We focus on three opportunity spaces that could reshape both neuroscience and computing over the coming decade:
-
+
Neural Augmentation (Brain-Computer Interfaces)
@@ -28,7 +28,7 @@ unlisted: true
Whole Organism Emulation (WOE)
-
+
Together, these areas form a loop: advances in neuroscience (via augmentation and mapping) generate new data and understanding; those insights enable more capable AI systems and neurotechnologies (NeuroAI applications); and progress across both creates entirely new possibilities for augmenting human cognition (improved augmentation & emulation).
@@ -84,7 +84,7 @@ unlisted: true
Progress so far / case studies of momentum
-
Making neural interfaces useful beyond a small number of research participants requires progress on several fronts: how much information they can carry, how invasively they interact with neural tissue, and how they reach patients through clinical care. Recent developments show movement on each, although the devices are at different stages of clinical and commercial development.
+
Making neural interfaces useful beyond a small number of research participants requires progress on several fronts: how much information they can carry, how directly they interact with neural tissue, and how they reach patients through clinical care. Recent developments show movement on each, although the devices are at different stages of clinical and commercial development.
diff --git a/package.json b/package.json
index 89d7890b..7db87103 100644
--- a/package.json
+++ b/package.json
@@ -8,7 +8,7 @@
"build": "node scripts/build-content.mjs && next build",
"start": "next start",
"generate-jwk": "node scripts/generate-jwk.js",
- "test": "node --test scripts/velocity/*.test.mjs"
+ "test": "node --test scripts/velocity/*.test.mjs scripts/neuro-content.test.mjs"
},
"dependencies": {
"@atproto/api": "^0.18.17",
diff --git a/scripts/neuro-content.test.mjs b/scripts/neuro-content.test.mjs
new file mode 100644
index 00000000..b146714c
--- /dev/null
+++ b/scripts/neuro-content.test.mjs
@@ -0,0 +1,13 @@
+import assert from 'node:assert/strict'
+import { readFileSync } from 'node:fs'
+import { test } from 'node:test'
+const read = (file) => readFileSync(new URL('../' + file, import.meta.url), 'utf8')
+const article = JSON.parse(read('src/data/generated/blog.json')).find(post => post.title === 'Neurotech as a Frontier for Human Flourishing')
+test('Neuro opportunity spaces render as bullets', () => {
+ assert.ok(/
Whole Organism Emulation \(WOE\)<\/li>\s*<\/ul>/.test(article.html), 'Introductory opportunity spaces must be an unordered list')
+})
+
+test('Neuro interface wording uses directly', () => {
+ assert.ok(article.html.includes('how directly they interact with neural tissue'))
+ assert.ok(!article.html.includes('how invasively they interact'))
+})
diff --git a/src/data/generated/blog.json b/src/data/generated/blog.json
index 5a3e5ff6..7dae808e 100644
--- a/src/data/generated/blog.json
+++ b/src/data/generated/blog.json
@@ -14,7 +14,7 @@
],
"external_url": "",
"coverImage": "/images/blog/neurotechnology-hero.webp",
- "html": "\n
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 breakthroughs within the fields of Neuroscience & Neurotechnology.
\n
Advances in neuroscience, brain-computer interfaces (BCIs), biologically inspired AI, and whole-brain 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.
\n
PL Neuro exists to break bottlenecks: to define the milestones neurotech and NeuroAI need, drive the research that shifts what's believed possible, and route talent and capital to some of humanity's hardest and most important problems.
\n
We focus on three opportunity spaces that could reshape both neuroscience and computing over the coming decade:
\n\n
Neural Augmentation (Brain-Computer Interfaces)
\n
Biologically Inspired Intelligence (NeuroAI)
\n
Whole Organism Emulation (WOE)
\n\n
Together, these areas form a loop: advances in neuroscience (via augmentation and mapping) generate new data and understanding; those insights enable more capable AI systems and neurotechnologies (NeuroAI applications); and progress across both creates entirely new possibilities for augmenting human cognition (improved augmentation & emulation).
\n
\n\n
\n
Opportunity Space 1: Neural Augmentation (BCI)
\n\n
Neural augmentation focuses on building high-bandwidth, bidirectional interfaces between brains and computers.
\n
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 that extend human capability.
