# Efficiently Updating Connections in AI Brains > 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. - **Patent:** US 9256823 - **Original title:** Apparatus and methods for efficient updates in spiking neuron network - **Owner:** Qualcomm Technologies - **Granted:** 2016 - **Status:** Active - **Times cited:** 11 - **Field:** ai_ml, semiconductors, consumer_electronics, telecommunications ## What it does The patent describes a way to make artificial brains, called "spiking neuron networks," learn more efficiently. Instead of constantly updating every connection, it stores a "time history of one or more inputs" (Claim 1) to a neuron. When an update is needed, the system determines "input-dependent connection change components (IDCC)" based on when inputs occurred and the current time (Claim 1). These components are then used to adjust "learning parameters" (like connection strengths) for that neuron. This allows updates to be done "on per neuron basis, as opposed to per-connection basis" (Abstract), significantly reducing the computational work. For example, if a neuron receives several inputs in a short burst, the system can calculate the combined effect of these inputs on its connections all at once, rather than processing each connection individually at every tiny time step. ## What it does NOT cover - Neural networks that are not specifically "spiking neuron networks" (Claim 5). - Update methods that always process every connection individually, rather than "on per neuron basis" (Abstract). - Updates that are performed strictly at regular time intervals without considering "on-demand" or "event-dependent" triggers (Abstract, Claim 1). - Learning parameter adjustments not based on a "time history of one or more inputs" or "input-dependent connection change components" (Claim 1). ## The clever bit The core innovation is decomposing connection updates into "event-dependent connection change components" and then applying these changes "on per neuron basis" rather than checking and updating every single connection individually. This significantly reduces the number of calculations needed, especially in large networks. ## Real-world examples 1. Neuromorphic computing chips like Intel Loihi 2. AI accelerators for edge devices 3. Low-power AI systems for IoT 4. Spiking neural network software frameworks ## Why it matters Efficient updates are crucial for deploying artificial intelligence on devices with limited power, like smartphones or IoT sensors. By reducing the computational load for learning, this technology helps make AI more practical for "edge computing" where data is processed locally instead of in the cloud. This allows for faster responses and greater privacy in AI applications. ## Frequently asked questions ### What does Efficiently Updating Connections in AI Brains cover? 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. ### Who owns patent US 9256823? Qualcomm Technologies 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 9256823 cited by? This patent has been cited by 11 later patents that build on its ideas. ### What problem does this patent solve? Efficient updates are crucial for deploying artificial intelligence on devices with limited power, like smartphones or IoT sensors. By reducing the computational load for learning, this technology helps make AI more practical for "edge computing" where data is processed locally instead of in the cloud. This allows for faster responses and greater privacy in AI applications. ### What does this patent NOT cover? Neural networks that are not specifically "spiking neuron networks" (Claim 5). **Full plain-English explainer:** https://patentbrief.org/patent/us/9256823/apparatus-and-methods-for-efficient-updates-in-spiking-neuron-network **Original patent:** https://patents.google.com/patent/US9256823 --- _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. - [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 Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth. - [How a Chip Uses Memory to Speed Up AI Calculations](https://patentbrief.org/patent/us/11741188/hardware-accelerated-discretized-neural-network) — This patent describes a specialized computer chip that uses non-volatile memory and analog signals to quickly perform calculations for artificial intelligence, especially for neural networks that need to remember past information. - [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.