Using competitive layer model implemented by Lotka–Volterra recurrent neural networks for detecting brain activated regions from fMRI data

The competitive layer model (CLM) implemented by the Lotka–Volterra recurrent neural networks (LV RNNs) is prominently characterized by its capability of binding neurons with similar feature into the same layer by competing among neurons at different layers in a column. This paper proposes to use the CLM of the LV RNN for detecting brain activated regions from the fMRI data. The correlated voxels from brain fMRI data can be obtained, and the clusters from fMRI time series can be uncovered. Experiments on synthetic and real fMRI data demonstrate the effectiveness of binding activated voxels into the ‘active’ layers of the CLM. The activated voxels can be detected more accurately than some existing methods by the proposed method.