Reconstruction of Visual Image from Functional Magnetic Resonance Imaging Using Spiking Neuron Model

Spikes are the basic elements of information dissemination in the organic brain. A spike train, a series of discrete action potentials, incorporates the conception of time. Nowadays, the majority of existing artificial neural networks use numerical information to realize machine learning and cognition tasks. The advantage of numerical computation is its precision and remarkable performance on well-structured problems. However, cognition tasks have semi-structured or ill-structured information, even worse, some cannot reach a conclusion. According to biological experiments, the spiking neuron model is potentially more suitable for dealing with undetermined and unstructured problems. Researchers are trying to decode functional magnetic resonance imaging (fMRI) data, and this is a typical task to extract meaningful information from relatively undetermined and unstructured data. In this paper, we selected the Tempotron neuron model to analyze and reconstruct visual images from fMRI data when subjects received several kinds of visual stimuli; the preliminary results of pattern reconstruction were unpolished, but somewhat effective. Thus, a new structure for the spiking neural network was built to achieve better results. Several important aspects of the proposed model were discussed in this paper: the encoding of external stimulating signals, the extraction of effective features, the relative position of spike trains on the timeline, the back propagation of error, and the rationality of parameter selection. These aspects are crucial in the spiking neuron model's implementation, and are worth further investigation in future studies.