Every year, I put together a year's review of what's going on in the crazily evolving world of brain chips and #BCI's.

2025 was huge for #Neuralink, but we saw many other trends.

Here are my top-10 trends of the year and what to watch for in 2026:

#10 Massive Capital Influx:

2025 saw unprecedented investment confidence, headlined by Neuralink’s $650M Series E. 

With ARK Invest and Sequoia joining in, it’s clear the market now views brain chips as the next major tech platform.

https://x.com/neuralink/status/1929604804411904096

#9 Germany:

Went under the radar but Germany implanted its first BCI participant for motor recovery with penetrating electrodes this year. This is unique in Europe.

Also, Germany's CorTec had its first implant (in a US center.)

https://www.linkedin.com/posts/mirmi-tum_robotisinstitutegermany-robotics-ai-activity-7391408327223848961-hkQ2/

#8 The Convergence of AI and Neuroscience:

The lines between "software" and "brain" continued to blur in 2025.

Patrick’s report from NeurIPS shows how the AI community and neuro-labs are beginning to working as one cohesive ecosystem.

https://www.linkedin.com/posts/pmineault_survived-neurips-onwards-to-montreal-ai-activity-7403918222841847808-9Npk/

#7 Global Expansion of BCI Clinical Trials:

We saw more clinical trials move beyond the US this year.

The launch of the UAE-PRIME trial in Abu Dhabi marks the beginning of a global race to bring thought-controlled devices to patient populations.

https://www.linkedin.com/posts/neuralink_were-launching-our-first-clinical-trial-activity-7328425578246660096-PwNS

#6 The Renaissance of Dexterous Robotic Hardware:

Brain signals are only half the battle; 2025 was the year the hardware caught up. 

Mehrdad Farimani’s mapping of five-finger robotic hands shows how humanoids are pushing the dexterity frontiers.

https://www.linkedin.com/posts/hmorvaridifa_humanoid-dexterousabrhand-robotics-activity-7404522096908931073-YOcF/

#5 Mainstream Acceptance as "Transformative Medicine":

The "sci-fi" stigma evaporated in 2025.

As reported by TIME, the medical establishment and the public now officially recognize BCIs as a real, life-changing pillar of future medical treatment.

https://time.com/7330887/brain-computer-chips-future-of-medicine/

#4 Science Corp:

Science surprised this year with their light-speed clinical trials, and demonstrated how close they are to commercial availability.

Science Corp's Vision Patient Registry is now open!

https://science.xyz/news/science-patient-registry-announcement/

#3 Neuralink's Employees:

We wouldn't be here if it weren't for Neuralink's talent.

Not only have they expanded their clinical trial massively, but also, their 2025 tour shows the industry is now focused on building a long-term force to scale operations.

https://www.youtube.com/watch?v=QJdgHXyJh7M

#2 Paradromics:

Paradromics really stole the show this year.

With NEOM funding, great communications, and securing IDE approval for the Connexus BCI, Paradromics proves that high-data-rate implants are moving steadily long-term.

https://www.linkedin.com/feed/update/urn:li:activity:7397259238638559232/

#1 Patients and Participants:

2025 was undoubtedly the year of patients. 

With Neuralink nearing 20 participants, and leveraging on 20 years of industry experience (14 BrainGate participants, plus at least 20 others), brain-computer interfaces are beginning to prove they're ...

not merely temporary experiments. They are poised to become reliable, lifelong solutions. 

Thanks for this, folks.

https://x.com/WillettNeuro/status/1940605924416409797

I hope you found this thread helpful.

In 2026, I'll keep updating you on the world of brain chips and expand operations with informative posts about top brain understanding research.

In the long term, I am dedicated to understanding the brain to help improve brain chips.

Brain Chip Trends 2025

The new Titans architecture shows how badly Google wants to go back to recurrence.

Why? Recurrence is damn cheap.

This paper shows how the brain does it.

