{
  "patent_number": "US 9256215",
  "country": "US",
  "title": "How Artificial Neurons Learn from Spikes and Internal States",
  "original_title": "Apparatus and methods for generalized state-dependent learning in spiking neuron networks",
  "summary": "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.",
  "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)."
  ],
  "filed": "2012-07-27",
  "granted": "2016-02-09",
  "expires": "2032-07-27",
  "status": "active",
  "holder": "Brain",
  "holder_url": "https://patentbrief.org/company/brain",
  "inventors": [
    {
      "name": "Oleg Sinyavskiy",
      "url": "https://patentbrief.org/inventor/oleg-sinyavskiy"
    },
    {
      "name": "Filip Ponulak",
      "url": "https://patentbrief.org/inventor/filip-ponulak"
    }
  ],
  "times_cited": 5,
  "tags": [
    "ai_ml",
    "robotics",
    "software",
    "semiconductors",
    "consumer_electronics"
  ],
  "abstract": "Generalized state-dependent learning framework in artificial neuron networks may be implemented. A framework may be used to describe plasticity updates of neuron connections based on connection state term and neuron state term. The state connections within the network may be updated based on inputs and outputs to/from neurons. The input connections of a neuron may be updated using connection traces comprising a time-history of inputs provided via the connections. Weights of the connections may be updated and connection state may be time varying. The updated weights may be determined using a rate of change of the trace and a term comprising a product of a per-neuron contribution and a per-connection contribution configured to account for the state time-dependency. 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/9256215/apparatus-and-methods-for-generalized-state-dependent-learning-in-spiking-neuron",
  "markdown_url": "https://patentbrief.org/patent/us/9256215/apparatus-and-methods-for-generalized-state-dependent-learning-in-spiking-neuron/md",
  "google_patents_url": "https://patents.google.com/patent/US9256215",
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}