Neural connectivity inference with spike-timing dependent plasticity network

Knowing the connectivity patterns in neural circuitry is essential to understand the operating mechanism of the brain, as it allows the analysis of how neural signals are processed and flown through the neural system. With the recent advances in neural recording technologies in terms of channel size and time resolution, a simple and efficient system to perform neural connectivity inference is highly desired, which will enable the process of high dimensional neural activity recording data and reduction of the computational time and cost. In this work, we show that the spike-timing dependent plasticity (STDP) algorithm can be used to reconstruct neural connectivity patterns in a biological neural network, with higher accuracy and efficiency than statistic-based inference methods. The biologically inspired STDP learning rules are natively implemented in a second-order memristor network and are used to estimate the type and the direction of neural connections. When stimulated by the recorded neural spike trains, the memristor device conductance is modulated by the proposed STDP learning rules, which in turn reflects the correlation of the spikes and the possibility of neural connections. By compensating for the different levels of neural activity, highly reliable inference performance can be achieved. The proposed approach offers real-time and local learning, resulting in reduced computational cost/time and strong tolerance to variations of the neural system.