Neural Functional Connectivity Reconstruction with Second‐Order Memristor Network

Abstract

The advances of neural recording techniques have fostered rapid growth of the number of simultaneously recorded neurons, opening up new possibilities to investigate the interactions and dynamics inside neural circuitry. The high recording channel counts, however, pose significant challenges for data analysis because the required time and computational resources grow superlinearly with the data volume. Herein, the feasibility of real‐time reconstruction of neural functional connectivity using a second‐order memristor network is analyzed. Spike‐timing‐dependent plasticity, natively implemented by the internal dynamics of the memristor device, leads to the successful discovery of temporal correlations between pre‐ and postsynaptic spikes of the simulated neural circuits in an unsupervised fashion. The proposed system demonstrates high classification accuracy under a wide range of parameter settings considering indirect connections, synaptic weights, transmission delays, connection density, and so on, and enables the capturing of dynamic connectivity evolutions. The influence of device nonideal factors on detection accuracy is systematically evaluated, and the system shows robustness to initial weight randomness, and cycle‐to‐cycle and device‐to‐device variations. The proposed method allows direct mapping of neural connectivity onto the artificial memristor network and can lead to efficient front‐end data analysis of high‐density neural recording systems and potentially directly coupled bioartificial networks.