Biology subjects

Rodriguez, F. B.

Publications and source records attributed to Rodriguez, F. B..

4 recordsLinked to original sources

bioRxiv2022

Predicting Working Memory performance based on specific individual EEG spatiotemporal features

Working Memory (WM) is a limited capacity system for storing and processing information, which varies from subject to subject. Several works show the ability to predict the performance of WM with machine learning (ML) methods, and although good prediction results are obtained in these works, ignoring the intersubject variability and the temporal and spatial characterization in a WM task to improve the prediction in each subject. In this paper, we take advantage of the spectral properties of WM to characterize the individual differences in visual WM capacity and predict the subjects performance. Feature selection was implemented through the selection of electrodes making use of methods to treat unbalanced classes. The results show a correlation between the accuracy achieved with an Regularized Linear Discriminant Analysis (RLDA) classifier using the power spectrum of the EEG signal and the accuracy achieved by each subject in the behavioral experiment response of a WM task with retro-cue. The proposed methodology allows identifying spatial and temporal characteristics in the WM performance in each subject. Our methodology shows that it is possible to predict the WM performance in each subject. Finally, our results showed that by knowing the spatiotemporal characteristics that predict WM performance, it is possible to customize a WM task and optimize the use of electrodes for agile processing adapted to a specific subject. Thus, we pave the way for implementing neurofeedback through a Brain-Computer Interface.

neuroscience↗

bioRxiv2020

Modeling the variability of the electromotor commandsystem of pulse mormyrids

Mormyrids, a family of weakly electric fish, use electric pulses for communication and for extracting information from the environment (active electroreception). The electromotor system controls the timing of pulse generation. Ethological studies have described several sequences of pulse intervals (SPIs) which are related to distinct behaviors (e.g. mating or exploratory behaviors). Accelerations, scallops, rasps, and cessations are four SPI patterns reported in these fish, each showing characteristic temporal structures and large variability. This paper presents a computational model of the electromotor command circuit that reproduces SPI patterns as a function of the inputs to the model while keeping the same internal network configuration. The topology of the model is based on a simplified representation of the network with four neuron clusters (nuclei). An initial ad hoc tuned configuration (S-T) was built to reproduce nucleus characteristics and network topology as described by detailed morphological and electrophysiological studies. Then, a genetic algorithm (GA) was developed and applied to automatically tune the synaptic parameters of the model connectivity. Two different configurations obtained from the GA are presented here: one optimized to a set of synthetic examples of SPI patterns (S-GA) and another configuration adjusted to patterns recorded from freely-behaving Gnathonemus Petersii specimens (R-GA). Robustness analyses to input variability were performed to discard overfitting and assess validity. Results show that the set of SPI patterns are consistently reproduced, both for synthetic data and for recorded data. This model can be used as a tool to test novel hypotheses regarding temporal structure in electrogeneration.

neuroscience↗

bioRxiv2018

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.

neuroscience↗

bioRxiv2018

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, drift compensation, 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 building hybrid circuits and closed-loop communication among living and artificial model neurons and connections. Furthermore contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

neuroscience↗