Invention Grant
- Patent Title: Predicting neuron types based on synaptic connectivity graphs
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Application No.: US16776579Application Date: 2020-01-30
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Publication No.: US11568201B2Publication Date: 2023-01-31
- Inventor: Sarah Ann Laszlo , Georgios Evangelopoulos , Philip Edwin Watson
- 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: GR20190100587 20191231
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06F16/901 ; G06T7/00

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an artificial neural network architecture corresponding to a sub-graph of a synaptic connectivity graph. In one aspect, there is provided a method comprising: obtaining data defining a graph representing synaptic connectivity between neurons in a brain of a biological organism; determining, for each node in the graph, a respective set of one or more node features characterizing a structure of the graph relative to the node; identifying a sub-graph of the graph, comprising selecting a proper subset of the nodes in the graph for inclusion in the sub-graph based on the node features of the nodes in the graph; and determining an artificial neural network architecture corresponding to the sub-graph of the graph.
Public/Granted literature
- US20210201111A1 PREDICTING NEURON TYPES BASED ON SYNAPTIC CONNECTIVITY GRAPHS Public/Granted day:2021-07-01
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