Computer programs
The Matlab code Example1_Filtering.m computes the approximate solution of the stochastic neural field equation (SNFE) and reconstructs the membrane potential of the cerebral cortex from incomplete measurements in the 1D-case. This problem is described in Example 1 of paper published by Kulikova M.V., Lima P.M., Kulikov G.Yu. (2022) Sequential method for fast neural population activity reconstruction in the cortex from incomplete noisy measurements. Computers in Biology and Medicine, 141: 105103. The output of the program consists of six graphs summarized in Fig.2. More precisely, this is one simulation of the SNFE with its weak noise level 0.1 that yields a one-bump numerical solution in Example 1. The first graph produced by Example1_Filtering.m depicts the top-left plot of Fig. 2 published in the cited paper, thesecond graph represents the top-middle plot of Fig. 2 and so on (see further details of this Matlab routine output description in the caption of Fig. 2 of the cited article).
The Matlab code Example2_Filtering.m computes the approximate solution of the stochastic neural field equation (SNFE) and reconstructs the membrane potential of the cerebral cortex from incomplete measurements in the 1D-case. This problem is described in Example 2 of paper published by Kulikova M.V., Lima P.M., Kulikov G.Yu. (2022) Sequential method for fast neural population activity reconstruction in the cortex from incomplete noisy measurements. Computers in Biology and Medicine, 141: 105103. The output of the program consists of six graphs summarized in Fig.3. More precisely, this is one simulation of the SNFE with its weak noise level 0.05 that yields a one-bump numerical solution in Example 2. The first graph produced by Example2_Filtering.m depicts the top-left plot of Fig. 3 published in the cited paper, the second graph represents the top-middle plot of Fig. 3 and so on (see further details of this Matlab routine output description in the caption of Fig. 3 of the cited article).
Computational package In Julia:
Solver for a stochastic version of the Neural Field Equation, in 1 and 2 spatial dimensions, available at
https://github.com/tiagoseq/NeuralFieldEq.jl
(a description of the codes and instructions for their use are availble in the file Readme at this link)
A paper describing the algorithm was accepted for publication:
T. Sequeira, A flexible solver to compute neural field equations in several scenarios, Journal of Open Source software, to appear.
The author of the program package and paper is Tiago Sequeira, one of the MSc students envolved in the projet.