Striatum, working with cortex, HPC, BG, and thalamus, in recurrent loops and shortcuts.

Here is my toy model and notes:

This paper proposes a new way to see the brain's core architecture and compares it to transformers.

The canonical view suggests goal-directed and habitual systems are separate and compete for control.

This is the classic and often abused dual view of the brain:

System I (habits+skills/fast)
System II (complex decisions/slow) 

This review argues this view is wrong, and behaviors are actually one integrated continuous network of interacting loops.

Core result:

They propose that behavior emerges dynamically from interacting corticobasal ganglia-thalamocortical loops.

Habits are simply anatomical shortcuts within this network that bypass slower processing.

Let's unpack this:

Behavior is not a binary choice between distinct systems but exists on a continuous spectrum from automatic habits 🔄 to complex goals 🎓.

Both extremes emerge from the same integrated neural architecture 🧠.

The old view maps System I and II to independent motor-striatal and pfc-striatal pathways.

The new view proposes there is a bunch of loops for things like motivation 🟪, planning 🟦🟩, and movement 🟥.

Behavior is done by these loops, running in parallel and feeding each other.

These loops are not isolated like the old view, but connected via "cross-talk" ↔️

Cross-talk mixes signals from all loops at all stages.

This allows motivational context to dynamically influence planning and motor output.

Habits are then just anatomical shortcuts ⚡, allowing loop signals to bypass computationally expensive cognitive processing 🐢. 

This enables e.g. the limbic context to trigger motor actions directly, increasing efficiency (but reducing flexibility).

All our behavior starts on raw, motivation and stored cognitive maps (HPC/limbic).

This flows to cognitive areas that concretize these plans.

Finally, motor loops do the dirty job.

Cross-talk provides context mixing information from all loops.

What's the brain secret weapon?

The striatum (ST).

ST integration hubs 🧠 do the cross-talk, analog to the self-attention mechanism🤖.

Both systems weigh converging context inputs to extract relevant meaning 💡.

But Brain does it in a single loop instead of 100x layers.

Here is another way to put it: the striatum acts like a biological search engine.

It weighs massive inputs to find relevant context, just like "self-attention" mechanisms in modern AI.

That's the gist of it.

But why?

Previous dual-system models failed to explain how goals seamlessly blend with habits.

They investigated this to reconcile behavioral flexibility with the rigid anatomy of known circuits.

This is how we learned that from the paper:

They argue that limbic, cognitive, and motor loops are not isolated tracks.

Instead, they share information through mutual crosstalk and overlapping terminal fields (Figure 1A).

They proposed that habits arise when connections bypass the slow cognitive loops.

Their model showed these shortcuts connect limbic inputs directly to motor outputs (Figure 1B-D).

They claim striatal hubs integrate context like AI self-attention mechanisms do.

Converging inputs allow the striatum to create new meaning from scattered signals (Figure 2B-D).

They identified the hippocampus as the generator of potential future sequences.

These "preplay" sequences feed into the loops to initiate goal selection (Figure 1A).

Finally, they propose behavior isn't binary but exists on a dynamic continuum.

The degree of shortcut usage determines where an action falls between goal and habit (Box 1).

Implications:

This reframes addiction and disorders not as broken systems, but as network pathologies.

It also suggests energy-efficient AI could be built using similar "lazy" shortcut architectures.

Limitations:

The precise anatomical routes for these shortcuts still need experimental verification.

It also remains unclear exactly how the brain governs the reliance on these shortcuts.

Link to the paper:
https://doi.org/10.1016/j.tins.2025.09.006

Link to the Titans blog post:
https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/

Disclaimers:

1) This is a simplification from a very complex paper. Sorry if I omitted some details, it is all for clarity 😸

2) As any paper, this study has to be further REPLICATED to know if its true.

Brain Architecture vs. Transformers

The hippocampus is not a library, it is a simulation engine.

HPC is known for storing maps of the environment but not so known for generating planned trajectories.

This paper proposes that recurrence in CA3 is crucial to produce those plans.

