Methods to facilitate the study of temporal coding in biological and artificial networks

Abstract

Technological advancements in analog and digital systems have enabled new approaches to study networks of physical and artificial neurons. In biological systems, a standard method to record neuronal activity is through cortically implanted micro-electrode arrays (MEAs). As advances in hardware continue to push channel counts of commercial MEAs upwards, it becomes imperative to develop automatic methods for data acquisition and analysis with high accuracy and throughput. Reliable, low latency methods are critical in closed-loop neuroprosthetic paradigms such as spike-timing dependent applications where the activity of a single neuron triggers specific stimuli with millisecond precision. This work presents an adapted version of an online spike detection algorithm, previously employed successfully on in vitro recordings, that has been improved to work under more stringent anesthetized in vivo environments subject to additional sources of variability and noise. The algorithm’s performance was compared with other commonly employed detection techniques for neural data on a newly developed and highly tunable extracellular recording model that features variable firing rates, adjustable SNRs, and multiple waveform characteristics. The testing framework was created from in vivo recordings collected during quiescence and electrical stimulation periods. The algorithm presents superior performance and efficiency in all evaluated conditions. Furthermore, we propose a methodology for online signal integrity analysis from MEA recordings and quantification of neuronal variability across different experimental settings. This work constitutes a stepping stone toward the creation of large scale neural data processing pipelines and aims to facilitate reproducibility in activity dependent experiments by offering a method for unifying various metrics calculated from single-unit activity. Precise spike detection becomes crucial for experiments studying temporal in addition to rate coding mechanisms. To further study and exploit the potential of temporal coding, a delay-feedback-based reservoir (DFB) has been implemented in software. This artificial network is found to be capable of processing spikes encoded from a benchmark task with performance comparable to that of more complex networks. This work allows us to corroborate the capabilities of temporal coding in a minimally-complex system suitable for implementation in physical hardware and inclusion in low-power circuit applications where computational power is also necessary.