Artificial neural networks having attention-based selective plasticity and methods of training the same
US11210559B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 23, 2019 |
| Grant date | Dec 28, 2021 |
| Priority date | — |
| Expiry date | Dec 16, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/274
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An autonomous navigation system for a vehicle includes a controller configured to control the vehicle, sensors configured to detect objects in a path of the vehicle, nonvolatile memory including an artificial neural network configured to classify the objects detected by the sensors, and a processor. The artificial neural network includes a series of neurons in each of an input layer, at least one hidden layer, and an output layer. The memory includes instructions which, when executed by the processor, cause the processor to train the artificial neural network on a first task, identify, utilizing a contrastive excitation backpropagation algorithm, important neurons for the first task, identify, utilizing a learning algorithm, important synapses between the neurons for the first task based on the important neurons identified, and rigidify the important synapses to achieve selective plasticity of the series of neurons in the artificial neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.