Biomedical subjects

Ehsan Arabzadeh

Publications and source records attributed to Ehsan Arabzadeh.

9 recordsLinked to original sources

PubMed2006

Factors affecting frequency discrimination of vibrotactile stimuli: implications for cortical encoding.

BACKGROUND: Measuring perceptual judgments about stimuli while manipulating their physical characteristics can uncover the neural algorithms underlying sensory processing. We carried out psychophysical experiments to examine how humans discriminate vibrotactile stimuli. METHODOLOGY/PRINCIPAL FINDINGS: Subjects compared the frequencies of two sinusoidal vibrations applied sequentially to one fingertip. Performance was reduced when (1) the root mean square velocity (or energy) of the vibrations was equated by adjusting their amplitudes, and (2) the vibrations were noisy (their temporal structure was irregular). These effects were super-additive when subjects compared noisy vibrations that had equal velocity, indicating that frequency judgments became more dependent on the vibrations' temporal structure when differential information about velocity was eliminated. To investigate which areas of the somatosensory system use information about velocity and temporal structure, we required subjects to compare vibrations applied sequentially to opposite hands. This paradigm exploits the fact that tactile input to neurons at early levels (e.g., the primary somatosensory cortex, SI) is largely confined to the contralateral side of the body, so these neurons are less able to contribute to vibration comparisons between hands. The subjects' performance was still sensitive to differences in vibration velocity, but became less sensitive to noise. CONCLUSIONS/SIGNIFICANCE: We conclude that vibration frequency is represented in different ways by different mechanisms distributed across multiple cortical regions. Which mechanisms support the "readout" of frequency varies according to the information present in the vibration. Overall, the present findings are consistent with a model in which information about vibration velocity is coded in regions beyond SI. While adaptive processes within SI also contribute to the representation of frequency, this adaptation is influenced by the temporal regularity of the vibration.

Adolescent↗

PubMed2006

Deciphering the spike train of a sensory neuron: counts and temporal patterns in the rat whisker pathway.

Rats achieve remarkable texture discriminations by sweeping their facial whiskers along surfaces. This work explores how neurons at two levels of the sensory pathway, trigeminal ganglion and barrel cortex, carry information about such stimuli. We identified two biologically plausible coding mechanisms, spike counts and patterns, and used "mutual information" to quantify how reliably neurons in anesthetized rats reported texture when "decoded" according to these candidate mechanisms. For discriminations between surfaces of different coarseness, spike counts could be decoded reliably and rapidly (within 30 ms after stimulus onset in cortex). Information increased as responses were considered as spike patterns with progressively finer temporal precision. At highest temporal resolution (spike sequences across six bins of 4 ms), the quantity of "information" in patterns rose 150% for ganglion neurons and 110% for cortical neurons above that in spike counts. In some cases, patterns permitted discriminations not supported by spike counts alone.

Action Potentials↗

PubMed2006

Neuronal encoding of natural stimuli: the rat tactile system.

A major challenge of sensory systems neuroscience is to quantify the brain activity underlying perceptual experiences and to explain this activity as the outcome of elemental neuronal response properties. One strategy is to measure variations in neuronal response in relation to controlled variations in an artificial stimulus. The limitation is that the stimuli scarcely resemble those which the sensory system has evolved to process-natural, behaviourally relevant stimuli. A more recent strategy is to measure neuronal responses during presentation of natural stimuli, but such experiments have failed to predict the observed responses according to the fundamental properties of neurons. In the work described here, we focus on tactile sensation in rats, and try to bridge the gap between neurons' responses to natural stimuli and their responses to controlled, artificial stimuli. We focus on texture, a submodality in which the rat whisker sensory system excels. Because the physical characteristics of texture stimuli have not yet been studied, the first set of experiments measures textures from the whiskers' point of view. The second set of experiments describes neurons' responses to textures. The third set of experiments computes kernels (estimates of the extracted stimulus features) of sensory neurons using white noise and then tries to account for natural texture responses according to these kernels. These investigations suggest ways of using natural stimuli to assemble a more complete picture of the neuronal basis of tactile sensation.

Afferent Pathways↗

PubMed2005

Texture signals in whisker vibrations.

Rodents excel in making texture judgments by sweeping their whiskers across a surface. Here we aimed to identify the signals present in whisker vibrations that give rise to such fine sensory discriminations. First, we used sensors to capture vibration signals in metal whiskers during active whisking of an artificial system and in natural whiskers during whisking of rats in vivo. Then we developed a classification algorithm that successfully matched the vibration frequency spectra of single trials to the texture that induced it. For artificial whiskers, the algorithm correctly identified one texture of eight alternatives on 40% of trials; for in vivo natural whiskers, the algorithm correctly identified one texture of five alternatives on 80% of trials. Finally, we asked which were the key discriminative features of the vibration spectra. Under both artificial and natural conditions, the combination of two features accounted for most of the information: The modulation power-the power of the part of the whisker movement representing the modulation due to the texture surface-increased with the coarseness of the texture; the modulation centroid-a measure related to the center of gravity within the power spectrum-decreased with the coarseness of the texture. Indeed, restricting the signal to these two parameters led to performance three-fourths as high as the full spectra. Because earlier work showed that modulation power and centroid are directly related to neuronal responses in the whisker pathway, we conclude that the biological system optimally extracts vibration features to permit texture classification.

