New Stroke Treatment Turns Brain Cavities Into Repair Hubs

 After your competent? doctor gets human testing going then s/he can create the protocols that fill those cavities with axon pathfinding and dendritic branching! Not understanding any of this IS PURE INCOMPETENCE from your doctor!

• axon pathfinding (51 posts to February 2012)

• axonal regeneration (29 posts to October 2012)

• dendritic branching (42 posts to February 2012)

  
  

  neurite outgrowth (32 posts to January 2012)

New Stroke Treatment Turns Brain Cavities Into Repair Hubs

An injectable biomaterial turned stroke-damaged areas into hubs of repair, helping mouse brains grow new blood vessels and nerve fibers while restoring near-normal movement.

A stroke can leave behind more than damaged brain cells. In severe cases, it creates an empty cavity where living tissue once carried signals, supplied blood, and controlled movement. Duke University researchers are now testing an injectable material designed to turn that biological void into a place where repair can begin.

In mice, the treatment drew immune cells into the stroke cavity and helped organize them into a coordinated healing response. New blood vessels spread through the injured area, nerve fibers became more abundant, and the animals regained motor abilities that approached those of healthy mice.

The findings were published in Cell Biomaterials. The material was injected directly into the damaged region five days after the stroke, meaning it was tested as a repair strategy rather than an emergency treatment.

Why Stroke Damage Is So Hard to Repair

Most strokes occur when a clot cuts off blood flow to part of the brain. Clot-dissolving drugs and procedures that physically remove the blockage can save threatened tissue when delivered quickly. Once brain cells have died, however, restoring circulation cannot bring them back.

A major ischemic stroke may destroy enough tissue to leave a fluid-filled cavity. Rehabilitation can train surviving brain networks to take on new roles, but medicine currently has no established way to reconstruct the missing region itself.

“Once brain tissue has been lost, restoring blood flow is no longer enough,” said Tatiana Segura, the Robert Plonsey Distinguished Professor of Biomedical Engineering at Duke. “Our goal is to engineer the injured space so that immune, vascular, and neural repair processes can begin to work together.”

Tatiana Segura. Credit: Duke University

An Injectable Scaffold for Brain Repair

Rather than trying to manufacture replacement brain tissue, Segura’s team developed a temporary framework that encourages the body to do more of the rebuilding itself.

The treatment is based on MAPS, or microporous annealed particle scaffolds. These injectable materials are assembled from tiny hydrogel particles that connect after delivery while leaving open spaces between them. Unlike a solid gel, the porous structure gives cells room to enter, move, and form new tissue. Microporous scaffolds can support cellular infiltration and blood vessel growth without waiting for the entire material to break down first.

The Duke team had previously investigated similar materials for stroke repair. In the new work, the researchers added biological instructions intended to shape the immune response inside the scaffold.

Astrocyte Signals Guide the Healing Response

Those instructions came from astrocytes, star-shaped cells that support neurons, help regulate the brain’s environment, and react rapidly to injury. Astrocytes communicate partly by releasing extracellular vesicles, or EVs. These nanoscale packages transport proteins, lipids, and genetic material between cells.

Researchers grew astrocytes in the laboratory and exposed them to different signaling molecules. They then collected the EVs produced under those conditions and tested whether the packages could attract immune cells and encourage tissue repair.

Simply releasing EVs into the damaged brain would allow many of them to disperse. To keep the signals where they were needed, the researchers chemically attached the vesicles to the hydrogel particles.

Turning the Scaffold Into a Signaling Hub

This design transformed the scaffold into more than a physical support. It became a localized signaling hub where incoming cells could repeatedly encounter molecular instructions.

“We are not simply placing a material into the brain,” Segura said. “We are engineering a local environment that can coordinate several parts of the repair response.”

EVs produced after astrocytes were exposed to IL-4 and C1q generated the strongest results. The combination attracted macrophages and a surprisingly persistent population of neutrophils into the stroke cavity.

