Real-Time software technology and its use in experimental neuroscience

Debido a las complicadas dinámicas no lineales de los sistemas neuronales así como a la incapacidad existente a la hora de observar más de unas pocas de las señales que participan en dichas dinámicas de forma simultánea, el estudio de estos sistemas es muy complejo. Además, el paradigma tradicional de trabajo es el de estímulo-respuesta, en el cual se registra el comportamiento del sistema al responder a ciertos estímulos de entrada y se estudian estos resultados a posteriori, lo que impide caracterizar completamente la dinámica de su funcionamiento. Las tecnologías de ciclo cerrado permiten superar estas adversidades mediante la observación, el control y la interacción bidireccional con estos elementos neuronales. Sin embargo, la implementación de este tipo de tecnologías no es tan sencilla debido a que en muchos casos la detección y estimulación del sistema biológico debe hacerse de acuerdo a ciertas restricciones temporales precisas. Esta capacidad del sistema para ejecutar tareas y responder a eventos externos (síncronos o asíncronos) en una determinada franja de tiempo es lo que se conoce como funcionamiento en "tiempo real". Los ordenadores personales actuales poseen la suficiente potencia de procesamiento como para cumplir con los requisitos de tiempo real, sin embargo debido al funcionamieno de los planificadores de los sistemas operativos de propósito general (Windows, Linux, MacOS), que no puede ser controlado por el usuario, no existe manera de asegurar que un proceso de tiempo real se ejecutará sin interrupciones y cumpliendo con las restricciones temporales. Por otra parte, las implementaciones en hardware pueden cumplir con dichas restricciones temporales pero son menos programables. Por ello, existen también los llamados sistemas operativos de tiempo real (RTOS). Sin embargo, esta tecnología es a menudo difícil de instalar, configurar y manejar. Estas dificultades relativas a los RTOS provocan que muchos equipos y laboratorios dedicados a la neurociencia no vean viable invertir tiempo y esfuerzo en dominar esta tecnología para realizar experimentos de ciclo cerrado. En este trabajo se realiza una comparativa cuantitativa de las herramientas para tiempo real RTAI, Xenomai y Preempt-RT, de acuerdo a su rendimiento así como su usabilidad y accesibilidad, en la que se compara sus valores de latencia y la variabilidad (jitter) de estos. La comparativa se lleva a cabo en el contexto del uso de la tecnología de tiempo real en neurociencia experimental. Además se ha desarrollado una librería de modelos neuronales y sinápticos en tiempo real para su uso en circuitos híbridos, con neuronas vivas y modelos artificiales, y experimentos de ciclo cerrado. El correcto funcionamiento de dicha librería ha sido probado mediante su integración en circuitos híbridos, tanto con neuronas vivas como electrónicas, así como con el manejo de un motor de pasos para la estimulación mecánica.Due to the complicated non linear dynamics of neuronal systems, as to the existing inability to observe simultaneously more than a few signals of the ones involved in said dynamics, the study of these systems is quite complex. Moreover, traditionally the working paradigm is the stimulus-response one, where the system behaviour is recorded while it responds to certain input stimuli and the results are studied afterwards, thus preventing the complete characterization of the behavioural dynamics. Closed-loop technologies allow to overcome these difficulties through online observation, control and bidirectional interaction with these neural elements. Nevertheless, implementing this kind of technologies is not an easy task because in many cases the detection and stimulation must be done within some precise temporal boundaries. This ability of the system to complete tasks and respond to external events (synchronous and asynchronous) within a determined time slot is known as "real-time" performance. Actual computers have enough processing power and speed to comply with real-time requirements, but due to the general purpose operating systems (Windows, Linux, MacOS) schedulers’ behaviour, which can not be controlled by the user, there is no way to ensure that a real-time process will be run without interruptions and respecting the temporal restrictions. On the other hand, hardware implementations can fullfil such boundaries, but are also less programmable. For this reason the real-time operating systems (RTOS) exist. However, this technology is often difficult to install, configure and use. This RTOS-related complications provoke that many neuroscience researching teams and laboratories do not consider feasible to spend time and effort to implement this tools for closed-loop experiments. In this work a quantitative comparison between the real-time solution RTAI, Xenomai and Preempt-RT is carried out, focusing on their performance, usability and accessibility, by comparing their latency values and jitter. The comparison done in the context of real-time software technology usage in experimental neuroscience. Furthermore, a real-time neuron and synapse model library was developed for its use in hybrid circuits and closed-loop experiments. To validate the correct functioning of said library it was used in hybrid circuits, with both electronic and living neurons, and to control a stepper motor for mechanical stimulation

RThybrid: A standardized and open-source real-time software model library for experimental neuroscience

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.This work was supported by MINECO/FEDER DPI2015-65833-P, TIN2017-84452-R and ONRG grant N62909-14-1-N27

Automatized offline and online exploration to achieve a target dynamics in biohybrid neural circuits built with living and model neurons

Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goalThis research was supported by grants AEI/FEDER PID2021-122347NB-100, PGC2018-095895-B-I00, and PID2020- 114867RB-I00 (funded by MCIN/AEI/10.13039/501100011033 and ERDF - "A way of making Europe”

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks

Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, real-time drift adaptation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates closed-loop communication among living and artificial model neurons and connections, and contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparationsThis work was supported by MINECO/ FEDER PGC2018-095895-B-I00, DPI2015-65833-P, TIN2017-84452-R and ONRG grant N62909-14-1-N27