RTBiomanager: a software platform to expand the applications of real-time technology in neuroscience

Abstract

A grand challenge in Computational Neuroscience is the integration of data arising from experimental techniques and theoretical work that involves large regions of unconstrained parameter space. Neural systems have drift, adaptation and learning mechanisms that result in transient behavior. Many information-processing mechanisms take place in this nonstationary activity that extends over several spatial and temporal scales. Studying this type of activity requires the use of activity-dependent stimulation techniques that can probe transient input/output relationships of neurons and circuits over different timescales (including under the millisecond). This requirement points towards the creation of new real-time software protocols to perform closed-loop adaptive interaction with neural systems to observe, manipulate and actively probe their function. These protocols may include the use of models that interact with living neurons to constrain their parameter space and even to refine themselves through this interaction [1]. RT activity-dependent stimulation can involve many types of stimuli (electrical, chemical, mechanical, etc.) and monitoring techniques (intra-or extracellular recordings, imaging, etc).Here we present RTBiomanager, a real-time software platform that can help to study neural transient dynamics. This platform can be used to build activity-dependent stimulus-response loops to interact with living systems below the millisecond time scale. So far, RT technology has been used to introduce artificial membrane or synaptic conductances and to create hybrid circuits of real and electronic neurons [2][3][4]. Generalizing the principles underlying dynamic-clamp, new protocols of RT eventdependent control, stimulation, and recording can be developed with applications in a broad spectrum of biomedical research. RTBiomanager is a multipurpose platform to control bi-directional interactions among living RT agents and artificial RT agents. Examples of living RT agents are cells, neurons, membranes, synapses, neural network and tissues. Examples of artificial RT agents are computer models, electronic devices, artificial neurons, sensors, microinjectors, motors [5], lasers and CCD cameras. RTBiomanager runs under RTAI (Real-Time Application Interface), a hard real-time layer over the Linux operating system to assure that the timing constraints in the detection, stimulation and control of artificial RT agents involved in physiological experiments can be accomplished.RTBiomanager is a user-friendly and customizable application that provides its own data monitor and analysis tools. This platform is designed for neuroscientists to explore the use of real-time technology to build a set of novel experiments that combine different recording and stimulation techniques. RTBiomanager will be available for free to download from our webpage.

Modular Acquisition and Stimulation System for Timestamp-Driven Neuroscience Experiments

“…The employed digital pulse timestamping technique allows to achieve a measurement precision in the order of 1 µs, much higher than most ADC-based acquisition systems. Even though our project was programmed to a small reconfigurable device, almost a half of LEs were left free and can be filled to implement future experiments with real-time feedback [26].…”

RTHybrid: a standardized and open-source real-time software model library for experimental neuroscience

“…While closed-loop technology allows researchers to conduct online observation, control and interaction with neural elements, it also presents some drawbacks in its implementation. Some of these difficulties are related to the complexity of their experimental design, but also to the accomplishment of precise temporal restrictions, in the scale of milliseconds or lower, which often are required during data acquisition and stimulation in biological experiments (Christini et al, 1999;Muñiz et al, 2005Muñiz et al, , 2008Muñiz et al, , 2009). The capacity of a system to perform periodic tasks and respond to asynchronous external events in an strict time slot (neither sooner nor later) is known as real-time (Furht et al, 1991).…”

“…Some of them are hardwarebased (Franke et al, 2012;Tessadori et al, 2012;Müller et al, 2013;Desai et al, 2017). Several software tools have been designed, particularly for dynamic-clamp electrophysiology experiments, both following soft- (Pinto et al, 2001;Nowotny et al, 2006;Linaro et al, 2014;Ciliberti and Kloosterman, 2017;Hazan and Ziv, 2017) and hard real-time (Christini et al, 1999;Dorval et al, 2001;Muñiz et al, 2005Muñiz et al, , 2009Biró and Giugliano, 2015;Patel et al, 2017) approaches, using distinct platforms and RTOS, which have diverse purposes and architectures, hence presenting different advantages and disadvantages.…”

Estudo da eletrocomunicação em Gymnotus carapo e Gnathothonemus petersii livres por tempos longos mediante protocolos realistas de estimulação

“…Os sinais foram gravados usando o software Dynamic Clamp (Muniz et al, 2005, Muniz et al, 2009, Chamorro et al, 2012 desenvolvido pelo Grupo de Neurocomputación Biológica (GNB) da Universidad Autónoma de Madrid, Espanha. Nesse aparato a separação entre pulsos do peixe e pulsos de estímulo é feita a posteriori.…”

“…Técnicas de estímulo bidirecionais dependentes da atividade do sistema vem sendo usadas em neurociência em experimentos de eletrofisiologia com o conceito de dynamic clamp desde a década de 40 (Marmont, 1949;Cole, 1955) até os dias de hoje Prinz et al, 2004;Muniz et al, 2005;Muniz et al, 2009;Rodrigues et al, 2011, Chamorro et al, 2012. O conceito clássico de dynamic clamp consistia de injeções de corrente dependentes da voltagem, em neurônios vivos, criando condutâncias artificiais.…”