Two-photon imaging at day 27 using Ly6G-green fluorescent protein (GFP) reporter mice to visualize neutrophils. IL-4/C1q-EV + MAPS implants exhibited dense vascularization and focal accumulation of GFP+ cells within scaffold pores. Credit: Duke University

Immune Cells Take on a Surprising Role

Neutrophils are among the immune system’s fastest responders. After a stroke, they are often associated with inflammation and additional tissue damage, especially during the early phase of injury. Yet immune cells do not always have a single fixed role. Their behavior can change depending on timing, location, and the molecular signals surrounding them.

Inside the engineered scaffold, neutrophils appeared to become part of the repair process rather than merely contributing to destruction.

The researchers tested that possibility by depleting the immune-cell population rich in neutrophils. Blood vessel formation dropped sharply, and the scaffold underwent far less remodeling. The experiment showed that these cells were not simply present at the injury site. They were helping drive the response.

“This result changes how we think about neutrophils after stroke,” said Shangjing Xin, lead scientist of the study and a postdoctoral fellow in the Segura Laboratory. “Their role appears to depend on when they arrive, where they are located, and the signals they receive from their surroundings. Our study demonstrates a potential engineering strategy to recruit and retain these cells at the right time.”

New Blood Vessels and Nerve Fibers Emerge

The treatment produced visible changes throughout the damaged region. Blood vessels grew across the cavity, potentially creating the circulation needed to support living tissue. Researchers also detected more axonal fibers within and around the injury. Axons are the long projections neurons use to carry electrical signals to other cells.

Those biological changes were accompanied by improved movement.

During a grid-walking test, scientists measured how often the mice misplaced a front paw while crossing an uneven surface. Animals treated with the optimized scaffold made fewer errors over time. By eight weeks, their performance could not be statistically distinguished from that of healthy control mice, and the improvement continued through the end of the study.

The scaffold itself proved essential. When researchers delivered the EVs without MAPS, they did not observe comparable blood vessel growth. The result suggests that the treatment depended on both components: the biological messages carried by the vesicles and the porous structure that concentrated those messages while giving cells space to organize.

Toward a Scalable Human Stroke Therapy

The study relied on EVs collected from primary rat astrocytes, which would not be a practical source for a widely available human therapy.

Segura’s laboratory is now exploring astrocytes made from human-induced pluripotent stem cells. These cells can be produced from reprogrammed adult cells and expanded in the laboratory, potentially offering a more scalable and clinically relevant source of EVs. Researchers may also be able to adjust the conditions under which the astrocytes grow to better control the messages their vesicles carry.

“You do not restore an ecosystem simply by containing the initial damage,” Segura said. “You have to create the conditions that allow life to return. That is how we think about the stroke cavity. The material is not intended to reproduce the brain itself but to create an environment where the body’s own cells can enter, communicate, and participate in rebuilding vascularized tissue.”

Reference: “IL-4/C1q activated astrocyte-derived extracellular vesicles promote stroke infarct recovery by recruiting peripheral leukocytes” by Shangjing Xin, Lucy Zhang, Nhi V. Phan, Mengying An, Ligen Shi, S. Thomas Carmichael and Tatiana Segura, 21 July 2026, Cell Biomaterials.
DOI: 10.1016/j.celbio.2026.100543

Learning Depends on Refining Existing Neural Connections

 Ask your competent? doctor EXACTLY HOW TO DO THIS POST STROKE, when those connections no longer exist! Blubbering and not answering means incompetence, so fire that doctor and get them fired!

Learning Depends on Refining Existing Neural Connections

Summary: A study demonstrates that learning in neural networks is driven primarily by adjusting the strength of existing connections rather than by continuously expanding or reconfiguring underlying network architecture.(Well, post stroke you'll need axon pathfinding and dendritic branching to occur to re-establish those needed connections! DOES YOUR DOCTOR KNOW HOW TO DO THAT?) 

The research team evaluated artificial neural networks trained on language-learning tasks across increasing volumes of training data. Despite marked improvements in performance, the trained networks maintained the capacity to lose a high proportion of synaptic connections without degrading functionality.

The findings suggest a unified computational principle across artificial and biological intelligence: cognitive learning and functional optimization rely on the refinement of existing synaptic weights and cooperative component dynamics rather than structural topological expansion.