\n
Why it matters
\n
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 access and innovation.
\n
Key areas of interest include:
\n
\n
Higher-bandwidth neural interfaces
\n
Less invasive and more scalable devices
\n
Improved implantation and deployment infrastructure
\n
Open software ecosystems built on safe, secure neural hardware
\n
\n
Progress so far / case studies of momentum
\n
Making neural interfaces useful beyond a small number of research participants requires progress on several fronts: how much information they can carry, how invasively they interact with neural tissue, and how they reach patients through clinical care. Recent developments show movement on each, although the devices are at different stages of clinical and commercial development.
\n
\n
On bandwidth, Paradromics received FDA approval for the Connect-One Early Feasibility Study of its fully implantable Connexus BCI. The system records activity from individual neurons and is designed to translate neural activity into speech and computer control. The study provides a way to test whether its high-data-rate interface can deliver safe, reliable benefits for people with severe motor impairment over sustained use.
\n
On invasiveness, Precision Neuroscience received FDA clearance for its Layer 7 Cortical Interface, a high-resolution electrode array that sits on the brain’s surface. The clearance permits recording, monitoring, and stimulation for up to 30 days, extending use beyond brief recordings during surgery. It creates more scope for clinical research and neural data collection, while the company’s fully implantable wireless BCI remains in development.
\n
On clinical deployment, Science Corp. announced the European commercial launch of PRIMA after receiving CE marking. The retinal implant uses electrical stimulation to restore functional central vision in people with geographic atrophy caused by age-related macular degeneration. Commercial authorization creates a route beyond clinical trials; reimbursement and clinical-site rollout are the next practical steps toward wider access.
\n
\n
\n
Inflection point #1:
\n
Clinical BCI Superpower
\n
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 - creating demand for BCIs beyond clinical applications to access these superpowers. This demand helps reach a major inflection point for BCI access and utility: 10,000 high-bandwidth neural implants in humans.
\n
\n
\n
Inflection point #2:
\n
The BCI App Store
\n
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.
\n
\n
Opportunity Space 2: Biologically Inspired Intelligence (NeuroAI)
\n\n
NeuroAI seeks to use insights from biological intelligence to build more capable, efficient, and accessible AI systems.
\n
Rather than treating the brain as merely an object of study, NeuroAI treats it as a source of architectural, representational, and algorithmic inspiration. NeuroAI harnesses AI tools like large-scale data collection, simulation, benchmarking, and predictive models to better understand the brain and how it operates so efficiently.
\n
Why it matters
\n
Modern AI carries enormous computational and energy cost.
\n
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 and align the next generation of AI.
\n
Areas we find particularly promising include:
\n
\n
Large-scale neural data collection and analysis
\n
Neural foundation models
\n
Brain-inspired learning algorithms
\n
Neuromorphic hardware and efficient computing architectures
\n
\n
Progress so far / case studies of momentum
\n
Using neuroscience to improve AI depends on data that connects neural structure to function, and models that can extract useful patterns from those observations. Recent work is making both more accessible to researchers, with shared datasets and predictive models that allow theories of biological computation to be tested at larger scales.
\n
\n
On neural data, the MICrONS project paired activity recordings from roughly 75,000 neurons with a detailed reconstruction of the same region of mouse visual cortex. Researchers can examine how neurons respond to visual stimuli alongside the connections between them. This makes it possible to test how wiring relates to computation, and to identify principles that could inform artificial neural networks.
\n
On neural foundation models, Meta’s TRIBE v2 predicts human brain responses to sights, sounds, and language. Its ability to predict fMRI responses in new subjects and tasks gives researchers a way to test hypotheses computationally before running additional human experiments.
\n
\n
\n
Inflection point #1:
\n
Neural Distillation
\n
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.
\n
\n
\n
Inflection point #2:
\n
The Neuromorphic Energy Pivot
\n
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.
\n
\n
Opportunity Space 3: Whole Organism Emulation (WOE)
\n\n
Whole organism emulation (also known as Whole Brain Emulation (WBE)) seeks to create computational models that reproduce the behavior of biological organisms using detailed neural and biological data.
\n
While often discussed as science fiction, the field is increasingly becoming an engineering challenge shaped by advances in connectomics, imaging, simulation, and neuroscience.