Here is my toy model and notes:

The traditional view treats the hippocampus as a passive archive of spatial maps.

This new perspective suggests it actively generates predictions to guide future behavior.

Core result:

They demonstrated that a recurrent neural architecture helps hold those plans in mind.

This architecture is essential for navigating uncertain environments.

Let's unpack this:

Agents and rats faced navigation tasks where crucial sensory cues vanished before decision points.

Success required maintaining past information over time to guide future actions under uncertainty.

Here is the toy task to understand it: 

A mouse 🐭 navigates a partially observable maze where cues 🔑 are visible only briefly at the start before vanishing.

To find the reward 🧀, the brain cannot rely on current vision but must use internal memory to bridge the gap.

The study compares a standard network to one mimicking the hippocampus 🧠, specifically the CA3 🟩 layer's recurrent loops 🔄.

These loops allow the model 🤖 to recirculate information, keeping the memory of the cue "alive" while the agent moves through the maze.

They two different maze tasks: 

An "Allocentric" 🟥  (based on the outside world) task and an "Egocentric" 🟦 (based on the body) task.

The egocentric task was easy: it was just remembering to turn left every time.

The allocentric task was harder: depending on the starting point, the mouse/agent should decide what direction to turn and has to remember the context cues to orient itself.

It had to think and plan more.

Using demixed PCA 🔍, they showed that the recurrent agent 🟩 was able to separate between an "Allocentric" 🟥 and "Egocentric" 🟦 strategies in its neural activity.

The simple agent 🟧 showed a confused mess, proving it cannot hold the strategy in mind.

The internal dynamics of the artificial agent 🤖 spontaneously aligned with the real mouse 🐭 recordings. 

Both systems sustain a stable strategy signal 🟥 over time.

The recurrent loops 🔄 allow the agent to  pause ⏸️, and project a path through the fog, actively predicting the goal rather than just reacting.

The HPC seems not to be just a passive library, but an active "simulation engine" that can hold future paths thanks to recurrence.

The recurrent architecture enables the agent to deliberate at decision points by projecting potential outcomes.

It actively simulates future paths based on past context to navigate uncertainty.

Put another way, the hippocampus does not merely retrieve static archival records.

Instead, it actively runs generative simulations to predict future states based on current context.

That's the gist of it.

But why?

Previous theories primarily viewed the hippocampus as a repository for episodic memories.

However, this static view failed to explain how animals flexibly plan behavior in dynamic, partially observable environments.

But how?

The advance came from combining complex, partially observable navigation tasks with deep reinforcement learning agents.

This enabled direct comparison between artificial neural activity and biological recordings.

This is how we learned that from their results:

They demonstrated that standard feedforward networks failed completely on tasks requiring memory across temporal gaps.

Only the recurrent agent successfully bridged past cues with future actions (Figure 2d).

Analyses revealed that the recurrent agent’s latent neural dynamics strongly resembled biological recordings.

Both exhibited distinct, stable components encoding task strategy and temporal progression (Figure 3a-b).

The recurrent model exhibited superior generalization capabilities when facing novel situations.

It maintained robust performance despite unseen manipulations like extended maze lengths or added distractors (Figure 8a-b).


The recurrent agents spontaneously developed deliberative behaviors characterized by pausing at decision points.

This "vicarious trial and error" mirrors typical rodent behavior during uncertain navigation (Figure 4b).

Interestingly, agents trained under partially observable conditions developed more robust internal models.

Seeing less of the world forced the network to rely on deeper cognitive maps (Figure 5a-b).

Implications:

These findings suggest hippocampal recurrence evolved specifically to manage environmental uncertainty.

It also adds more evidence that the region also predicts the future.

Limitations:

This paper doe snot describe yet how these plans form.

Furthermore, the artificial neural network remains a simplified abstraction of complex biological circuitry.