Key Facts

• Weight Modification vs. Topological Expansion: Experimental results indicate that enhanced learning performance stems from adjusting internal connection strengths (synaptic weights) rather than adding new structural nodes or reconfiguring network topology.
• Network Pruning Resilience: Trained neural networks were able to withstand the loss of a significant proportion of synaptic connections without experiencing measurable performance degradation, demonstrating high functional redundancy and efficiency.
• Alignment with Biological Constraints: In adult biological brains, neuron counts remain approximately constant, and there is no established biological mechanism supporting continuous, large-scale structural rewiring of global network architecture during standard learning.
• Convergence of Biological and Artificial AI: The findings point toward a shared principle between biological brains and modern artificial neural networks: intelligence emerges from optimizing cooperative interaction among existing components.
• Computational Efficiency Implications: Demonstrating that architectural expansion is unnecessary for complex learning supports the development of more energy-efficient AI models and provides a physical model for biological cognitive reserve.

Source: Bar-Ilan University

How does the brain learn? Does it acquire new knowledge by creating new neural pathways, or by strengthening the connections that already exist?

A new study from Bar-Ilan University offers evidence in favor of the latter, suggesting that learning is driven primarily by changes in the strength of existing neural connections rather than by expanding the brain’s underlying architecture.

Neural networks optimize learning by adjusting the strength of existing connections rather than expanding overall network architecture. Credit: Neuroscience News

Published in Physica A, the study by Prof. Ido Kanter of Bar-Ilan University’s Department of Physics and the Gonda (Goldschmied) Multidisciplinary Brain Research Center explored this longstanding question using artificial neural networks trained on language-learning tasks.

As the amount of training data increased, the models became significantly better at learning. Surprisingly, however, the researchers found that the networks could still lose roughly the same proportion of connections (synapses) without any meaningful decline in performance. In other words, improved learning did not depend on building more complex networks. Instead, it resulted from more effective cooperation among the components that were already there.

The findings suggest that learning is achieved primarily by adjusting the strength of existing connections—known as synaptic weights—rather than by continually reorganizing or expanding the network itself.

“This finding is particularly intriguing in the context of biological brains, where the number of neurons remains approximately constant,” said Yanir Harel, an M.Sc. student at Bar-Ilan University and the study’s first author.

“Moreover, there is currently no evidence for a biological mechanism that would enable large-scale, continuous reconfiguration of neural network topology during learning.”

The research points to a possible common principle shared by biological and artificial intelligence: intelligence may emerge less from adding new components and more from improving how existing ones work together. If so, both the human brain and modern AI systems may owe much of their remarkable learning ability to the continual refinement of internal connections rather than to ever-expanding architectures.

Key Questions Answered:

Q: How did the researchers test whether learning requires new network architecture versus adjusting existing connections?

A: The researchers trained artificial neural networks on language-learning tasks with expanding datasets and analyzed performance under synaptic removal. They observed that as models improved, they could still lose a large fraction of connections without performance dropping, proving that learning relied on refined connection strengths rather than adding architectural complexity.

Q: How do these findings align with what is known about human brain biology?

A: In mature biological brains, the total number of neurons remains essentially fixed, and there is no evidence for large-scale, continuous structural rewiring of overall brain architecture during everyday learning. The study’s results support the biological reality that learning occurs through adjusting synaptic weights (synaptic plasticity) between established neurons.

Q: What implications does this research hold for future artificial intelligence design?

A: Instead of endlessly scaling up network sizes and adding parameters, AI development can focus on optimizing how existing weights cooperate and prune unnecessary connections. This approach could lead to significantly more compact, energy-efficient, and computationally effective machine learning models.

Editorial Notes:

• This article was edited by a Neuroscience News editor.
• Journal paper reviewed in full.
• Additional context added by our staff.

About this learning and neuroscience research news

Author: Elana Oberlander
Source: Bar-Ilan University
Contact: Elana Oberlander – Bar-Ilan University
Image: The image is credited to Neuroscience News

Original Research: Open access.
“NLP models: Capacity per weight—capacity per label” by Jeff R. Temple, Elizabeth Baumler, Leila Wood. Physica A: Statistical Mechanics and its Applications
DOI:10.1016/j.physa.2026.131855

Study Overturns Decades of External Axon Growth Theory

 How will your competent? doctor use this to get your needed axon pathfinding working properly AND have your doctor proves it is working!

