Estimating the topology of neural networks from distributed observations.
File(s)
Author(s)
Alexandru, Roxana
Malhotra, Pranav
Reynolds, Stephanie
Dragotti, Pier Luigi
Type
Conference Paper
Abstract
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.
Date Issued
2018-12-03
Date Acceptance
2018-05-18
Citation
2018 26th European Signal Processing Conference (EUSIPCO), 2018
Publisher
IEEE
Journal / Book Title
2018 26th European Signal Processing Conference (EUSIPCO)
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
European Signal Processing Conference 2018
Publication Status
Published
Start Date
2018-09-03
Finish Date
2018-09-07
Coverage Spatial
Rome, Italy