Publications

BibTex format

@article{Perez-Nieves:2021:10.1038/s41467-021-26022-3,
author = {Perez-Nieves, N and Leung, VCH and Dragotti, PL and Goodman, DFM},
doi = {10.1038/s41467-021-26022-3},
journal = {Nature Communications},
pages = {5791--5791},
title = {Neural heterogeneity promotes robust learning},
url = {http://dx.doi.org/10.1038/s41467-021-26022-3},
volume = {12},
year = {2021}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The brain has a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that it substantially improved task performance. Learning was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks closely matches those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.Summary Neural heterogeneity is metabolically efficient for learning, and optimal parameter distribution matches experimental data.
AU  - Perez-Nieves,N
AU  - Leung,VCH
AU  - Dragotti,PL
AU  - Goodman,DFM
DO  - 10.1038/s41467-021-26022-3
EP  - 5791
PY  - 2021///
SP  - 5791
TI  - Neural heterogeneity promotes robust learning
T2  - Nature Communications
UR  - http://dx.doi.org/10.1038/s41467-021-26022-3
UR  - http://hdl.handle.net/10044/1/91600
VL  - 12
ER  -

Download

Dr Dan Goodman

Contact

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)