• axon pathfinding (51 posts to February 2012)

• axonal regeneration (29 posts to October 2012)

Study Overturns Decades of External Axon Growth Theory

Summary: Researchers discovered that axon generation is actually an autonomous, internally driven process. The team proved that young nerve cells utilize an internal protein complex to methodically “unzip” their own structural scaffolding from the inside out, establishing early brain wiring via an intrinsic genetic protocol.

Key Facts

• The Intrinsic Control Shift: Rather than being passively shaped by external biological chemical trails, embryonic neurons actively remodel their own cytoskeleton (the cell’s internal structural scaffold) to trigger axon outgrowth, originating from the cell body (soma).
• The Dance of the Neurites: Early symmetric neurons exhibit small, bud-like extensions called neurites. These extensions display a highly rhythmic “two steps forward, one step back” movement behavior, constantly stretching outward and shrinking backward on a minute-by-minute basis.
• The Arp2/3 Molecular Zipper: The key to breaking this loop is a specialized protein complex called Arp2/3. Acting like a microscopic molecular zipper, Arp2/3 locally opens the cell’s tight, tension-bearing structural “corset,” allowing individual neurites to bulge outward.
• Wave-Like Propagation: This internal unzipping action travels outward through a single neurite at a time like a physical wave. The wave continues until it meets the mechanical resistance of the remaining cellular corset, which forces the neurite back into a brief rest state.
• Microtubule Lock-In: While these random, wave-driven expansions alternate between different neurites by chance, a parallel internal process is underway: rigid structural proteins (microtubules) grow outward from within. Eventually, one lucky neurite accumulates enough rigid microtubule scaffolding to resist pulling back.
• Symmetry Shattered: Once this stable tipping point is reached within roughly 48 hours, that specific neurite transitions into independent, rapid growth to become the official axon. The overall wave-driven shape-shifting stops, and the remaining neurites are locked into becoming input-receiving dendrites.
• Neurons in the brain and spinal cord form a vast network in which each cell receives many inputs but sends output through only a single, long extension: the “axon”.

“If our neurons had multiple axons, this would cause chaos in the brain,” says Professor Frank Bradke, a neurobiologist and research group leader at DZNE. 

“Nature has therefore found a clever way to make sure that neurons generate only one axon. This applies not only to humans, but across the entire animal kingdom. So, we’re dealing with very fundamental processes that shape the wiring of the brain and nervous system.”

Breaking symmetry

During early embryonic development, neurons are initially largely symmetric, exhibiting small projections known as neurites. E

ventually one of these develops into the axon, thereby breaking the symmetry. Until now, it was largely assumed that this process is determined by biological growth factors that act on a neuron from the outside – and that, much like attractants, lead to the development of the axon. The team led by Frank Bradke reaches a different conclusion.

“According to our observations, the axon forms as a result of a remodeling of the cytoskeleton initiated by the young neuron. The process originates in the cell body, the so-called soma – the very center of the neuron,” says Dr. Tien-chen Lin, first author of the current publication and a scientist at DZNE.

Young neurons display a rhythmic behavior: Their neurites stretch out somewhat, and then shrink back slightly.

“This happens on a minute-by-minute basis. In a sense, the process follows the principle of two steps forward and one step back. This sequence repeats again and again,” says Tien-chen Lin.

Within typically 48 hours, however, one of the neurites grows into an axon. The remaining neurites later develop into receptors for inputs.

“Actually, this recurring process was already known. But it was unclear what lies behind it. We have now been able to shed considerable light on the underlying mechanisms.”

The key lies in the neuron’s cytoskeleton, a tension‑bearing, molecular scaffold that acts like a corset around the cell.

“This is where a protein complex called Arp2/3 enters the picture. Our findings show that it works like a zipper, locally opening the cell’s corset,” says Tien-chen Lin. “By doing so, Arp2/3 drives the cell’s rhythmic shape-shifting, repeatedly loosening a network that would otherwise tighten up again.”

