Detecting synaptic connections in neural systems using compressive sensing

Abstract

Revealing synaptic connections between neurons is of great significance and practical value to biomedicine and bioneurology. We present a general approach to reconstruct neuronal synapses, which is based on compressive sensing and special data processing. And this approach is more suitable for nervous system with peak time series. Numerical simulations illustrate the feasibility and effectiveness of the proposed approach. Moreover, this approach not only adapts to the asymmetry of neural connections and the diversity of coupling strength, but also adapts to the excitability and inhibition of neural node classification. In addition, the effects of the factors on the synaptic connection identification performance and their optimal states for the synaptic connection recovery are discussed. Besides, it is of great practical significance to control the order of Taylor expansion to improve the performance of synaptic connection recognition.

Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties

“…Network models used in data research include the online social networks [3], mail networks [4], biological networks [5], annotated networks [6], and online dating market [7]. Recently, complex networks have been extensively evaluated and applied in several applications by physicists [8][9][10][11][12][13][14][15]. Most of these networks have been shown to achieve a stationary state and become scale-free.…”