Abstract
Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.
Highlights
The study of neural systems dynamics is hindered by various factors
We provide validation examples of this tool in the context of hybrid circuit implementations using dynamic-clamp, including detailed analysis and benchmarking of temporal precision in different real-time operating system (RTOS)
Not all tasks are sensitive to high latency values or data loss, real-time software can be classified in two types: soft real-time, when some deadlines can be missed without performance degradation as long as some threshold is not exceeded, and hard real-time, when all deadlines must be met or the system fails critically (Shin and Ramanathan, 1994)
Summary
The study of neural systems dynamics is hindered by various factors. The first one is their intrinsic non-linearity, since they process information in several interacting spatial and temporal scales and are affected by multiple transient adaptation and learning mechanisms. The third factor is related to the use of the traditional stimulus-response paradigm in most experimental neuroscience research, which only allows to record the behavior of the system under different stimuli and to analyze the collected data offline. Closed-loop techniques provide an efficient way to overcome such difficulties by interacting online with the system, producing precise stimulus according to RTHybrid the recorded information and presenting valuable insights on transient neural processes. This paradigm allows more flexibility in the experiment and favors its automation, as well as the control of neural dynamics (Chamorro et al, 2012; Potter et al, 2014; Roth et al, 2014; Varona et al, 2016)
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