging, simulation, and neuroscience.
### Why it matters
-A working emulation system would give researchers a new tool for understanding intelligence, learning, memory, and behavior. It could also accelerate neuroscience by enabling experiments that are difficult or impossible to run in living systems.
+A successful emulation system would provide an unprecedented tool for understanding intelligence, learning, memory, and behavior.
-Areas we find particularly promising include:
+It could also dramatically accelerate neuroscience by enabling experiments that are difficult (or impossible) to perform in living systems.
+
+Promising areas include:
* High-throughput connectomics
* Brain reconstruction pipelines
@@ -120,56 +152,52 @@ Areas we find particularly promising include:
### Progress so far / case studies of momentum
-High-throughput connectomics is getting cheaper and faster: a Nature study on [light-microscopy-based dense connectomic reconstruction](https://www.nature.com/articles/s41586-025-08985-1) of mammalian brain tissue shows a route to mapping wiring and molecular identity together. Reconstruction pipelines are removing the manual-correction bottleneck: Janelia's [PATHFINDER](https://www.janelia.org/publication/accelerating-neuron-reconstruction-with-pathfinder) uses AI to segment volumetric image data and assemble neuron reconstructions with far less human proofreading. And modeling work is beginning to translate static structure into dynamics: PL-supported research on [compiling molecular ultrastructure into neural dynamics](/publications/molecular-ultrastructure-neural-dynamics/) proposes mapping ultrastructural data to the physiological parameters that govern neural activity. PL has also laid out [how a complete human connectome could be obtained at synaptic resolution within the next decade](/publications/how-to-obtain-complete-human-connectome/).
+Three major constraints sit between a fixed brain and a running simulation: how much tissue can be imaged, how much human labor reconstruction takes, and whether structural data is sufficient to specify function. Significant progress has been made against all three constraints in the past year.
-
-
Inflection point
-
Memory retrieval in simulation
-
The most catalytic milestone may be a simple one. If a reconstructed mouse brain can be simulated and reliably reproduce a learned behavior or memory from its biological counterpart in a virtual environment, whole-organism emulation moves from speculation to demonstration. That result would establish a concrete benchmark for the field and change how researchers, funders, and policymakers view the possibility and impact of emulation.
-
+On imaging, moving off electron microscopy changes the cost curve. A team at the Institute of Science and Technology Austria published a light-microscopy pipeline that expands tissue roughly 16-fold. Light microscopes are cheaper and far more widely installed than electron microscopes, and the method leaves room for molecular labels that electron microscopy cannot read.
-## The 2030 milestones we are working toward
+On reconstruction, the binding cost at mouse scale is human proofreading hours. Janelia's [PATHFINDER](https://www.janelia.org/publication/accelerating-neuron-reconstruction-with-pathfinder) pipeline reports 94.2% normalized expected run length for exhaustive axon reconstruction and an 84-fold reduction in projected proofreading cost against prior work.
-Field-building needs shared targets. We are organizing PL Neuro's work around three measurable milestones that mark real progress across the stack:
+On sufficiency, the field's hardest open question is whether a connectome constrains dynamics tightly enough to simulate. A recent preprint proposes an ultrastructure-to-dynamics compiler. If mappings like this hold, molecular annotation plus a connectome becomes enough to parameterize a simulation.
-
-
10,000
invasive high-bandwidth neural implants in humans — multiple devices with clinical approval, and a first invasive consumer use case.
-
100M
hours of human neural data — across cognitive tasks and recording-device types, enough to unlock neural foundation models for prediction, translation, and fill-in, and to pair cheap noninvasive readout hardware (e.g. EEG) with bidirectional implants for closed-loop control.
-
1
complete mouse-brain connectome — a foundation for new architectures and learning rules, and a major step toward whole-organism emulation.
+
+
Inflection point
+
Memory Retrieval in Simulation
+
The most catalytic milestone may be surprisingly simple. If a reconstructed mouse brain can be simulated and reliably reproduce a learned behavior or memory from its biological counterpart in a virtual environment, whole-organism emulation moves from speculation to demonstration. Such a result would establish a concrete benchmark for the field and fundamentally change how researchers, funders, and policymakers view the possibility and impact of emulation.
