# 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. - **Patent:** US 9256215 - **Original title:** Apparatus and methods for generalized state-dependent learning in spiking neuron networks - **Owner:** Brain - **Granted:** 2016 - **Status:** Active - **Times cited:** 5 - **Field:** ai_ml, robotics, software, semiconductors, consumer_electronics ## What it does 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). ## 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). ## 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. ## Real-world examples 1. Robotics control systems for autonomous vehicles 2. Neuromorphic computing hardware 3. Energy-efficient AI processors 4. Event-based vision processing systems ## Why it matters 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. ## 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). **Full plain-English explainer:** https://patentbrief.org/patent/us/9256215/apparatus-and-methods-for-generalized-state-dependent-learning-in-spiking-neuron **Original patent:** https://patents.google.com/patent/US9256215 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Efficiently Updating Connections in AI Brains](https://patentbrief.org/patent/us/9256823/apparatus-and-methods-for-efficient-updates-in-spiking-neuron-network) — This patent describes a method for updating the connections in artificial "spiking neuron networks" more efficiently by only making changes when needed, saving computational power. - [Efficient Hardware for Spiking Neural Network Calculations](https://patentbrief.org/patent/us/11507804/device-and-method-for-calculating-convolution-in-a-convolutional-neural-network) — This patent describes a specialized processor designed to quickly calculate layers in a spiking convolutional neural network by using parallel computing units whose number doesn't depend on the network's size, making AI processing more efficient. - [How to Automatically Expand Neural Networks by Adding New Nodes](https://patentbrief.org/patent/us/10832138/gpt-language-model-pre-training) — A method for growing artificial intelligence models by identifying underperforming parts of a network and adding new nodes based on the behavior of existing ones. - [How to Save and Reuse Skills Learned by Artificial Intelligence Hardware](https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries) — A method for capturing the internal settings of a neuromorphic AI chip after it learns a task, allowing that 'skill' to be exported and loaded onto another AI chip. - [How Nanoparticles Form Adaptive Neural Network Connections](https://patentbrief.org/patent/us/8156057/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.