Link to the paper:

https://doi.org/10.1038/s41467-025-64591-9

Hippocampus: Simulation Engine

Decisions under uncertainty are a coordinated action of PFC, pre/motor, BG, and thalamus.

This model could explain decision-making and impairments by hyperactive D2 receptors.

We can clarify the paper by building a mouse with schizophrenia.

Here is my toy model and notes:

Traditionally, the basal ganglia and cortex were seen as separate systems for habit and planning.

This study proposes the thalamus acts as a critical bridge, orchestrating how these systems interact to handle uncertainty.

Core result:

They found the brain uses specialized circuits for different types of uncertainty, linked hierarchically by the thalamus.

Disrupting this link mimics schizophrenia symptoms, causing the mouse to perceive volatility where none exists.

Let's unpack this:

Imagine a mouse in a T maze with a red 🟥 and a blue 🟦 side.

In this world the rules flip between:

Context A: get a reward 70% of the times if go 🟥

Context B: gets a reward 70% of the times if go 🟦.

The mouse faces two puzzles: 

which arm pays out right now?, and 

have the hidden rules changed?

A "Gambler" circuit is formed by:

The basal ganglia "notebook": stores payout probabilities as full distributions, not just averages.

Premotor cortex: samples from these notes to explore options when the outcome is uncertain.

Motor cortex: compares values and takes action.

A "Detective" circuit in the PFC tracks clues to infer the current context or "hidden state".

PFC helped by the mediodorsal (MD) thalamus "belief keeper" who uses attractor dynamics to hold a stable belief until enough evidence pushes it to a new conclusion.

The Detective acts as a master switch, engaging the specific Gambler circuit that matches the current context.

This hierarchy allows the mouse to instantly swap strategies without unlearning everything when the world changes.

E.g. PFC has evidence of a wrong choice.

Here's what could be happening in schizophrenia:

Hyperactive D2 receptors in BG unbalance activity in Thalamus, making the "Belief keeper" unstable.

This results in a unstable attractor, sensitive to noise and that cannot hold onto evidence.

This causes the mouse to perceive constant volatility, leading to erratic switching even after successful choices.

This behavior has been observed in schizophrenia subjects. 

Here is another way to put it:

A broken thermostat makes a furnace switch on and off wildly.

By damaging the thalamic detector, the brain loses its stability and hallucinates change in a stable world.

That's the gist of it.

But why?

Previous models were either too abstract to map onto biology or too opaque to understand.

Researchers needed a bridge between mathematical theory and the messy reality of neurons and synapses.

But how?

They built "CogLinks," a model using realistic neurons that approximates ideal mathematical algorithms.

This allowed them to mathematically prove the biological circuit performs near-optimal decision-making.

This is how we learned that from their results:

They showed the basal ganglia encodes uncertainty as a probability distribution used for exploration (Fig 2f).

Synapses representing different value quantiles allowed the model to balance risk and reward efficiently.

They demonstrated that the PFC-MD circuit learns a generative model to infer hidden contexts (Fig 4c).

This circuit maintained a low-dimensional representation of likelihood that tracked the changing environment.

The biological model consistently outperformed standard "ideal" algorithms like Thompson Sampling (Fig 3a-c).

Biological heuristics like dynamic sparsity modulation allowed for faster adaptation than rigid mathematical formulas.

They found that inhibitory interneurons gate learning based on the thalamus's confidence level (Fig 4).

Uncertainty suppresses plasticity to prevent errors, while certainty releases the brakes to encode new rules.

Overactive D2 receptors caused the thalamic circuit to fail, mimicking schizophrenic symptoms (Fig 7).

Restoring activity in the mediodorsal thalamus rescued the behavior, validating the circuit mechanism.

Implications:

This framework connects molecular defects directly to cognitive symptoms and behavioral issues.

It suggests future psychiatric treatments could target specific circuit dynamics rather than just global chemistry.

Limitations:

The model has not yet been validated in experiments.

It also abstracts the basal ganglia into a simple loop, omitting direct and indirect pathways.

