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

@inproceedings{Alexandru:2018:10.23919/EUSIPCO.2018.8553016,
author = {Alexandru, R and Malhotra, P and Reynolds, S and Dragotti, PL},
doi = {10.23919/EUSIPCO.2018.8553016},
publisher = {IEEE},
title = {Estimating the topology of neural networks from distributed observations.},
url = {http://dx.doi.org/10.23919/EUSIPCO.2018.8553016},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We address the problem of estimating the effective connectivity of the brain network, using the input stimulus model proposed by Izhikevich in [1], which accurately reproduces the behaviour of spiking and bursting biological neurons, whilst ensuring computational simplicity. We first analyse the temporal dynamics of neural networks, showing that the spike propagation within the brain can be modelled as a diffusion process. This helps prove the suitability of NetRate algorithm proposed by Rodriguez in [2] to infer the structure of biological neural networks. Finally, we present simulation results using synthetic data to verify the performance of the topology estimation algorithm.
AU  - Alexandru,R
AU  - Malhotra,P
AU  - Reynolds,S
AU  - Dragotti,PL
DO  - 10.23919/EUSIPCO.2018.8553016
PB  - IEEE
PY  - 2018///
TI  - Estimating the topology of neural networks from distributed observations.
UR  - http://dx.doi.org/10.23919/EUSIPCO.2018.8553016
UR  - http://hdl.handle.net/10044/1/64801
ER  -
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