How Hopfield Networks Use Resistors to Mimic Brain-Like Memory
A foundational patent describing an electronic circuit that uses a grid of resistors to perform computations, effectively creating an artificial neural network that can store and recall patterns.
Original patent title: “Electronic network for collective decision based on large number of connections between signals”
A foundational patent describing an electronic circuit that uses a grid of resistors to perform computations, effectively creating an artificial neural network that can store and recall patterns. Granted to California Institute of Technology in 1987 with 12 claims and 167 forward citations, and it is now in the public domain.
Coverage
What does this patent actually cover?
This patent describes a hardware architecture where multiple amplifiers are connected in a dense matrix using resistors. Each resistor acts as a weight, determining how much the output of one amplifier influences the input of another. By setting these resistance values, the network can be programmed to store specific patterns or solve optimization problems. When the system is given a partial or noisy input, the interconnected resistors cause the circuit to settle into a stable state, effectively reconstructing the full stored pattern or finding a solution to a complex problem.
The gap
What does this patent NOT cover?
- Does not cover software-based neural network simulations running on standard CPUs.
- Does not cover digital logic gates or traditional von Neumann computing architectures.
- Does not cover learning algorithms that automatically adjust resistor values during operation (this patent focuses on fixed, programmed resistances).
- Does not cover non-electronic implementations, such as optical or biological neural models.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The innovation lies in using the physical laws of electricity—specifically Kirchhoff's circuit laws—to perform computation. Instead of calculating results step-by-step, the network 'finds' the solution by naturally evolving toward a state of lowest energy.
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
Early hardware-based associative memory systems
Neuromorphic computing research platforms
Analog neural network circuit prototypes
Why it matters
The bigger picture
This is a seminal document in the history of artificial intelligence. John Hopfield's work provided a physical, electronic blueprint for associative memory, bridging the gap between theoretical neuroscience and practical circuit design. It proved that simple, collective interactions between basic components could produce complex, brain-like computational behavior.
Filed
January 22, 1985
Granted
April 21, 1987
Market context
Who's building on this
Companies in this space
Modern neuromorphic chip designers, such as those at Intel with the Loihi processor or startups like BrainChip, are building on the fundamental concept of using physical hardware properties to emulate neural connectivity. While the original patent is long expired, its core principle of 'compute-in-memory' remains a major area of research for low-power AI hardware.
Market impact
This patent helped shift the focus of computer science toward parallel, distributed processing models. It provided the theoretical and practical foundation for the field of neuromorphic engineering, which seeks to create hardware that mimics the energy efficiency and parallel structure of the human brain.
Claim 1 — Plain English
What this patent covers
This patent describes a hardware architecture where multiple amplifiers are connected in a dense matrix using resistors. Each resistor acts as a weight, determining how much the output of one amplifier influences the input of another. By setting these resistance values, the network can be programmed to store specific patterns or solve optimization problems. When the system is given a partial or noisy input, the interconnected resistors cause the circuit to settle into a stable state, effectively reconstructing the full stored pattern or finding a solution to a complex problem.
The clever bit
The innovation lies in using the physical laws of electricity—specifically Kirchhoff's circuit laws—to perform computation. Instead of calculating results step-by-step, the network 'finds' the solution by naturally evolving toward a state of lowest energy.
What it does not cover
- Does not cover software-based neural network simulations running on standard CPUs.
- Does not cover digital logic gates or traditional von Neumann computing architectures.
- Does not cover learning algorithms that automatically adjust resistor values during operation (this patent focuses on fixed, programmed resistances).
- Does not cover non-electronic implementations, such as optical or biological neural models.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
This patent is in the public domain
See the Freedom to Build guide — what is free to use, what is not, and how to cite this patent.
PatentBrief Score
Impact Score
Strong
Citation count
40/40
Highly cited
Claim breadth
8/20
Moderate scope
Recency
0/20
Older than 20 years
Assignee scale
20/20
Major company or institution
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
$72K – $230K
Midpoint $144K · expired or expiring · 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
12 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Hopfield, J. J. (1987). How Hopfield Networks Use Resistors to Mimic Brain-Like Memory (U.S. Patent No. 4,660,166). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/4660166/electronic-network-for-collective-decision-based-on-large-number-of-connections-between-signals
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 Hopfield Networks Use Resistors to Mimic Brain-Like Memory cover?
A foundational patent describing an electronic circuit that uses a grid of resistors to perform computations, effectively creating an artificial neural network that can store and recall patterns.
Who owns patent US 4660166?
California Institute of Technology owns this patent, granted in 1987.
When does this patent expire?
This patent has expired and is now in the public domain — anyone can use the invention freely.
What is patent US 4660166 cited by?
This patent has been cited by 167 later patents that build on its ideas.
What problem does this patent solve?
This is a seminal document in the history of artificial intelligence. John Hopfield's work provided a physical, electronic blueprint for associative memory, bridging the gap between theoretical neuroscience and practical circuit design. It proved that simple, collective interactions between basic components could produce complex, brain-like computational behavior.
What does this patent NOT cover?
Does not cover software-based neural network simulations running on standard CPUs.
Same assignee
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