\n
Why it matters
\n
A successful emulation system would provide an unprecedented tool for understanding intelligence, learning, memory, and behavior.
\n
It could also dramatically accelerate neuroscience by enabling experiments that are difficult (or impossible) to perform in living systems.
\n
Promising areas include:
\n
\n
High-throughput connectomics
\n
Brain reconstruction pipelines
\n
Neuromechanical simulation
\n
Memory and behavior modeling
\n
\n
Progress so far / case studies of momentum
\n
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.
\n
\n
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.
\n
On reconstruction, the binding cost at mouse scale is human proofreading hours. Janelia's 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.
\n
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.
\n
\n
\n
Inflection point #1:
\n
Memory Retrieval in Simulation
\n
The most catalytic milestone may be surprisingly simple.
\n
If a reconstructed brain or organoid can be simulated and reliably reproduce a specific learned behavior or memory from its biological counterpart in a virtual environment, whole-brain 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.
\n
\n
\n
Inflection point #2:
\n
Mouse Brain Connectome
\n
Mapping and emulating a whole mouse connectome would be a crucial stepping stone to understanding how mammalian brains process information, and how physical connections and synapses translate into real-time brain activity, perception, and behavior. It would also provide a platform for studying mammalian synaptic plasticity—how experience changes the strength and organization of connections between neurons—and how these changes enable learning. Finally, an effort at this scale would also solidify methods and drive cost-curve improvements to make human-brain mapping feasible.
\n
\n
PL Neuro: Building the Neurotech Field
\n
Breakthroughs do not emerge from technology alone. They require healthy ecosystems that connect research institutions, domain experts, capital allocators, startup founders, and more.
\n
Today, neurotechnology faces fragmentation across disciplines, institutions, and stakeholder groups. Researchers, founders, funders, policymakers, and engineers often operate in separate communities despite working toward related goals.
\n
Building the field itself is therefore a critical leverage point. Our field-building strategy centers on:
\n
\n
Shaping field narrative, identity, momentum, and alignment
\n
Developing shared milestone targets, roadmaps, benchmarks, and success criteria
\n
Connecting talent networks and catalyzing cross-disciplinary collaborations
\n
Advancing principles for human-flourishing such as privacy, governance, openness, and agency
\n
Increasing public understanding and institutional engagement
\n
\n
A stronger ecosystem increases the likelihood that advances in BCI, NeuroAI, and emulation reinforce one another rather than developing in isolation.
\n
Looking Ahead
\n
The coming decade could determine whether neurotechnology remains a niche scientific endeavor or becomes one of the defining technological frontiers of the century. We believe the field is approaching several important inflection points:
\n\n
10,000 invasive high-bandwidth neural implants in humans
\n
100,000,000 hours of human neural data
\n
A whole brain mouse connectome completed
\n\n
Reaching them will require coordinated effort across research, entrepreneurship, policy, infrastructure, and capital. PL R&D exists to help accelerate that coordination.
\n
Get Involved
\n
We're actively building relationships with researchers, founders, funders, policymakers, and technologists working across neurotechnology, NeuroAI, connectomics, and related fields. You can see our latest work (including roadmaps, podcasts, and more) at plneuro.xyz.
\n
If you're interested in contributing to the future of neurotechnology or would like to attend a future PL Neuro event, we'd love to hear from you: research@protocol.ai
\n
Follow PL R&D and Protocol Labs for future publications, field maps, convenings, and opportunities to participate in the ecosystem as it continues to grow.
\n
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.
\n",
+ "html": "\n
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 breakthroughs within the fields of Neuroscience & Neurotechnology.
\n
Advances in neuroscience, brain-computer interfaces (BCIs), biologically inspired AI, and whole-brain 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.
\n
PL Neuro exists to break bottlenecks: to define the milestones neurotech and NeuroAI need, drive the research that shifts what's believed possible, and route talent and capital to some of humanity's hardest and most important problems.
\n
We focus on three opportunity spaces that could reshape both neuroscience and computing over the coming decade:
\n
\n
Neural Augmentation (Brain-Computer Interfaces)
\n
Biologically Inspired Intelligence (NeuroAI)
\n
Whole Organism Emulation (WOE)
\n
\n
Together, these areas form a loop: advances in neuroscience (via augmentation and mapping) generate new data and understanding; those insights enable more capable AI systems and neurotechnologies (NeuroAI applications); and progress across both creates entirely new possibilities for augmenting human cognition (improved augmentation & emulation).
