How Nanoparticles Form Adaptive Neural Network Connections
This patent describes how to build and strengthen a physical neural network using tiny nanoparticles suspended in a liquid, where electric fields make the connections learn and adapt.
Original patent title: “Adaptive neural network utilizing nanotechnology-based components”
This patent describes how to build and strengthen a physical neural network using tiny nanoparticles suspended in a liquid, where electric fields make the connections learn and adapt. Granted to Knowmtech in 2012 with 11 claims and 12 forward citations, and it is expected to expire in 2028.
Coverage
What does this patent actually cover?
This patent outlines methods for creating and modifying a physical neural network, called an electromechanical neural network. It uses many tiny nanoparticles floating in a non-conductive liquid, positioned between 'pre-synaptic' and 'post-synaptic' electrodes (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). When one neuron fires, it increases the voltage at its pre-synaptic electrode. A special 'refractory pulse' then decreases the voltage at the post-synaptic electrode, increasing the voltage difference across the connection (Claim 1). This increased electric field, especially when applied at a higher frequency, causes the nanoparticles to align better, making the connection stronger and reducing its electrical resistance (Claim 2). The network can adapt by summing signals, comparing them to a threshold, and then grounding the post-synaptic junction during a pulse to further increase the local electric field and strengthen the active connections (Claim 4).
The gap
What does this patent NOT cover?
- Does not cover neural networks implemented purely in software on traditional digital processors, as it specifies 'electromechanical' components and 'nanoconnections'.
- Does not cover physical neural networks that use different materials or mechanisms to form and strengthen connections, such as optical switches or fixed-wire connections without nanoparticles.
- Does not cover adaptive neural networks where connection strength is modified without an increase in the local electric field or the physical alignment of nanoparticles.
- Does not cover neural networks that do not utilize a 'refractory pulse' mechanism to specifically adjust synaptic strength as described in ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1.
- Does not cover networks where the connections are not formed from nanoparticles disposed within a dielectric solution.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The truly novel aspect is using nanoparticles suspended in a liquid to form dynamic, self-organizing connections (nanoconnections) between electrodes. These connections can be strengthened by simply applying electric fields, causing the nanoparticles to align and reduce resistance, effectively 'learning' in a physical way.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Knowmtech's memristor-based AI hardware
Experimental neuromorphic computing chips
AI accelerators designed for energy efficiency
Why it matters
The bigger picture
This patent is foundational for building hardware that mimics the brain's structure, known as neuromorphic computing. Such physical neural networks offer the potential for significantly more energy-efficient and faster artificial intelligence compared to software-based AI running on traditional computer chips. It addresses the challenge of creating physical, adaptive connections that can learn and change, much like biological synapses.
Filed
April 10, 2008
Granted
April 10, 2012
Market context
Who's building on this
Companies in this space
Knowmtech, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, continues to develop and commercialize hardware based on similar principles, focusing on memristive AI processors. Other companies in the neuromorphic computing space, such as IBM with its TrueNorth chip and Intel with Loihi, are also exploring physical implementations of neural networks, though often with different underlying technologies.
Market impact
This patent contributes to the growing intellectual property landscape for neuromorphic computing, a field aiming to create brain-inspired hardware. It helps define methods for building physical, adaptive AI systems, which could eventually lead to a new category of highly energy-efficient AI accelerators. While not yet a mainstream technology, it lays groundwork for future advancements in AI hardware that could significantly impact data centers and edge computing devices.
Claim 1 — Plain English
What this patent covers
This patent outlines methods for creating and modifying a physical neural network, called an electromechanical neural network. It uses many tiny nanoparticles floating in a non-conductive liquid, positioned between 'pre-synaptic' and 'post-synaptic' electrodes (Claim 1). When one neuron fires, it increases the voltage at its pre-synaptic electrode. A special 'refractory pulse' then decreases the voltage at the post-synaptic electrode, increasing the voltage difference across the connection (Claim 1). This increased electric field, especially when applied at a higher frequency, causes the nanoparticles to align better, making the connection stronger and reducing its electrical resistance (Claim 2). The network can adapt by summing signals, comparing them to a threshold, and then grounding the post-synaptic junction during a pulse to further increase the local electric field and strengthen the active connections (Claim 4).
The clever bit
The truly novel aspect is using nanoparticles suspended in a liquid to form dynamic, self-organizing connections (nanoconnections) between electrodes. These connections can be strengthened by simply applying electric fields, causing the nanoparticles to align and reduce resistance, effectively 'learning' in a physical way.
What it does not cover
- Does not cover neural networks implemented purely in software on traditional digital processors, as it specifies 'electromechanical' components and 'nanoconnections'.
- Does not cover physical neural networks that use different materials or mechanisms to form and strengthen connections, such as optical switches or fixed-wire connections without nanoparticles.
- Does not cover adaptive neural networks where connection strength is modified without an increase in the local electric field or the physical alignment of nanoparticles.
- Does not cover neural networks that do not utilize a 'refractory pulse' mechanism to specifically adjust synaptic strength as described in Claim 1.
- Does not cover networks where the connections are not formed from nanoparticles disposed within a dielectric solution.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
22/40
Moderately cited
Claim breadth
7/20
Moderate scope
Recency
5/20
Granted 10–20 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$38K – $123K
Midpoint $77K · 1.7 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
11 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Nugent, A. (2012). How Nanoparticles Form Adaptive Neural Network Connections (U.S. Patent No. 8,156,057). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/8156057/adaptive-neural-network-utilizing-nanotechnology-based-components
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How Nanoparticles Form Adaptive Neural Network Connections cover?
This patent describes how to build and strengthen a physical neural network using tiny nanoparticles suspended in a liquid, where electric fields make the connections learn and adapt.
Who owns patent US 8156057?
Knowmtech owns this patent, granted in 2012.
When does this patent expire?
This patent is expected to expire on April 10, 2028, when the invention enters the public domain.
What is patent US 8156057 cited by?
This patent has been cited by 12 later patents that build on its ideas.
What problem does this patent solve?
This patent is foundational for building hardware that mimics the brain's structure, known as neuromorphic computing. Such physical neural networks offer the potential for significantly more energy-efficient and faster artificial intelligence compared to software-based AI running on traditional computer chips. It addresses the challenge of creating physical, adaptive connections that can learn and change, much like biological synapses.
What does this patent NOT cover?
Does not cover neural networks implemented purely in software on traditional digital processors, as it specifies 'electromechanical' components and 'nanoconnections'.
Patent monitoring