Wave-like propagation

The researchers found that Arp2/3 always acts on only one neurite at a time, temporarily enabling its growth.

“This comes with a locally confined restructuring of the cytoskeleton that spreads like a wave,” says Tien-chen Lin. “These events continue until the wave subsides because, although the cellular corset has been loosened, it still offers a certain degree of resistance. Then the process begins again. The same neurite may be affected once more, or a different one may be involved. Which one seems to be a matter of chance.”

Parallel to this outward extension, relatively rigid structural proteins grow into the neurites from within.

“Eventually, one of the neurites becomes stable enough to resist being pulled back. It can then continue growing independently of Arp2/3 and ultimately develops into the axon. Meanwhile, the overall ’wave-driven’ outgrowth comes to a stop,” says Tien-chen Lin.

Open questions

“We cannot exclude the possibility that external factors play a certain role. However, given our data, we are convinced that the basic process that drives axon growth originates within the cell itself,” says Frank Bradke.

Open questions remain: What initiates the remodeling? Why does it proceed rhythmically and one neurite at a time? And, why does remodeling stop as soon as one of the neurites has grown large enough?

“The young nerve cell presumably follows a protocol encoded in its genome. However, we do not yet know the relevant genetic program, and our understanding of the associated regulatory processes remains limited. Thus, there is still plenty of research ahead, which motivates us to continue pursuing this topic.”

Key Questions Answered:

Q: Why would it cause “chaos in the brain” if a single nerve cell accidentally developed two or three axons?

A: Think of a neuron like an ultra-precise telephone line. It is biologically designed to collect information from thousands of neighbors through its many input roots (dendrites), but it must broadcast its finalized message through a single, dedicated output wire (the axon). If a neuron sprouted multiple axons, it would blast its electrical signals into multiple unintended circuits simultaneously, causing widespread cross-talk, short-circuiting sensory loops, and destroying the brain’s delicate computational symmetry.

Q: How does the Arp2/3 protein complex function like a “zipper” to change the shape of a cell?

A: Every young neuron is wrapped in a highly tense, rigid mesh network of structural proteins that acts like a cellular corset, keeping the cell tight and round. Dr. Tien-chen Lin discovered that the Arp2/3 complex acts as a localized release valve. When it activates at the base of a neurite, it unzips the interlocking threads of that corset, temporarily loosening the tension. This allows the internal contents of the cell to push outward in a wave, extending that specific branch further into space.

Q: If this entire process is managed from inside the cell, how does the neuron decide which branch becomes the final axon?

A: According to the DZNE data, the initial selection process is actually driven by chance. The Arp2/3 complex randomly shifts from one neurite to another, causing them all to rhythmically stretch and contract. However, as these branches take turns expanding, rigid rod-like structural proteins called microtubules are continuously growing outward from the center. Eventually, purely by coincidence, one neurite remains expanded just long enough for these rigid rods to pack inside and lock its skeleton in place, permanently stabilizing it so it can no longer shrink back.

Editorial Notes:

• This article was edited by a Neuroscience News editor.
• Journal paper reviewed in full.
• Additional context added by our staff.

About this neuroscience research news

Author: Marcus Neitzert
Source: DZNE
Contact: Marcus Neitzert – DZNE
Image: The image is credited to Neuroscience News

Original Research: Open access.
“An intrinsic cytoskeletal oscillator establishes neuronal polarity” by Tien-chen Lin (林天正), Charlotte H. Coles, Eissa Alfadil, Florian Fäßler, Andreas Husch, Sebastian Dupraz, Thorben Pietralla, Akihiro Narita, Max Schelski, Kevin C. Flynn, Sina Stern, Christoph Möhl, Brett J. Hilton, Franz Vauti, Hans-Henning Arnold, Florian K. M. Schur & Frank Bradke. Nature
DOI:10.1038/s41586-026-10755-6

Neuronal migration into injured tissue: mechanisms and therapeutic strategies for brain regeneration

 How will your competent? doctor PRECISELY USE THIS TO MIGRATE NEURONS TO WHERE THEY ARE NEEDED POST STROKE?

