Abstract
The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and reconstruction via nanoscale imaging. Nevertheless, this method still cannot scale well, and the observation on the neural activities based on the reconstructed neural network is not possible. Neuron activities are based on the neural network of the brain. In this paper, we propose that multi-neuron spike train data can be used as an alternative source to predict the neural network structure. And two concrete strategies for neural network structure prediction based on such kind of data are introduced, namely, the time-ordered strategy and the spike co-occurrence strategy. The proposed methods can even be applied to in vivo studies since it only requires neural spike activities. Based on the predicted neural network structure and the spreading activation theory, we propose a spike prediction method. For neural network structure reconstruction, the experimental results reveal a significantly improved accuracy compared to previous network reconstruction strategies, such as Cross-correlation, Pearson, and the Spearman method. Experiments on the spikes prediction results show that the proposed spreading activation based strategy is potentially effective for predicting neural spikes in the biological neural network. The predictions on the neural network structure and the neuron activities serve as foundations for large scale brain simulation and explorations of human intelligence.
Highlights
The micro-scale brain anatomy and activities of the neurons are essential for understanding how the brain works and the nature of human intelligence
We propose that multi-neuron spike train data can be used as an alternative source to predict the neural network structures, and here we discuss two concrete strategies for neural network structure prediction based on this kind of data
Two strategies are proposed for neural network structure prediction: 1) the time-ordered strategy: synapses exist between neurons that generate spikes at the two neighborhood time points; 2) the spike co-occurrence strategy: synapses exist between neurons that fire together at the same time slot
Summary
The micro-scale brain anatomy and activities of the neurons are essential for understanding how the brain works and the nature of human intelligence. The general idea is that even without nanoscale imaging techniques (which cannot scale well), it is still possible to reconstruct the neural network structure and predict spike activities by analyzing the spike train data. Two strategies are proposed for neural network structure prediction: 1) the time-ordered strategy: synapses exist between neurons that generate spikes at the two neighborhood time points (i.e. the time point N and N-1); 2) the spike co-occurrence strategy: synapses exist between neurons that fire together at the same time slot. This strategy is consistent with the Hebb’s law “cells that fire together, wire together”
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