Large scale spatio-temporal point processes: novel machine learning methodologiesand application to neural multi-electrode arrays.

Sanguinetti, Guido (Principal Investigator)
Hennig, Matthias (Co-investigator)

Status	Finished
Effective start/end date	1/11/14 → 31/07/18

Funding

EPSRC: £335,781.00

6 Article

The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules
Scholl, C., Rule, M. E. & Hennig, M. H., 11 Oct 2021, In: PLoS Computational Biology. 17, 10, 23 p., e1009458.
Research output: Contribution to journal › Article › peer-review

Open Access
File
Optimal Encoding in Stochastic Latent-Variable Models
Rule, M. E., Sorbaro, M. & Hennig, M. H., 28 Jun 2020, In: Entropy. 22, 7, 17 p., 714.
Research output: Contribution to journal › Article › peer-review

Open Access
File
Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings
Rule, M. E., Schnoerr, D., Hennig, M. & Sanguinetti, G., 4 Nov 2019, In: PLoS Computational Biology. 15, 11, p. 1-23 23 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File