And then initiate dendritic branching and axon pathfinding to connect everything up! NO clue! COMPLETE FUCKING INCOMPETENCE, get them fired, it means they have not had one complete thought on stroke recovery ever!

Neuronal migration into injured tissue: mechanisms and therapeutic strategies for brain regeneration

Author links open overlay panel, , 

a

Department of Developmental and Regenerative Neurobiology, Institute of Brain Science (IBS), Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan

b

Division of Neural Development and Regeneration, National Institute for Physiological Sciences (NIPS), Okazaki, Japan

Highlights

• •
  New neurons generated in the V-SVZ migrate in the intact and injured brain by regulating cytoskeletal dynamics.
• •
  New neurons migrate to the injured region and replace lost neurons, contributing to the recovery of motor and sensory functions.
• •
  Biomaterials and pharmacological approaches that promote neuronal migration toward injured regions are being actively investigated and may lead to fundamental therapies for brain injury.

Abstract

Neuronal migration is a crucial process not only for brain development but also for neural regeneration after injury. It has been reported that some new neurons generated in the ventricular-subventricular zone (V-SVZ) migrate toward tissue injured by ischemic stroke and other forms of brain damage. These migrating neurons can partially compensate for lost neurons and contribute to functional recovery, including improvements in motor function. Therefore, understanding the mechanisms that regulate neuronal migration is expected to facilitate the development of novel therapeutic strategies that enhance endogenous neural regeneration after brain injury.

In this review, we discuss the migratory mechanisms of new neurons generated in the V-SVZ and summarize current insights into strategies aimed at promoting neuronal migration and neuronal replacement in the injured brain.

New Artificial Neurons Cause Living Brain Cells to Fire

 If your competent? doctor can't figure out how to get dendritic branching/neurite outgrowth and axon pathfinding to work to connect up gray matter again then maybe this could work.

• axon pathfinding (51 posts to March 2012)
• axonal regeneration (29 posts to October 2012)
• dendritic branching (42 posts to February 2012)
• neurite outgrowth (32 posts to January 2012)

New Artificial Neurons Cause Living Brain Cells to Fire

A team at Northwestern University has printed artificial neurons from molybdenum disulfide (MoS₂) — a semiconducting mineral — on flexible plastic that produce spiking waveforms closely matching biological action potentials in shape, width, and timing. When delivered to living Purkinje cells in mouse cerebellar tissue, the artificial spikes drove the cells to fire — the first demonstration that a printed device can produce electrical signals a real brain cell accepts and responds to.

The result, recently published in Nature Nanotechnology, could lay groundwork for a new generation of neural interfaces — prosthetic limbs that deliver realistic sensation, spinal cord bridges that relay motor commands, and benchtop disease models with tunable parameters.

“There’s this white space — organic devices are too slow, metal oxides are too fast — and biology lives in between,” said Mark C. Hersam, PhD, the study’s senior author and Walter P. Murphy Professor of Materials Science and Engineering at Northwestern University in Evanston, Illinois.

An array of printed artificial neurons on flexible polyimide held by tweezers to show the substrate bending.

“We got these devices working at that timescale,” he said, “and when you have the right timescale and the right spike shape, you can directly interface with living cells.”

Key Points

• Printed MoS2 artificial neurons generated action-potential-like spikes.
• Spike shape/timing matched biological APs; duration ≈0.7-2 ms.
• Mouse Purkinje cells fired to artificial spikes at <200 Hz.
• 740 Hz output failed; neuronal firing capacity limits response.
• Potential uses: neural interfaces, spinal bridges, tunable disease models.

How do printed neural interfaces compare with metal oxide devices?

What limits long-term stability of MoS2 neural circuits?

Which cerebellar diseases could benefit from tunable circuit models?

Engineers Interface With Neuroscientists

The collaboration began not in biology but in electrical engineering. Hersam’s National Science Foundation grant aimed to build computing hardware that mimics the brain’s energy efficiency. The human brain runs on about 20 watts, whereas modern AI training runs on megawatts — a millionfold difference — and is extravagantly wasteful and potentially harmful to the environment.