Link to the paper:

https://doi.org/10.1038/s41467-025-63994-y

Understanding Decision-Making Systems

Where's Doomscrolling in the brain? Evolved circuits in ant. cingulate & vlPFC to seek info about bad possibilities. Helped us survive resolving uncertainty. Problem? Our brains weren't made for X. News hijack this, trapping us in a compulsive loop #neuroscience #anxiety #brain

This research suggests doomscrolling might be driven by specific brain circuits that evolved to help us deal with uncertainty.

https://medicine.washu.edu/news/study-finds-brain-areas-involved-in-seeking-information-about-bad-possibilities/

Here’s how the article explains it:

We have a Drive to Resolve Uncertainty. Our brains are wired to seek information, even about potential negative events. This likely evolved as a survival mechanism in an uncertain world.

They identified the anterior cingulate cortex and ventrolateral prefrontal cortex as key areas involved in the choice to seek information about unwanted possibilities. These circuits weigh whether we want to know the bad news or prefer to remain ignorant.

The "Information Age" Mismatch is that our brains "aren't well equipped to deal with the information age." We now have a "constant availability of information" (like endless social media feeds).

The Compulsive Loop is this constant stream of new, often negative, information may trigger our brain's ancient drive to resolve uncertainty. 

This can lead to the compulsive "checking, checking, checking" behavior of doomscrolling, as our brains are drawn to seek out every "worrisome tidbit" to try and understand potential threats, even if we have no power to prevent them.

https://www.sciencedirect.com/science/article/pii/S0896627321003536#fig1

Brain Circuits in Doomscrolling

Dopamine ≠ reward but turns out, also not the learning molecule we thought.

If DA RPE is the emperor, this work SCREAMS it was running naked all the time.

This paper got quite some attention recently. Let me explain why it is so relevant.

Here is my toy model and notes:

The canonical view is DA signals RPE, a "teaching signal" for learning.

This paper argues we were wrong all the time: DA is a "performance signal" that controls the vigor and direction of movement and it is only indirectly related to learning by controlling effort.

For years, neuroscientists have scratched their heads about dopamine because it seems to do so much.

This paper might finally explain what have we been doing wrong all the time in experiments. 

It also matches alternative evidence that has been accumulating over the years.

This paper says that all those observations we thought were related to the RL inspired Reward Prediction Error (RPE), are telling us something else.

Although mathematically beautiful, this theory might have nothing to do with the brain reality.

Or at least not with DA.

Let's unpack this:

The classical RPE model says DA neurons encode the *difference* between expected and actual rewards.

A surprise spikes DA activity, while disappointment causes a dip.

This helps a mouse 🐭 learn what to pay attention to.

In RPE, the DA signal shifts from reward to cue 🔔 over time.

Why does the DA signal "move" to the cue?

RPE view: This helps an animal learn what things are important.

New view: the signal shifts because the animal's *action* (pushing forward) shifts to the cue.

Why does DA firing "dip" when a reward is omitted?

RPE view: The famous "dip" in DA when a reward is omitted is a negative prediction error.

New view: The dip simply reflects the animal *abruptly stopping* its forward movement.

Why does a bigger reward cause a bigger DA spike?

RPE view: A larger reward causes a larger DA spike because it's a bigger "positive error."

New view: A larger reward makes the mouse push *harder*, and the DA spike just tracks that *vigor*.

How does reward probability change the DA signal?

RPE view: DA activity scales with reward probability, encoding the *strength* of the prediction.

New view: Probability changes the animal's *effort*, and the DA signal simply tracks that performance.

Here is another way to put it: We thought DA was the GPS, telling the brain when to change direction when we did a wrong turn.

This paper suggests DA is the *accelerator*, just making the car go.

But why?

The RPE hypothesis has been a cornerstone of neuroscience, explaining associative learning.

But it couldn't explain why DA activity also correlates with movement kinematics or aversive events.

But how?