\n
\n\n
\n
Opportunity Space 1: Neural Augmentation (BCI)
\n\n
Neural augmentation focuses on building high-bandwidth, bidirectional interfaces between brains and computers.
\n
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 that extend human capability.
\n
Why it matters
\n
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 access and innovation.
\n
Key areas of interest include:
\n
\n
Higher-bandwidth neural interfaces
\n
Less invasive and more scalable devices
\n
Improved implantation and deployment infrastructure
\n
Open software ecosystems built on safe, secure neural hardware
\n
\n
Progress so far / case studies of momentum
\n
Making neural interfaces useful beyond a small number of research participants requires progress on several fronts: how much information they can carry, how directly they interact with neural tissue, and how they reach patients through clinical care. Recent developments show movement on each, although the devices are at different stages of clinical and commercial development.
\n
\n
On bandwidth, Paradromics received FDA approval for the Connect-One Early Feasibility Study of its fully implantable Connexus BCI. The system records activity from individual neurons and is designed to translate neural activity into speech and computer control. The study provides a way to test whether its high-data-rate interface can deliver safe, reliable benefits for people with severe motor impairment over sustained use.
\n
On invasiveness, Precision Neuroscience received FDA clearance for its Layer 7 Cortical Interface, a high-resolution electrode array that sits on the brain’s surface. The clearance permits recording, monitoring, and stimulation for up to 30 days, extending use beyond brief recordings during surgery. It creates more scope for clinical research and neural data collection, while the company’s fully implantable wireless BCI remains in development.
\n
On clinical deployment, Science Corp. announced the European commercial launch of PRIMA after receiving CE marking. The retinal implant uses electrical stimulation to restore functional central vision in people with geographic atrophy caused by age-related macular degeneration. Commercial authorization creates a route beyond clinical trials; reimbursement and clinical-site rollout are the next practical steps toward wider access.
\n
\n
\n
Inflection point #1:
\n
Clinical BCI Superpower
\n
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 - creating demand for BCIs beyond clinical applications to access these superpowers. This demand helps reach a major inflection point for BCI access and utility: 10,000 high-bandwidth neural implants in humans.
\n
\n
\n
Inflection point #2:
\n
The BCI App Store
\n
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.
\n
\n
Opportunity Space 2: Biologically Inspired Intelligence (NeuroAI)
\n\n
NeuroAI seeks to use insights from biological intelligence to build more capable, efficient, and accessible AI systems.
\n
Rather than treating the brain as merely an object of study, NeuroAI treats it as a source of architectural, representational, and algorithmic inspiration. NeuroAI harnesses AI tools like large-scale data collection, simulation, benchmarking, and predictive models to better understand the brain and how it operates so efficiently.
\n
Why it matters
\n
Modern AI carries enormous computational and energy cost.
\n
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 and align the next generation of AI.
\n
Areas we find particularly promising include:
\n
\n
Large-scale neural data collection and analysis
\n
Neural foundation models
\n
Brain-inspired learning algorithms
\n
Neuromorphic hardware and efficient computing architectures
\n
\n
Progress so far / case studies of momentum
\n
Using neuroscience to improve AI depends on data that connects neural structure to function, and models that can extract useful patterns from those observations. Recent work is making both more accessible to researchers, with shared datasets and predictive models that allow theories of biological computation to be tested at larger scales.
\n
\n
On neural data, the MICrONS project paired activity recordings from roughly 75,000 neurons with a detailed reconstruction of the same region of mouse visual cortex. Researchers can examine how neurons respond to visual stimuli alongside the connections between them. This makes it possible to test how wiring relates to computation, and to identify principles that could inform artificial neural networks.
\n
On neural foundation models, Meta’s TRIBE v2 predicts human brain responses to sights, sounds, and language. Its ability to predict fMRI responses in new subjects and tasks gives researchers a way to test hypotheses computationally before running additional human experiments.
\n
\n
\n
Inflection point #1:
\n
Neural Distillation
\n
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.
\n
\n
\n
Inflection point #2:
\n
The Neuromorphic Energy Pivot
\n
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.