The grant’s challenge: build computing hardware that mimics the brain’s efficiency.

“Most of the artificial neurons in the literature, if you actually look at their spiking profiles, they look more like a sine wave or just an oscillator, not a sharp action potential,” Hersam said. “They don’t achieve things like bursts of spikes, which is one of the things we demonstrate.”

Meghana Holla, PhD

But to mimic the brain, Hersam needed people who study it. He teamed up with Indira M. Raman, PhD, a neurophysiologist in the Department of Neurobiology at Northwestern University, and her lab. Her doctoral students, Spencer Brown, PhD, and Meghana Holla, PhD, visited Hersam’s lab to see what the engineers were up to.

The engineers in Hersam’s lab showed them the device output, with waveforms spiking at 3000 times per second, much too fast to mimic a neural cell. Purkinje cells may be among the fastest-firing neurons in the brain, but they only reach about 100 spikes per second.

“That’s not a neuron,” Brown, an incoming assistant professor of neuroscience at Brandeis University in Waltham, Massachusetts, recalled. “They can’t do that.”

The artificial neuron’s output (blue) overlaid with a living Purkinje cell’s action potential (red). Both spikes match closely in shape and duration, completing within about 2 milliseconds.

Over the following year, both fields discovered they used identical terminology, such as long-term potentiation, memory, and synapse weight, to mean different things.

Spencer Brown, PhD

“We thought we were talking about the same things, but we weren’t,” Brown said.

Brown and Holla provided “ground truth”: Each spike had to last between a fraction of a millisecond and a few milliseconds, matching a real action potential.

And the firing rate — the number of spikes per second — had to fall between single digits and low hundreds, not the thousands the engineers’ devices had been producing.

The engineers took the neuroscientists’ advice and successfully reconfigured the circuit to match.

The Glue That Makes Artificial Neurons Fire

The artificial neurons are built from a liquid, a custom ink formulated for a specialized printer. MoS₂, a semiconducting mineral, is peeled into flakes that are just a few atoms thick and suspended in ethanol. Without that suspension, the flakes clump together and settle out.

To keep them suspended, the researchers add ethylcellulose, a polymer derived from wood pulp, which coats each MoS₂ flake and holds it apart from its neighbors, kind of like glue. The resulting ink is a stable suspension of semiconductor particles in solvent, and it flows through an aerosol jet printer that deposits it as a fine mist onto flexible plastic.

An aerosol jet printer deposits molybdenum disulfide ink onto flexible plastic. The nozzle sprays the ink as a fine mist, printing rows of artificial neurons without a cleanroom.

The ethylcellulose scaffolding is essential for the artificial neurons to communicate like a network. When the printed film is baked at 350 °C, the ethylcellulose partially decomposes into carbon residue that settles into tiny, nanometer-sized gaps between flakes to form conductive bridges. Once fabricated, the device operates at room temperature.

Then comes what’s called electroforming. The first time a large current passes through the device, it doesn’t flow evenly. Some pathways are slightly more conductive, such as wherever carbon residue accumulated more thickly, or wherever flakes overlapped. The more conductive pathways carry more current, and more current generates more heat. That heat decomposes more polymer residue into carbon along the same route, making it more conductive and drawing still more current toward it.

Mark C. Hersam, PhD

The result is a single dominant channel — a filament — burned through the thickness of the film. “This occurs in a spatially inhomogeneous manner, leading to the formation of a conductive filament…all the current constricted into a narrow region,” said Hersam.

The filament has two states: hot and conducting, or cool and nonconducting.

On its own, that’s just a switch. What turns it into something that fires like a neuron is the circuit around it.

“This is a random network of flakes with gaps of a few nanometers,” said Vinod K. Sangwan, PhD, co-corresponding author and research associate professor of materials science and engineering at Northwestern University. “You cannot have atoms going from one place to another across that vacuum. The only mechanism left is thermal.”

In the full artificial neuron, the printed switch sits alongside a capacitor, which is a component that stores electrical charge. A steady input current slowly charges the capacitor, the way a biological neuron gradually accumulates signals from its neighbors.