The breakthrough was using highly sensitive force sensors on head-fixed mice.

This allowed them to measure subtle, continuous movements, not just discrete licks or port entries.

This is how we learned that from their results:

They identified two distinct DA neuron types: "Forward" and "Backward" populations.

These cells fire to drive movement in a specific direction (Fig 1e, 1h, 1k).

This force-tuning was independent of reward, appearing during spontaneous movements.

It even held true during an aversive air puff, proving it's not about "reward" (Fig 3e, 3h).

Changes in DA firing due to reward magnitude, probability, and omission were all explained by parallel changes in the *force* the mice exerted (Figs 4, 5).

Optogenetically stimulating DA neurons in place of reward was *not sufficient* for learning.

Inhibiting DA neurons during the task *did not impair* learning (Fig 10b, 10i).

DA is an adaptive signal that switches its function in real-time.

Upon reward delivery, it stops predicting force and starts predicting the *rate of licking* (Fig 9h, 9j).

Implications for neuro:

This challenges a foundational dogma about dopamine and learning.

It shifts focus from abstract cognitive states to the concrete physical actions and motor vigor that DA might directly encode.

Limitations:

The study used head-fixed mice, which restricts their natural movement repertoire.

The findings must also be tested in more complex, non-Pavlovian tasks.

Disclaimers:

1) This is a simplification from a very complex paper! Sorry if I omitted some details, it is all for clarity 😸

2) As any paper, this study has to be further REPLICATED to know if its true!

Revisiting Dopamine's Role

New habits move from the Prefrontal Cortex (conscious effort) to the Basal Ganglia (habit loop). A trick is to automate the first 30 seconds of a habit, minimizing the PFC's required 'energy' for initiation until the Basal Ganglia takes over. #LifeHacks #SelfImprovement #Brain

https://people.duke.edu/~hy43/role%20of%20basal.pdf


Automate Habits for Success

To plan the future, the PFC represents a step-by-step map of actions, and at every step, this plan moves to the past like a conveyor belt.

This proposes a super simple neural-subspace architecture for planning.

A toy environment can clarify it.

Here is my toy model and notes:

The old view saw planning as a slow, sequential search, like exploring a maze path by path.

This paper proposes a fast, parallel inference, where the optimal plan emerges all at once.

Core result:

The main result is a "Spacetime Attractor", a neural circuit that infers optimal future plans.

It does this by embedding the rules of the environment directly into its synaptic connections.

Let's unpack this:

The model assumes the PFC is split into different "subspaces," each representing a different point in the future.

One group of neurons maps "NOW (0)," 🟩 another maps "NEXT (1)," 🟦 and another maps "LATER (2),"🟪 all active simultaneously.

These subspaces are connected based on the environment's rules, like a 'world model' etched into the wiring.

Synapses from "NOW" 🟩 to "NEXT" 🟦 only exists if the agent can *actually* move between those two locations.

Possible move: excitatory
Not possible move: inhibitory

Planning happens when inputs, like the 'start' and 'goal', are applied to this network.

The circuit activity then rapidly "relaxes" into the most stable pattern, which *is* the optimal path.

In each subspace, only the units representing the locations in the plan are active.

As the agent takes the first action, the entire plan representation shifts forward in time.

What was in the "NEXT" 🟦 subspace moves to the "NOW" 🟩 subspace, readying the next action without re-planning.

Conveyor belt dynamics, they call it.

But how does this adapt to new mazes? 

Training nets to challenging environments, they found nets learn scaffolds of all possible actions instead of locations.

When it sees a new wall, sensory input just "blocks" or inhibits that specific action, making the plan flexible.

Here is another way to put it: The brain builds a master map of all possible moves, like "go north" or "go east" instead of "north" and "east".

Seeing a new wall is like the brain telling itself, "Okay, the 'go north' option is not available."

Instead of names it learns verbs.

But why?

We’ve known the prefrontal cortex is crucial for planning, but how its circuits actually compute a plan was a mystery.

