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Synaptic Scaling Improves the Stability of Neural Mass Models Capable of Simulating Brain Plasticity

Lookup NU author(s): Dr Rob ForsythORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by M I T Press, 2019.

For re-use rights please refer to the publisher's terms and conditions.


Abstract: Neural mass models offer a way of studying the development and behaviour of large-scale brain networks through computer simulations. Such simulations are currently mainly research tools but as they improve, they could soon play a role in understanding, predicting and optimising pa- tient treatments, particularly in relation to effects and outcomes of brain injury. To bring us closer to this goal we took an existing state-of-the-art neural mass model capable of simulating connection growth through simu- lated plasticity processes. We identified and addressed some of the model’s limitations by implementing biologically plausible mechanisms. The main limitation of the original model was its instability, which we addressed by incorporating a representation of the mechanism of synaptic scaling and examining the effects of optimising parameters in the model. We show that the updated model retains all the merits of the original model, while being more stable and capable of generating networks that are in several aspects similar to those found in real brains.

Publication metadata

Author(s): Demsar J, Forsyth R

Publication type: Article

Publication status: Published

Journal: Neural Computation

Year: 2019

Volume: 32

Issue: 2

Pages: 424-446

Print publication date: 01/02/2020

Online publication date: 24/01/2020

Acceptance date: 08/10/2019

Date deposited: 09/10/2019

ISSN (print): 0899-7667

ISSN (electronic): 1530-888X

Publisher: M I T Press


DOI: 10.1162/neco_a_01257


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