The filament heats up and becomes conductive, and the capacitor rapidly discharges through it. That sudden discharge is the spike — a sharp, fast voltage pulse. Then the filament cools, the switch resets, and the capacitor begins slowly charging again. The cycle repeats: slow accumulation, sudden firing, reset.

And because the filament heats and cools on a millisecond timescale, the spikes fall within the same timing window as a real neuronal action potential.

How Real Brain Cells Respond to Artificial Neurons

Holla, who completed her PhD in Raman’s lab and is now a postdoctoral researcher studying memory at New York University in New York City, designed and ran experiments in mouse cerebellar slices. She positioned a stimulation electrode on the parallel fibers, the main pathway that excites Purkinje cells, and a recording electrode on the Purkinje cells themselves.

She played recordings of the artificial neurons’ waveforms into the tissue through a standard stimulation electrode at four different speeds: 7, 60, 218, and 740 spikes per second.

At every speed below 200 spikes per second, the Purkinje cells fired in response. The strongest results came at 60 spikes per second, where each artificial spike lasted 0.7 milliseconds, which is fast enough to trigger the cell but brief enough to avoid flooding the tissue with unnecessary current.

Above 200 spikes per second, the cells stopped responding. They simply cannot fire that fast. The team included the 740-spikes-per-second condition on purpose to directly challenge the many engineering groups building artificial neurons that operate at those speeds. “We had to show them [740 spikes] wasn’t sufficient,” Brown said. “You can’t work that fast.”

“You can see the living neurons respond to our artificial neuron,” Hersam said. But he is careful to note a caveat: The printed artificial neurons were not touching the brain tissue. The waveforms they generated were recorded and then played back into the slice through standard laboratory stimulation equipment.

The next step is to prove the printed device itself can interface with living tissue.

Clinical Possibilities

Ian Gaudet, PhD, a neuroscientist at Florida Atlantic University in Boca Raton, Florida, who was not involved in the study, sees multiple clinical possibilities from this work.

Ian Gaudet, PhD

“I’ve been waiting for [work like this] for years,” Gaudet said. “The signals coming off of these devices are the right shape, the right speed, and the right language for real neurons to be properly affected by them.”

The printed artificial neuron, he argues, is like a translator, converting digital information into electrical patterns neurons accept as input. And in prosthetic limbs, it could replace the rectangular pulses that give amputees a buzzing sensation with signals that peripheral nerves evolved to receive. A crude approximation of sensation could become something much closer to the real feeling.

“The idea would be to have this system where you’re controlling your prosthetic limb and you are feeling your prosthetic limb using the existing neuronal systems of your peripheral nervous system,” Gaudet said.

In spinal cord injury, it could convert decoded motor intentions into biologically shaped signals that motor neurons below a lesion treat as natural commands.

“If you can make that signal seamless,” Gaudet said, “people with spinal cord injuries could walk again one day.”

But where these artificial neurons may prove most valuable first is not as replacements for any damaged brain tissue but as test models. Researchers could build a small artificial cerebellar circuit on a benchtop, configure each element to fire like a different cell type, then deliberately break it to change the firing rate, and, in turn, simulate Purkinje cell loss in spinocerebellar ataxia.

Or one could remove an element to model a cerebellar stroke to see what happens — a disease model that could lead to novel treatments.

“You can turn this on, or turn this off,” Gaudet explained. “What happens if we mimic the patterns that we see in people who have a certain disease?”

Gaudet suggests researchers may use these devices as a physical disease model with real electrical dynamics.

What the Artificial Neuron Cannot Do

Hersam’s next goal is a small circuit — perhaps 10 artificial neurons — where each one fires differently, and together they accomplish what would require thousands of conventional transistors.

“Silicon achieves complexity by having billions of identical devices,” Hersam said. “The brain is the opposite. It’s heterogeneous. The complexity is at the device level.”

But Gaudet sees a gap no circuit design can yet fill: Biological neurons grow new connections and prune old ones, strengthening pathways that are used and weakening those that aren’t. Hersam’s lab’s printed neurons — or any other neuromorphic technology that mimics neuronal dynamics — can’t achieve that level of complexity yet.