# How Brain-Inspired Computers Learn and Unlearn Information > This patent describes a method for a brain-like computer network to learn associations between different inputs and then specifically unlearn mistakes or 'false positives' using a unique feedback mechanism. - **Patent:** US 11481621 - **Original title:** Unsupervised, supervised and reinforced learning via spiking computation - **Owner:** International Business Machines - **Granted:** 2022 - **Status:** Active - **Times cited:** 0 - **Field:** artificial_intelligence, machine_learning, semiconductors, software, consumer_electronics ## What it does This patent describes a way for a special type of computer network, called a neural network, to learn by associating features from two different inputs. For example, if you show it a picture of a cat (first input) and the sound of a meow (second input), it learns to connect them. The process involves feeding the first input to a 'first neuron population' and the second input to a 'third neuron population.' As these inputs travel through the network along 'pathways,' the network extracts their features. The clever part is how it corrects errors: it 'unlearns false positives' by taking copies of 'firing events' (like signals from brain cells) from certain parts of the network and propagating them back through 'additional pathways.' This unlearning uses a 'second learning rule,' which can be an 'anti-STDP' or 'anti-Hebb' rule, effectively weakening incorrect connections. ## What it does NOT cover - Does not cover traditional artificial neural networks that do not use 'spiking computation' or 'firing events' as described. - Does not cover learning systems that correct errors without explicitly propagating 'copies of firing events' through 'additional pathways' for unlearning. - Does not cover methods of unlearning that do not employ an 'anti-learning rule' such as anti-STDP or anti-Hebb. - Does not cover neural networks that only process a single input without associating features between two distinct inputs. - Does not cover systems where the unlearning process is not initiated by firing events from the second and fourth neuron populations. ## The clever bit The truly novel aspect is the explicit, structured mechanism for 'unlearning false positives.' Instead of just adjusting weights during forward passes, the patent describes copying 'firing events' and sending them back through dedicated 'additional pathways' with an 'anti-learning rule' (like anti-STDP) to specifically weaken incorrect associations. This provides a targeted way to forget wrong connections. ## Real-world examples 1. IBM's TrueNorth neuromorphic chip 2. Research in low-power AI for edge devices 3. Brain-inspired computing hardware 4. Robotics learning systems requiring error correction ## Why it matters This technology is important because it addresses a fundamental challenge in artificial intelligence: making learning systems more efficient and robust. By providing a specific mechanism for 'unlearning,' it allows neural networks to refine their knowledge and correct mistakes, which is crucial for complex tasks. This approach, using brain-inspired 'spiking computation,' could lead to more energy-efficient AI hardware, especially for devices that need to learn on the go. ## Frequently asked questions ### What does How Brain-Inspired Computers Learn and Unlearn Information cover? This patent describes a method for a brain-like computer network to learn associations between different inputs and then specifically unlearn mistakes or 'false positives' using a unique feedback mechanism. ### Who owns patent US 11481621? International Business Machines owns this patent, granted in 2022. ### When does this patent expire? This patent is expected to expire on August 16, 2039, when the invention enters the public domain. ### What problem does this patent solve? This technology is important because it addresses a fundamental challenge in artificial intelligence: making learning systems more efficient and robust. By providing a specific mechanism for 'unlearning,' it allows neural networks to refine their knowledge and correct mistakes, which is crucial for complex tasks. This approach, using brain-inspired 'spiking computation,' could lead to more energy-efficient AI hardware, especially for devices that need to learn on the go. ### What does this patent NOT cover? Does not cover traditional artificial neural networks that do not use 'spiking computation' or 'firing events' as described. **Full plain-English explainer:** https://patentbrief.org/patent/us/11481621/unsupervised-supervised-and-reinforced-learning-via-spiking-computation **Original patent:** https://patents.google.com/patent/US11481621 --- _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: - [How Artificial Neurons Learn from Spikes and Internal States](https://patentbrief.org/patent/us/9256215/apparatus-and-methods-for-generalized-state-dependent-learning-in-spiking-neuron) — 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. - [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. - [Training AI Chips Using Memristor Crossbars Directly On-Chip](https://patentbrief.org/patent/us/20230409893/on-chip-training-of-memristor-crossbar-neuromorphic-processing-systems) — This patent describes an electronic circuit that uses special memory components called memristors to train artificial intelligence models directly on the chip, making them learn more efficiently. - [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. - [How to Fix Faulty Memory Cells in AI Chips](https://patentbrief.org/patent/us/10956815/killing-asymmetric-resistive-processing-units-for-neural-network-training) — This patent describes a system that tests individual memory cells in AI chips for uneven behavior and then permanently disables the faulty ones before the chip starts learning, making AI training more efficient.