{
  "patent_number": "US 9256823",
  "country": "US",
  "title": "Efficiently Updating Connections in AI Brains",
  "original_title": "Apparatus and methods for efficient updates in spiking neuron network",
  "summary": "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.",
  "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)."
  ],
  "filed": "2012-07-27",
  "granted": "2016-02-09",
  "expires": "2032-07-27",
  "status": "active",
  "holder": "Qualcomm Technologies",
  "holder_url": "https://patentbrief.org/company/qualcomm-technologies",
  "inventors": [
    {
      "name": "Jeffrey Alexander Levin",
      "url": "https://patentbrief.org/inventor/jeffrey-alexander-levin"
    },
    {
      "name": "Eugene Izhikevich",
      "url": "https://patentbrief.org/inventor/eugene-izhikevich"
    },
    {
      "name": "Oleg Sinyavskiy",
      "url": "https://patentbrief.org/inventor/oleg-sinyavskiy"
    },
    {
      "name": "Vadim Polonichko",
      "url": "https://patentbrief.org/inventor/vadim-polonichko"
    }
  ],
  "times_cited": 11,
  "tags": [
    "ai_ml",
    "semiconductors",
    "consumer_electronics",
    "telecommunications"
  ],
  "abstract": "Efficient updates of connections in artificial neuron networks may be implemented. A framework may be used to describe the connections using a linear synaptic dynamic process, characterized by stable equilibrium. The state of neurons and synapses within the network may be updated, based on inputs and outputs to/from neurons. In some implementations, the updates may be implemented at regular time intervals. In one or more implementations, the updates may be implemented on-demand, based on the network activity (e.g., neuron output and/or input) so as to further reduce computational load associated with the synaptic updates. The connection updates may be decomposed into multiple event-dependent connection change components that may be used to describe connection plasticity change due to neuron input. Using event-dependent connection change components, connection updates may be executed on per neuron basis, as opposed to per-connection basis.",
  "url": "https://patentbrief.org/patent/us/9256823/apparatus-and-methods-for-efficient-updates-in-spiking-neuron-network",
  "markdown_url": "https://patentbrief.org/patent/us/9256823/apparatus-and-methods-for-efficient-updates-in-spiking-neuron-network/md",
  "google_patents_url": "https://patents.google.com/patent/US9256823",
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}