Brain machine interface decoding method based on spiking neural network
US11948068B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 27, 2021 |
| Grant date | Apr 2, 2024 |
| Priority date | — |
| Expiry date | Oct 27, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention discloses a brain machine interface decoding method based on spiking neural network, comprising: (1) constructing a liquid state machine model based on a spiking neural network, the liquid state machine model consists of an input layer, an middle layer and an output layer, wherein, a connection weight from the input layer to the middle layer is Whh, a loop connection weight inside the middle layer is Whh, a readout weight from the middle layer to the output layer is Wyh; (2) Inputting a neuron spike train signal, and training each weight with the following strategy: (2-1) Using STDP without supervision to train the connection weight Whh from the input layer to the middle layer; (2-2) Setting the loop connection weight Whh inside the middle layer by means of distance model and random connection, and obtaining a middle layer liquid information R(t); (2-3) Using ridge regression with supervision to train the readout weight Wyh from the middle layer to the output layer, and establishing a mapping between the middle layer liquid information R(t) and the output motion information, and finally outputting a predicted motion trajectory. The present invention can quickl…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.