{
  "patent_number": "US 11481621",
  "country": "US",
  "title": "How Brain-Inspired Computers Learn and Unlearn Information",
  "original_title": "Unsupervised, supervised and reinforced learning via spiking computation",
  "summary": "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.",
  "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."
  ],
  "filed": "2019-08-16",
  "granted": "2022-10-25",
  "expires": "2039-08-16",
  "status": "active",
  "holder": "International Business Machines",
  "holder_url": "https://patentbrief.org/company/international-business-machines",
  "inventors": [
    {
      "name": "Dharmendra S. Modha",
      "url": "https://patentbrief.org/inventor/dharmendra-s-modha"
    }
  ],
  "times_cited": 0,
  "tags": [
    "artificial_intelligence",
    "machine_learning",
    "semiconductors",
    "software",
    "consumer_electronics"
  ],
  "abstract": "The present invention relates to unsupervised, supervised and reinforced learning via spiking computation. The neural network comprises a plurality of neural modules. Each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of edges interconnects the plurality of neural modules. Each edge interconnects a first neural module to a second neural module, and each edge comprises a weighted synaptic connection between every neuron in the first neural module and a corresponding neuron in the second neural module.",
  "url": "https://patentbrief.org/patent/us/11481621/unsupervised-supervised-and-reinforced-learning-via-spiking-computation",
  "markdown_url": "https://patentbrief.org/patent/us/11481621/unsupervised-supervised-and-reinforced-learning-via-spiking-computation/md",
  "google_patents_url": "https://patents.google.com/patent/US11481621",
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}