Imperial College London
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ProfessorPier LuigiDragotti

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6192p.dragotti

 
 
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Location

 

814Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Perez-Nieves:2020:10.1101/2020.12.18.423468,
author = {Perez-Nieves, N and Leung, V and Dragotti, PL and Goodman, D},
doi = {10.1101/2020.12.18.423468},
title = {Neural heterogeneity promotes robust learning},
url = {http://dx.doi.org/10.1101/2020.12.18.423468},
year = {2020}
}
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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. <h4>Summary</h4> Neural heterogeneity is metabolically efficient for learning, and optimal parameter distribution matches experimental data.
AU  - Perez-Nieves,N
AU  - Leung,V
AU  - Dragotti,PL
AU  - Goodman,D
DO  - 10.1101/2020.12.18.423468
PY  - 2020///
TI  - Neural heterogeneity promotes robust learning
UR  - http://dx.doi.org/10.1101/2020.12.18.423468
ER  -
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