Abstract
BackgroundIt is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network functions.ResultsIn this study, the ideas of linear Granger causality modeling and causality identification are extended to those of nonlinear Granger causality modeling and network structure identification. We employed Radial Basis Functions to fit the nonlinear multivariate dynamical responses of BNNs with neuronal pulse firing. By introducing the contributions from presynaptic neurons and detecting whether the predictions for postsynaptic neurons’ pulse firing signals are improved or not, we can reveal the information flows distribution of BNNs. Thus, the functional connections from presynaptic neurons can be identified from the obtained network information flows. To verify the effectiveness of the proposed method, the Nonlinear Granger Causality Identification Method (NGCIM) is applied to the network structure discovery processes of Spiking Neural Networks (SNN). SNN is a simulation model based on an Integrate-and-Fire mechanism. By network simulations, the multi-channel neuronal pulse sequence data of the SNNs can be used to reversely identify the synaptic connections and strengths of the SNNs.ConclusionsThe identification results show: for 2–6 nodes small-scale neural networks, 20 nodes medium-scale neural networks, and 100 nodes large-scale neural networks, the identification accuracy of NGCIM with the Gaussian kernel function was 100%, 99.64%, 98.64%, 98.37%, 98.31%, 84.87% and 80.56%, respectively. The identification accuracies were significantly higher than those of a traditional Linear Granger Causality Identification Method with the same network sizes. Thus, with an accumulation of the data obtained by the existing measurement methods, such as Electroencephalography, functional Magnetic Resonance Imaging, and Multi-Electrode Array, the NGCIM can be a promising network modeling method to infer the functional connective maps of BNNs.
Highlights
It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN)
The data quality obtained by the existing measurement methods, such as Electroencephalography (EEG), Magnetoencephalography (MEG), functional Near-infrared Spectroscopy, functional Magnetic Resonance Imaging, and Invasive Electrode Implantation (IEI), are usually limited because of a low temporal and spatial resolution
To verify the effectiveness of the proposed method in multi-channel BNN analysis, the Nonlinear Granger Causality Identification Method (NGCIM) based on the Radial Basis Functions (RBF) is applied to the network structure identifications
Summary
It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The major characteristics of BNNs, for example, Integrate-and-Fire (IF) mechanism, plasticity of synapses, and the complexity of network structure, Zhu et al BMC Neurosci (2020) 21:7 enable them to have adaptability and learning ability, which are significantly different from general artificial networks. These unique characteristics of the BNNs constitute the internal regulatory mechanism and substantial basis of various life functions. It is of great significance to explore the connection mode and connection characteristics of BNNs for studying the information processing and transmission mechanism of BNNs. At present, this research objective is still restricted by two factors: (1) Accurate identification of network structure requires a large amount of multi-channel neuronal pulse response data with a high temporal and spatial resolution. The data quality obtained by the existing measurement methods, such as Electroencephalography (EEG), Magnetoencephalography (MEG), functional Near-infrared Spectroscopy (fNIRS), functional Magnetic Resonance Imaging (fMRI), and Invasive Electrode Implantation (IEI), are usually limited because of a low temporal and spatial resolution. (2) Because biological neurons have strong nonlinear dynamic characteristics, currently, there are few effective network structure reverse identification methods, which can accurately model and adapt this nonlinear dynamic relationship
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