This study provides a testable, mechanistic theory for how neurons can represent and infer future paths.

This completes a beautiful picture of how learning/adaptation could happen in the 🧠:

Striatum: temporal-difference learning. Slow, habits🐢

Hippocampus: successor-representation learning. OK fast🐇, memories. 

PFC: space-time attractor. Very fast🚀, reconfigures brain.

This is how we learned that from their results:

The Spacetime Attractor (STA) model successfully solves complex, dynamic tasks that other models fail (Fig 3D-E).

It excels when rewards change *within* a trial, like intercepting a moving target.

Recurrent Neural Networks (RNNs) trained to solve these problems learned the STA solution on their own (Fig 4C).

They developed the same "conveyor belt" dynamics, suggesting it's an efficient solution (Fig 4E).

The trained RNNs explicitly learned the maze rules—the 'world model'—in their synaptic weights (Fig 5B).

The connections between subspaces representing future steps perfectly matched the maze's adjacency matrix (Fig 5E).

For new mazes, the RNNs adapted by learning *transitions* instead of fixed locations (Fig 6C).

Sensory input about a wall specifically inhibited the neural representation for that impossible transition (Fig 6H).

This work unifies planning with sequence working memory.

The same neural architecture used to *remember* a sequence can be used to *infer* a plan (Fig 2B).

Implications for AI:

This suggests AI could be improved by moving beyond brute-force computation.

Building in structured 'world models' and attractor dynamics could lead to more flexible and efficient planning agents.

Limitations:

The model was tested in deterministic environments where the rules were clear, not uncertain or probabilistic.

It also models a solitary agent, not the complex social planning needed to interact with other agents.

Disclosure: this is a simplification from a very complex paper!

Sorry if I omitted some details, it is all for clarity 😸

Paper: https://doi.org/10.1101/2025.09.23.677709

Spacetime Attractor Model

To stop unwanted thoughts, the PFC sends a control signal to the hippocampus (brain's memory retrieval center). This activates local inhibitory interneurons (GABA), which then suppresses retrieval. Difficulties in this pathway may underlie #PTSD, #OCD, anxiety, and #depression.

https://www.nature.com/articles/s41583-025-00929-y

https://osf.io/preprints/psyarxiv/4wcnb_v1

Brain's Control of Unwanted Thoughts

Impressive (200+ bps) decoding speeds by Paradromics reported this month.

Plus updates from:
- E11 Bio
- Morgan Stanley
- Neuralink
- New England Journal of Medicine
- Precision
- Figure
- China and more.

Here's everything you need to know about BCIs this month and my opinions:

#1 E11 Bio unveils PRISM tech for brain mapping:

E11 Bio (Andrew Payne, LinkedIn) introduced PRISM, a tech combining molecular codes, AI, and light microscopes for brain wiring mapping, boosting neuron tracing accuracy.

https://www.linkedin.com/posts/ugcPost-7379154744537358336-h1iA

#2 Paradromics BCI Claims Superior Text Decoding:

Paradromics Inc. reports its Connexus® BCI achieved 200+ bps text decoding on the SONIC benchmark, claiming superior performance over other BCI types, with clear user experience differences shown.

https://www.linkedin.com/posts/paradromics-inc_bci-text-decoding-comparison-activity-7381348535901933569-eA8i

#3 Jacob Robinson highlights Morgan Stanley's BCI as platform, AI impact:

Jacob Robinson highlights Morgan Stanley's report: BCI is a platform technology, gaining from AI via a data flywheel effect. Vertical integration marks these companies.

https://www.linkedin.com/posts/jacob-robinson-75b6b0108_morgan-stanley-bci-report-ugcPost-7381753833493823491-HvLi

#4 Neuralink Submits First Human Study to NEJM for Review:

Neuralink submitted its first human BCI study to the New England Journal of Medicine for peer review. The controversial paper details results and safety outcomes from its first three patients.