\n
\n
Opportunity Space 3: Whole Organism Emulation (WOE)
\n\n
Whole organism emulation (also known as Whole Brain Emulation (WBE)) seeks to create computational models that reproduce the behavior of biological organisms using detailed neural and biological data.
\n
While often discussed as science fiction, the field is increasingly becoming an engineering challenge shaped by advances in connectomics, imaging, simulation, and neuroscience.
\n
Why it matters
\n
A successful emulation system would provide an unprecedented tool for understanding intelligence, learning, memory, and behavior.
\n
It could also dramatically accelerate neuroscience by enabling experiments that are difficult (or impossible) to perform in living systems.
\n
Promising areas include:
\n
\n
High-throughput connectomics
\n
Brain reconstruction pipelines
\n
Neuromechanical simulation
\n
Memory and behavior modeling
\n
\n
Progress so far / case studies of momentum
\n
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.
\n
\n
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.
\n
On reconstruction, the binding cost at mouse scale is human proofreading hours. Janelia's 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.
\n
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.
\n
\n
\n
Inflection point #1:
\n
Memory Retrieval in Simulation
\n
The most catalytic milestone may be surprisingly simple.
\n
If a reconstructed brain or organoid can be simulated and reliably reproduce a specific learned behavior or memory from its biological counterpart in a virtual environment, whole-brain 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.
\n
\n
\n
Inflection point #2:
\n
Mouse Brain Connectome
\n
Mapping and emulating a whole mouse connectome would be a crucial stepping stone to understanding how mammalian brains process information, and how physical connections and synapses translate into real-time brain activity, perception, and behavior. It would also provide a platform for studying mammalian synaptic plasticity—how experience changes the strength and organization of connections between neurons—and how these changes enable learning. Finally, an effort at this scale would also solidify methods and drive cost-curve improvements to make human-brain mapping feasible.
\n
\n
PL Neuro: Building the Neurotech Field
\n
Breakthroughs do not emerge from technology alone. They require healthy ecosystems that connect research institutions, domain experts, capital allocators, startup founders, and more.
\n
Today, neurotechnology faces fragmentation across disciplines, institutions, and stakeholder groups. Researchers, founders, funders, policymakers, and engineers often operate in separate communities despite working toward related goals.
\n
Building the field itself is therefore a critical leverage point. Our field-building strategy centers on:
\n
\n
Shaping field narrative, identity, momentum, and alignment
\n
Developing shared milestone targets, roadmaps, benchmarks, and success criteria
\n
Connecting talent networks and catalyzing cross-disciplinary collaborations
\n
Advancing principles for human-flourishing such as privacy, governance, openness, and agency
\n
Increasing public understanding and institutional engagement
\n
\n
A stronger ecosystem increases the likelihood that advances in BCI, NeuroAI, and emulation reinforce one another rather than developing in isolation.
\n
Looking Ahead
\n
The coming decade could determine whether neurotechnology remains a niche scientific endeavor or becomes one of the defining technological frontiers of the century. We believe the field is approaching several important inflection points:
\n\n
10,000 invasive high-bandwidth neural implants in humans
\n
100,000,000 hours of human neural data
\n
A whole brain mouse connectome completed
\n\n
Reaching them will require coordinated effort across research, entrepreneurship, policy, infrastructure, and capital. PL R&D exists to help accelerate that coordination.
\n
Get Involved
\n
We're actively building relationships with researchers, founders, funders, policymakers, and technologists working across neurotechnology, NeuroAI, connectomics, and related fields. You can see our latest work (including roadmaps, podcasts, and more) at plneuro.xyz.
\n
If you're interested in contributing to the future of neurotechnology or would like to attend a future PL Neuro event, we'd love to hear from you: research@protocol.ai
\n
Follow PL R&D and Protocol Labs for future publications, field maps, convenings, and opportunities to participate in the ecosystem as it continues to grow.
\n
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.
![\"Neurotechnology:](\"/images/blog/neurotechnology-hero.webp\")
![\"Neural](\"/images/opportunity-spaces/neural-augmentation.jpg\")
![\"Biologically](\"/images/opportunity-spaces/biologically-inspired-intelligence.jpg\")
![\"Whole-Organism](\"/images/opportunity-spaces/whole-organism-emulation.jpg\")