@InProceedings{8553016,
  author = {R. Alexandru and P. Malhotra and S. Reynolds and P. L. Dragotti},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Estimating the Topology of Neural Networks from Distributed Observations},
  year = {2018},
  pages = {420-424},
  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.},
  keywords = {brain;learning (artificial intelligence);neural nets;neurophysiology;probability;topology;distributed observations;bursting biological neurons;diffusion process;NetRate algorithm;topology estimation algorithm;biological neural networks;spike propagation;temporal dynamics;input stimulus model;brain network;Neurons;Biological neural networks;Inference algorithms;Signal processing algorithms;Biological system modeling;Stability analysis;Mathematical model;Neural networks;network topology inference;stability analysis of spike propagation;Izhikevich neuron model;Brian simulator;NetRate algorithm},
  doi = {10.23919/EUSIPCO.2018.8553016},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570436793.pdf},
}

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