A Closed-Loop System Processing High-Density Electrical Recordings and Visual Stimuli to Study Retinal Circuits Properties

Abstract

Active high-density electrode arrays can record the spiking activity of a large number of single-neurons in brain circuits. This offers the opportunity to develop closed-loop neural interfaces for studying complex brain circuits contingent on their cellular activity. However, this requires adapted solutions to process large-volumes of data acquired by thousands of electrodes and, in turn, generate feedbacks.Here, we present a closed-loop system that exploits System on Chip resources of a Xilinx ZedBoard Zynq-7000 to processes the instantaneous mice retina activity recorded by 4096 closely spaced microelectrodes. This is used to infer in real-time the functional properties of retinal ganglion cells (RGCs) all over the retina. Results show the performances of two interactive algorithms designed for (i) data reduction by clustering single-unit sub-millisecond correlated spike-trains from closely spaced neighboring electrodes (closed-loop latency of 22.9 ± 1.5 ms per second of recording, n = 5 retina); (ii) the classification of major types of RGCs consisting in ON and OFF types of functional responses (closed-loop latency of 117.3 ± 30.9 ms, n = 5 retina).