Texture discrimination and multi-unit recording in the rat vibrissal nerve

Comparative study of extracellular recording methods for analysis of afferent sensory information: Empirical modeling, data analysis and interpretation

BACKGROUND

Physiological studies of sensorial systems often require the acquisition and processing of data extracted from their multiple components to evaluate how the neural information changes in relation to the environment changes. In this work, a comparative study about methodological aspects of two electrophysiological approaches is described.

NEW METHOD

Extracellular recordings from deep vibrissal nerves were obtained by using a customized microelectrode Utah array during passive mechanical stimulation of rat´s whiskers. These recordings were compared with those obtained with bipolar electrodes. We also propose here a simplified empirical model of the electrophysiological activity obtained from a bundle of myelinated nerve fibers.

RESULTS

The peripheral activity of the vibrissal system was characterized through the temporal and spectral features obtained with both recording methods. The empirical model not only allows the correlation between anatomical structures and functional features, but also allows to predict changes in the CAPs morphology when the arrangement and the geometry of the electrodes changes.

COMPARISON WITH EXISTING METHOD(S)

This study compares two extracellular recording methods based on analysis techniques, empirical modeling and data processing of vibrissal sensory information.

CONCLUSIONS

This comparative study reveals a close relationship between the electrophysiological techniques and the processing methods necessary to extract sensory information. This relationship is the result of maximizing the extraction of information from recordings of sensory activity.

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.

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.

The mathematical whisker: A review of numerical models of the rat׳s vibrissa biomechanics

The vibrissal system of the rat refers to specialized hairs the animal uses for tactile sensory perception. Rats actively move their whiskers in a characteristic way called "whisking". Interaction with the environment produces elastic deformation of the whiskers, generating mechanical signals in the whisker-follicle complex. Advances in our understanding of the vibrissal complex biomechanics is of interest not only for the biological research field, but also for biomimetic approaches. The recent development of whisker numerical models has contributed to comprehending its sophisticated movements and its interactions with the follicle. The great diversity of behavioral patterns and complexities of the whisker-follicle ensemble encouraged the creation of many different biomechanical models. This review analyzes most of the whisker biomechanical models that have been developed so far. This review was written so as to render it accessible to readers coming from different research areas.

Study of the cortical representation of whisker frequency selectivity using voltage-sensitive dye optical imaging

The facial whiskers of rodents act as a high-resolution tactile apparatus that allow the animal to detect the finest details of its environment. Previously it was shown that whisker-sensitive neurons in the somatosensory cortex show frequency selectivity to small amplitude stimuli, An intravital voltage-sensitive dye optical imaging (VSDi) method in combination with the different frequency whisker stimulation was used in order to visualize neural activity in the mice somatosensory cortex in response to the stimulation of a single whisker by different frequencies. Using the intravital voltage-sensitive dye optical imaging (VSDi) method in combination with the different frequency whisker stimulation we visualized neural activity in the mice somatosensory cortex in response to the stimulation of a single whisker by different frequencies. We found that whisker stimuli with different frequencies led to different optical signals in the barrel field. Our results provide evidence that different neurons of the barrel cortex have different frequency preferences. This supports prior research that whisker deflections cause responses in cortical neurons within the barrel field according to the frequency of the stimulation. Many studies of the whisker frequency selectivity were performed using unit recording but to map spatial organization, imaging methods are essential. In the work described in the present paper, we take a serious step toward detailed functional mapping of the somatosensory cortex using VSDi. To our knowledge, this is the first demonstration of whisker frequency sensitivity and selectivity of barrel cortex neurons with optical imaging methods.

Neural encoding schemes of tactile information in afferent activity of the vibrissal system

When rats acquire sensory information by actively moving their vibrissae, a neural code is manifested at different levels of the sensory system. Behavioral studies in tactile discrimination agree that rats can distinguish different roughness surfaces by whisking their vibrissae. The present study explores the existence of neural encoding in the afferent activity of one vibrissal nerve. Two neural encoding schemes based on "events" were proposed (cumulative event count and median inter-event time). The events were detected by using an event detection algorithm based on multiscale decomposition of the signal (Continuous Wavelet Transform). The encoding schemes were quantitatively evaluated through the maximum amount of information which was obtained by the Shannon's mutual information formula. Moreover, the effect of difference distances between rat snout and swept surfaces on the information values was also studied. We found that roughness information was encoded by events of 0.8 ms duration in the cumulative event count and event of 1.0 to 1.6 ms duration in the median inter-event count. It was also observed that an extreme decrease of the distance between rat snout and swept surfaces significantly reduces the information values and the capacity to discriminate among the sweep situations.

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

Background

Studies 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.

Results

We 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).

Conclusions

The 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.

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.

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.