Action Potentials↗

PubMed2005

Neuronal encoding of texture in the whisker sensory pathway.

A major challenge of sensory systems neuroscience is to quantify brain activity underlying perceptual experiences and to explain this activity as the outcome of elemental neuronal response properties. Rats make extremely fine discriminations of texture by "whisking" their vibrissae across an object's surface, yet the neuronal coding underlying texture sensations remains unknown. Measuring whisker vibrations during active whisking across surfaces, we found that each texture results in a unique "kinetic signature" defined by the temporal profile of whisker velocity. We presented these texture-induced vibrations as stimuli while recording responses of first-order sensory neurons and neurons in the whisker area of cerebral cortex. Each texture is encoded by a distinctive, temporally precise firing pattern. To look for the neuronal coding properties that give rise to texture-specific firing patterns, we delivered horizontal and vertical whisker movements that varied randomly in time ("white noise") and found that the response probabilities of first-order neurons and cortical neurons vary systematically according to whisker speed and direction. We applied the velocity-tuned spike probabilities derived from white noise to the sequence of velocity features in the texture to construct a simulated texture response. The close match between the simulated and real responses indicates that texture coding originates in the selectivity of neurons to elemental kinetic events.

Animals↗

PubMed2004

Whisker vibration information carried by rat barrel cortex neurons.

Rats can make extremely fine texture discriminations by "whisking" their vibrissa across the surface of an object. We have investigated one hypothesis for the neuronal basis of texture representation by measuring how clusters of neurons in the barrel cortex of anesthetized rats encode the kinetic features of sinusoidal whisker vibrations. Mutual information analyses of spike counts led to a number of findings. Information about vibration kinetics became available as early as 6 msec after stimulus onset and reached a peak at approximately 20-30 msec. Vibration speed, proportional to the product of vibration amplitude (A) and frequency (f), was the kinetic property most reliably reported by cortical neurons. Indeed, by measuring information when the complete stimulus set was collapsed into feature-defined groups, we found that neurons reduced the dimensionality of the stimulus from two features (A, f) to a single feature, the product Af. Moreover, because different neurons encode stimuli in the same manner, information loss was negligible even when the activity of separate neuronal clusters was pooled. This suggests a decoding scheme whereby target neurons could capture all available information simply by summating the signals from separate barrel cortex neurons. These results indicate that neuronal population activity provides sufficient information to allow nearly perfect discrimination of two vibrations, based on their deflection speeds, within a time scale comparable with that of a single whisking motion across a surface.

Animals↗

PubMed2003

Encoding of whisker vibration by rat barrel cortex neurons: implications for texture discrimination.

Rats, using their whiskers, have excellent capabilities in texture discrimination. What is the representation of texture in rat somatosensory cortex? We hypothesize that as rats "whisk" over a surface, the spatial frequency of a grooved or pebbled texture is converted to a temporal frequency of whisker vibration. Surface features such as groove depth or grain size modulate the amplitude of this vibration. Validation of the hypothesis depends on showing that vibration parameters have distinct neuronal representations in cortex. To test this, we delivered sinusoidal vibrations to the whisker shaft and analyzed cortical neuronal activity. Seven amplitudes and seven frequencies were combined to construct 49 stimuli while recording activity through a 10 x 10 microelectrode array inserted into the middle layers of barrel cortex. We find that cortical neurons do not explicitly encode vibration frequency (f) or amplitude (A) by any coding measure (average spike counts over different time windows, spike timing patterns in the peristimulus time histograms or in autocorrelograms). Instead, neurons explicitly encode the product of frequency and amplitude, which is proportional to the mean speed of the vibration. The quantity Af is an invariant because neuronal response encodes this feature independently of the values of the individual terms A and f. This was true across a wide time scale of firing rate measurements, from 5 to 500 msec. We conclude that vibration kinetics are rapidly and reliably encoded in the firing rate of cortical ensembles. Therefore, the cortical representation of vibration speed could underlie texture discrimination.

Action Potentials↗

PubMed2002

New attractor states for synchronous activity in synfire chains with excitatory and inhibitory coupling.

In a feedforward network of integrate-and-fire neurons, where the firing of each layer is synchronous (synfire chain), the final firing state of the network converges to two attractor states: either a full activation or complete fading of the tailing layers. In this article, we analyze various modes of pattern propagation in a synfire chain with random connection weights and delta-type postsynaptic currents. We predict analytically that when the input is fully synchronized and the network is noise free, varying the characteristics of the weights distribution would result in modes of behavior that are different from those described in the literature. These are convergence to fixed points, limit cycles, multiple periodic, and possibly chaotic dynamics. We checked our analytic results by computer simulation of the network, and showed that the above results can be generalized when the input is asynchronous and neurons are spontaneously active at low rates.

Action Potentials↗

PubMed2002

Munker-White-like illusions without T-junctions.

Some interpretations of the Munker-White illusion were evaluated by designing new versions of this illusion devoid of T-junctions (Munker-White-like images). The magnitudes of both Munker-White and Munker-White-like illusions were then quantified by using a brightness-matching technique. The results showed the effect to persist in all proposed versions. Since the illusion still remains despite the absence of explicit T-junctions and any explanation considering transparency, mechanisms other than those proposed by these interpretations must be responsible.

Contrast Sensitivity↗