Ana L. Albarracín

Electrophysiological characterization of texture information slip-resistance dependent in the rat vibrissal nerve

BackgroundStudies in tactile discrimination agree that rats are able to learn a rough-smooth discrimination task by actively touching (whisking) objects with their vibrissae. In particular, we focus on recent evidence of how neurons at different levels of the sensory pathway carry information about tactile stimuli. Here, we analyzed the multifiber afferent discharge of one vibrissal nerve during active whisking. Vibrissae movements were induced by electrical stimulation of motor branches of the facial nerve. We used sandpapers of different grain size as roughness discrimination surfaces and we also consider the change of vibrissal slip-resistance as a way to improve tactile information acquisition. The amplitude of afferent activity was analyzed according to its Root Mean Square value (RMS). The comparisons among experimental situation were quantified by using the information theory.ResultsWe found that the change of the vibrissal slip-resistance is a way to improve the roughness discrimination of surfaces. As roughness increased, the RMS values also increased in almost all cases. In addition, we observed a better discrimination performance in the retraction phase (maximum amount of information).ConclusionsThe evidence of amplitude changes due to roughness surfaces and slip-resistance levels allows to speculate that texture information is slip-resistance dependent at peripheral level.

Design and construction of a photoresistive sensor for monitoring the rat vibrissal displacement.

Rats sweep their vibrissae in a rhythmic and coordinated fashion in order to acquire tactile information from their environment. Measuring vibrissae movement has become a matter of increased attention, from several labs, over the last few years. We describe the design and construction of an inexpensive photoresistive sensor that registers horizontal vibrissae movement. The device consists of an LED array and a light-dependent resistor (LDR) covered by a coating with varying transparency along its axis. When a vibrissa is located in the sensor, it generates a shadowy line over the photosensitive material, thus changing the LDR resistance. These changes are transduced into voltage changes. Our measurements on vibrissa show that this simple and inexpensive sensor effectively monitors the movement of a single vibrissa.

Discriminability measures and time-frequency features: an application to vibrissal tactile discrimination.

BACKGROUND Often, the first problem that the neuroscientist must face is to determine if a specific stimulus set applied to biological system produces specific, precise and well differentiated responses. NEW METHOD In the present study we have proposed four discriminability measures to evaluate the feasibility of differentiating experimental conditions: information measures based on information theory, percentage overlap based on Linacre method, Bhattacharyya distance and univariate standard distance. All discriminability measures were evaluated on experimental protocols related to vibrissal tactile discrimination. RESULTS Time-frequency features were extracted from afferent discharges and then, pairwise comparisons were realized by using the proposed discriminability measures. Our results reveal the existence of time-frequency patterns which allows differentiating of sweep conditions from multifiber recordings. COMPARISON WITH EXISTING METHODS Currently, statistical methods used to justify significant differences in experimental conditions have rigorous criteria that must be met for correct validation of results. Discriminability measures proposed here are robust and can be adjusted to different experimental conditions (time series, repeated measures, specific variables and other). CONCLUSIONS Discriminability measures allowed determining the time intervals where two sweep situations have the highest probability to be differentiated from each other. High discriminability percentages were observed into protraction phase, although to a lesser degree, it was also observed in retraction phase. It was demonstrated that sensibility of discriminability measures are different. This revealing a greater ability to highlight percentage changes of pairwise comparisons. Finally, the methods here proposed can be adapted to other features of biological responses.

Time–frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition

BACKGROUND Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. NEW METHOD In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. RESULTS The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. COMPARISON WITH EXISTING METHODS Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. CONCLUSIONS Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods.

Functional specificity of rat vibrissal primary afferents.

In this study, we propose to analyze the peripheral vibrissal system specificity through its neuronal responses. Receiver operating characteristics (ROC) curve analyses were used, which required the implementation of a binary classifier (artificial neural network) trained to identify the applied stimulus. The training phase consisted of the observation of a predetermined amount of vibrissal sweeps on two surfaces of different texture and similar roughness. Our results suggest that the specificity of the peripheral vibrissal system easily permits the discrimination between perceived stimuli, quantified through neuronal responses, and that it can be evaluated through an ROC curve analysis. We found that such specificity makes a linear binary classifier capable of detecting differences between stimuli with five sweeps at most.

Electrophysiology for biomedical engineering students: a practical and theoretical course in animal electrocorticography

The major challenge in laboratory teaching is the application of abstract concepts in simple and direct practical lessons. However, students rarely have the opportunity to participate in a laboratory that combines practical learning with a realistic research experience. In the Biomedical Engineering career, we offer short and optional courses to complement studies for students as they initiate their Graduation Project. The objective of these theoretical and practical courses is to introduce students to the topics of their projects. The present work describes an experience in electrophysiology to teach undergraduate students how to extract cortical information using electrocorticographic techniques. Students actively participate in some parts of the experience and then process and analyze the data obtained with different signal processing tools. In postlaboratory evaluations, students described the course as an exceptional opportunity for students interested in following a postgraduate science program and fully appreciated their contents.

Laboratory experience for teaching sensory physiology

The major challenge in laboratory teaching is the application of abstract concepts in simple and direct practical lessons. However, students rarely have the opportunity to participate in a laboratory that combines practical learning with a realistic research experience. In the Bioengineering Department, we started an experiential laboratory physiology to teach graduated students some aspects of sensorial physiology and exposes them to laboratory skills in instrumentation and physiological measurements. Students were able to analyze and quantify the effects of activation of mechanoreceptors in multifiber afferent discharges using equipment that was not overly sophisticated. In consequence, this practical laboratory helps students to make connections with physiological concepts acquired in theoretical classes and to introduce them to electrophysiological research.

Toward an Improvement of the Analysis of Neural Coding

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.

Identification of Functionally Interconnected Neurons Using Factor Analysis

The advances in electrophysiological methods have allowed registering the joint activity of single neurons. Thus, studies on functional dynamics of complex-valued neural networks and its information processing mechanism have been conducted. Particularly, the methods for identifying neuronal interconnections are in increasing demand in the area of neurosciences. Here, we proposed a factor analysis to identify functional interconnections among neurons via spike trains. This method was evaluated using simulations of neural discharges from different interconnections schemes. The results have revealed that the proposed method not only allows detecting neural interconnections but will also allow detecting the presence of presynaptic neurons without the need of the recording of them.

A simplified empirical modeling of electrophysiological activity in a bundle of myelinated nerve fibers

In vivo or in vitro electrophysiological characterization is a standard procedure to know specific aspects of the sensory and motor fibers conduction. In this work we propose a simplified empirical modeling that can predict the electrical activity evocated in a nerve by different experimental conditions. This approach includes physical and chemical concepts about the generation and propagation of myelin fiber action potentials, volume conduction and medium bioelectric properties; this was implemented by using simple qualitative parameters. The validation included the qualitative analysis of compound action potentials (CAPs) obtained from frog sciatic nerve in different experimental conditions and CAPs recordings obtained from infraorbital nerve in rats. The results reveal an adequate model fit to the CAPs waveform experimentally obtained. The intuitive parameters used in our approach facilitate the implementation and results interpretation, at the same time that provides versatility and robustness.