How Artificial Neurons Learn from Spikes and Internal States
This patent describes a method for artificial neurons to learn by adjusting their connections based on a history of incoming electrical 'spikes' and the neuron's own internal state, aiming to improve its performance.
Original patent title: “Apparatus and methods for generalized state-dependent learning in spiking neuron networks”
This patent describes a method for artificial neurons to learn by adjusting their connections based on a history of incoming electrical 'spikes' and the neuron's own internal state, aiming to improve its performance. Granted to Brain in 2016 with 28 claims and 5 forward citations, and it is expected to expire in 2032.
Coverage
What does this patent actually cover?
This patent outlines a computer-implemented method for learning in artificial spiking neuron networks. The core idea is to update the strength of connections (called 'synaptic weights') between neurons. It does this by tracking 'traces' for each connection, which are like a memory of past inputs (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). When a neuron receives a 'spiking input' (like a brief electrical signal), it calculates a 'rate of change' for these traces. This rate depends on the trace's previous value and a unique combination of a 'neuron portion' (reflecting the neuron's overall state) and a 'connection portion' (reflecting the specific input's history). For example, a robot's control system using these neurons could learn to associate specific sensor inputs (spikes) with desired motor outputs by adjusting connection strengths based on how well the robot is performing a task, with the 'node state' transitioning towards a 'target state' (Claim 2).
The gap
What does this patent NOT cover?
- Does not cover learning methods for traditional artificial neural networks that do not use 'spiking input' or 'spiking neurons' (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 learning rules that update connection strengths without using 'traces' that store a time-history of inputs (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 learning where the update of connection strengths is not dependent on a 'product of a neuron portion and a connection portion' (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 learning where the 'node component' (neuron's state) is not common to multiple connections or interfaces (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 2).
- Does not cover learning where connection updates are purely continuous and not based on 'event-dependent connection change components' (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in its 'generalized state-dependent learning framework,' which updates connection strengths by combining a 'per-neuron contribution' (based on the neuron's overall state) with a 'per-connection contribution' (based on specific input history). This allows for 'event-dependent connection changes' that can be executed on a 'per neuron basis,' making learning more efficient and biologically plausible.
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
Robotics control systems for autonomous vehicles
Neuromorphic computing hardware
Energy-efficient AI processors
Event-based vision processing systems
Why it matters
The bigger picture
This patent provides a framework for how artificial neurons, particularly 'spiking neurons,' can learn and adapt. Spiking neural networks are a promising area for developing more energy-efficient and brain-like artificial intelligence. The assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, Brain Corp, is active in robotics, where such learning mechanisms are crucial for autonomous systems to adapt to new environments and tasks.
Filed
July 27, 2012
Granted
February 9, 2016
Market context
Who's building on this
Companies in this space
Brain Corp, the original assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, continues to develop AI and robotics solutions, likely building on foundational learning mechanisms like this. Companies active in neuromorphic computing, such as Intel with its Loihi chip and IBM with its TrueNorth processor, are also exploring and implementing advanced learning rules for spiking neural networks.
Market impact
This patent contributes to the foundational intellectual property for spiking neural networks, a field that aims to create more energy-efficient and biologically inspired AI. While not yet mainstream, advancements in SNN learning rules, like those described here, are critical for enabling future generations of AI hardware and software, particularly in areas requiring low-power, real-time processing such as edge AI and robotics.
Claim 1 — Plain English
What this patent covers
This patent outlines a computer-implemented method for learning in artificial spiking neuron networks. The core idea is to update the strength of connections (called 'synaptic weights') between neurons. It does this by tracking 'traces' for each connection, which are like a memory of past inputs (Claim 1). When a neuron receives a 'spiking input' (like a brief electrical signal), it calculates a 'rate of change' for these traces. This rate depends on the trace's previous value and a unique combination of a 'neuron portion' (reflecting the neuron's overall state) and a 'connection portion' (reflecting the specific input's history). For example, a robot's control system using these neurons could learn to associate specific sensor inputs (spikes) with desired motor outputs by adjusting connection strengths based on how well the robot is performing a task, with the 'node state' transitioning towards a 'target state' (Claim 2).
The clever bit
The novelty lies in its 'generalized state-dependent learning framework,' which updates connection strengths by combining a 'per-neuron contribution' (based on the neuron's overall state) with a 'per-connection contribution' (based on specific input history). This allows for 'event-dependent connection changes' that can be executed on a 'per neuron basis,' making learning more efficient and biologically plausible.
What it does not cover
- Does not cover learning methods for traditional artificial neural networks that do not use 'spiking input' or 'spiking neurons' (Claim 1).
- Does not cover learning rules that update connection strengths without using 'traces' that store a time-history of inputs (Claim 1).
- Does not cover learning where the update of connection strengths is not dependent on a 'product of a neuron portion and a connection portion' (Claim 1).
- Does not cover learning where the 'node component' (neuron's state) is not common to multiple connections or interfaces (Claim 2).
- Does not cover learning where connection updates are purely continuous and not based on 'event-dependent connection change components' (Abstract).
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
Moderate
Citation count
16/40
Early citations
Claim breadth
19/20
Very broad protection
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
$51K – $164K
Midpoint $102K · 5.9 yr remaining · industry ×1.5
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
28 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Sinyavskiy, O., & Ponulak, F. (2016). How Artificial Neurons Learn from Spikes and Internal States (U.S. Patent No. 9,256,215). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/9256215/apparatus-and-methods-for-generalized-state-dependent-learning-in-spiking-neuron
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 Artificial Neurons Learn from Spikes and Internal States cover?
This patent describes a method for artificial neurons to learn by adjusting their connections based on a history of incoming electrical 'spikes' and the neuron's own internal state, aiming to improve its performance.
Who owns patent US 9256215?
Brain owns this patent, granted in 2016.
When does this patent expire?
This patent is expected to expire on July 27, 2032, when the invention enters the public domain.
What is patent US 9256215 cited by?
This patent has been cited by 5 later patents that build on its ideas.
What problem does this patent solve?
This patent provides a framework for how artificial neurons, particularly 'spiking neurons,' can learn and adapt. Spiking neural networks are a promising area for developing more energy-efficient and brain-like artificial intelligence. The assignee, Brain Corp, is active in robotics, where such learning mechanisms are crucial for autonomous systems to adapt to new environments and tasks.
What does this patent NOT cover?
Does not cover learning methods for traditional artificial neural networks that do not use 'spiking input' or 'spiking neurons' (Claim 1).
Patent monitoring