Differentiable temporal point processes for spiking neural networks
US12147894B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | May 4, 2021 |
| Grant date | Nov 19, 2024 |
| Priority date | — |
| Expiry date | Sep 21, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method performs a Differentiable Point Process (DPP). Generate a first sample sk by sampling from a Poisson process with reference to an upper bound λ of a conditional intensity function representing the DPP given a first set of samples S. Determine whether sk>T, output a second set of samples and train a probabilistic model using when sk>T, and perform the next four steps (generate a second sample, add, add, update) and return to the first step (generate a first sample) when sk≤T, where T denotes an observation length. Generate a second sample by sampling from a concrete distribution with reference to a parameter of the distribution defined by the conditional intensity function and a temperature τ, given a second set of samples . Add a pair of sk and pk to and discard rk. Add sk to S. Update k to k+1.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.