Invention Grant
- Patent Title: Reservoir computing neural networks based on synaptic connectivity graphs
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Application No.: US16776574Application Date: 2020-01-30
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Publication No.: US11593617B2Publication Date: 2023-02-28
- Inventor: Sarah Ann Laszlo , Philip Edwin Watson , Georgios Evangelopoulos
- Applicant: X Development LLC
- Applicant Address: US CA Mountain View
- Assignee: X Development LLC
- Current Assignee: X Development LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Priority: GR20190100586 20191231
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06N3/04 ; G06N3/08 ; G06T7/00 ; G06V10/82 ; G06V30/18

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.
Public/Granted literature
- US20210201115A1 RESERVOIR COMPUTING NEURAL NETWORKS BASED ON SYNAPTIC CONNECTIVITY GRAPHS Public/Granted day:2021-07-01
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