https://thedebrief.org/neuralink-submits-first-human-clinical-data-for-peer-review-marking-milestone-in-brain-computer-interface-research/

#5 Neuralink's "Telepathy" Calibration for Computer Control:

DJ Seo presented Neuralink's "telepathy" calibration process. Just beautiful UI.

https://x.com/djseo/status/1976297851576598913

#6 Max Hodak Explores Consciousness Engineering at CC Tokyo:

Max Hodak shared deep insights on consciousness, including "Consciousness Engineering," during a recent talk at CC Tokyo, as reported by @kanair.

https://x.com/kanair/status/1975891166588809316

#7 Neuralink Draws Wall Street Attention, Fuels "Neuro Elite" Controversy:

Bluesky's @seeingwithsound highlights The Debrief's report on Wall Street eyeing Neuralink. This controversial piece, criticized as an infomercial, sparks debate on 'neuro elite'.

https://bsky.app/profile/seeingwithsound.bsky.social/post/3m2s3emunns27

#8 Figure 03's semi-soft robotics design confirmed by r/accelerate:

A post on r/accelerate confirms Figure 03 will feature semi-soft robotics, including a bending foot, distinguishing it from Unitree and Optimus. Launch is October 9.

https://www.reddit.com/r/accelerate/comments/1o0ivud/figure03_looks_absolutely_insaneconfirmed/

#9 Neuralink trial participant controls robotic arm:

Nick Wray, a Neuralink trial participant, showed mind-control of his assistive robotic arm. He finds his BCI experience "surreal and rewarding," as reported by @NeuraPod.

https://x.com/NeuraPod/status/1975313047578431583

#10 Precision Neuroscience unveils minimally invasive BCI:

Precision Neuroscience released its foundational report in Nature BME, detailing a high-bandwidth, minimally invasive BCI. Preclinical and first-in-human data show a safer approach.

https://www.linkedin.com/posts/precision-neuroscience-corporation_minimally-invasive-implantation-of-scalable-activity-7379485421036273664-gHSc

#11 Zhongmou ZM-02 Optogenetic Gene Therapy Patient Shares Experience:

A blind patient treated with Zhongmou Therapeutics' ZM-02 optogenetic gene therapy shares their experience. Clinical studies for Retinitis Pigmentosa are ongoing and published.

https://www.linkedin.com/posts/petermeijer_hear-our-patient-treated-with-zm-02-optogenetic-activity-7375550442644217856-g-PS

#12 Stanford's soft bioelectronic fibers published in Nature:

Researchers from Stanford published their high-density, soft bioelectronic fibers. These enable minimally invasive sensing/stimulation deep inside the body.

https://www.linkedin.com/posts/muhammad-khatib-a93167180_high-density-soft-bioelectronic-fibres-for-activity-7374206766647205888-FaUj

#13 Kral et al. Advocate Earlier Cochlear Implants for Infants:

Prof. Kral et al. analyzed cochlear implant data, finding most pediatric procedures occur in the second year. They advocate for implantation within nine months.

https://www.linkedin.com/posts/andrej-kral-md-phd-80077a49_in-our-recent-paper-httpslnkdinembijxc5-activity-7375098581050957824-ak7o

#14 BCI-Agent adds cell-type interpretability to brain recordings:

Arnau Marin Llobet et al introduced BCI-Agent, an AI tool that infers cell types from brain recordings. Validated with optotagged data, it applies to human neuropixel recordings.

https://www.linkedin.com/posts/arnau-marin-llobet_an-ai-agent-for-cell-type-specific-brain-activity-7374436149978742784-n7X9

#15 China's BCI Firms Push Implants, Eyes 2030 Dominance Goal:

Chinese firms NeuroXess and Neucyber implanted their 6th ECoG BCI participants, decoding speech and cursor control. These advances align with China's 2030 BCI dominance strategy.

https://www.linkedin.com/posts/saborg_neuroxess-just-implanted-their-6th-participant-activity-7375801073539645440-Nnh5