Dynamic Translation of Surface Coarseness Into Whisker Vibrations

Rodents in their natural environment use their whiskers to distinguish between surfaces having subtly different textures and shapes. They do so by actively sweeping their whiskers across surfaces in a rhythmic motion. To determine how textures are transformed into vibration signals in whiskers and how these vibrations are expressed in neuronal discharges, we induced active whisking in anesthetized rats, monitored the movement of whiskers across surfaces, and concurrently recorded from trigeminal ganglion (TG) neurons. We show that tactile information is transmitted through high-frequency micromotions superimposed on whisking macro motions. Consistent with this, we find that in most TG neurons, spike activity, and high-frequency micromotions are closely correlated. To determine whether these vibration signals can support texture discrimination, we examined their dependence on surface roughness and found that both vibration signals carry information about surface coarseness. Despite a large variability in this translation process, different textures are translated into distinct vibrations profiles. These profiles depend on whiskers properties, on radial distance to the surface, and on whisking frequency. Using the characteristics of these signals, we employ linear discriminant analysis and found that all whiskers were able to discriminate between different textures. While deteriorating with radial distance, this classification did not depend on whisking frequency. Finally, increasing the number of whisks and integrating tactile information from multiple whiskers improved texture discrimination. These results indicate that surface roughness is translated into distinct whisker vibration signals that result in neuronal discharges. However, due to the dynamic nature of this translation process, we propose that texture discrimination may require the integration of signals from multiple spatial and temporal sensory channels to disambiguate surface roughness.

Neuronal activity in rat barrel cortex underlying texture discrimination

Rats and mice palpate objects with their whiskers to generate tactile sensations. This form of active sensing endows the animals with the capacity for fast and accurate texture discrimination. The present work is aimed at understanding the nature of the underlying cortical signals. We recorded neuronal activity from barrel cortex while rats used their whiskers to discriminate between rough and smooth textures. On whisker contact with either texture, firing rate increased by a factor of two to ten. Average firing rate was significantly higher for rough than for smooth textures, and we therefore propose firing rate as the fundamental coding mechanism. The rat, however, cannot take an average across trials, but must make an immediate decision using the signals generated on each trial. To estimate single-trial signals, we calculated the mutual information between stimulus and firing rate in the time window leading to the rat's observed choice. Activity during the last 75 ms before choice transmitted the most informative signal; in this window, neuronal clusters carried, on average, 0.03 bits of information about the stimulus on trials in which the rat's behavioral response was correct. To understand how cortical activity guides behavior, we examined responses in incorrect trials and found that, in contrast to correct trials, neuronal firing rate was higher for smooth than for rough textures. Analysis of high-speed films suggested that the inappropriate signal on incorrect trials was due, at least in part, to nonoptimal whisker contact. In conclusion, these data suggest that barrel cortex firing rate on each trial leads directly to the animal's judgment of texture.

How cortical activity contributes to sensation is among biology's oldest problems. We studied the nature of the cortical representations underlying judgments of texture in rats. The rodent whisker sensory system is particularly intriguing because it is “active”: the animal generates sensory signals by palpating objects through self-controlled whisker motion (just as we move our fingertips along surfaces to measure their tactile features). Rats touched rough or smooth textures with their whiskers and turned left or right for a reward according to the texture identity. Monitoring behavior with high-speed videography, we have found that on trials when the rat correctly identified the stimulus, the firing rate of cortical neurons varies during a window of a few hundred milliseconds before making a decision according to the contacted texture: high for rough and lower for smooth. This firing-rate code is reversed on error trials (lower for rough than smooth). So when cortical neurons report the wrong stimulus, the rat, “feeling” the signals of its cortical neurons, fails to identify the stimulus. We conclude that barrel cortex firing rate on each trial predicts the animal's judgment of texture. This experiment begins to elucidate which features of cortical activity underlie the animal's capacity for tactile sensory discrimination.

Rats palpate objects with their whiskers to perceive texture. Their judgment of texture is predicted by the firing rate of neurons in the somatosensory cortex.

Responses of Trigeminal Ganglion Neurons during Natural Whisking Behaviors in the Awake Rat

Rats use their whiskers to locate and discriminate tactile features of their environment. Mechanoreceptors surrounding each whisker encode and transmit sensory information from the environment to the brain via afferents whose cell bodies lie in the trigeminal ganglion (Vg). These afferents are classified as rapidly (RA) or slowly (SA) adapting by their response to stimulation. The activity of these cells in the awake behaving rat is yet unknown. Therefore, we developed a method to chronically record Vg neurons during natural whisking behaviors and found that all cells exhibited (1) no neuronal activity when the whiskers were not in motion, (2) increased activity when the rat whisked, with activity correlated to whisk frequency, and (3) robust increases in activity when the whiskers contacted an object. Moreover, we observed distinct differences in the firing rates between RA and SA cells, suggesting that they encode distinct aspects of stimuli in the awake rat.

Active sensation: insights from the rodent vibrissa sensorimotor system

Rats sweep their vibrissae through space to locate objects in their immediate environment. In essence, their view of the proximal world is generated through pliable hairs that tap and palpate objects. The texture and shape of those objects must be discerned for the rat to assess the value of the object. Furthermore, the location of those objects must be specified with reference to the position of the rat's head for the rat to plan its movements. Recent in vivo and in vitro electrophysiological measurements provide insight into the algorithms and mechanisms that underlie these behavioral-based computations.