Information transmission and detection thresholds in the vestibular nuclei: single neurons vs. population encoding

Abstract

Understanding how sensory neurons transmit information about relevant stimuli remains a major goal in neuroscience. Of particular relevance are the roles of neural variability and spike timing in neural coding. Peripheral vestibular afferents display differential variability that is correlated with the importance of spike timing; regular afferents display little variability and use a timing code to transmit information about sensory input. Irregular afferents, conversely, display greater variability and instead use a rate code. We studied how central neurons within the vestibular nuclei integrate information from both afferent classes by recording from a group of neurons termed vestibular only (VO) that are known to make contributions to vestibulospinal reflexes and project to higher-order centers. We found that, although individual central neurons had sensitivities that were greater than or equal to those of individual afferents, they transmitted less information. In addition, their velocity detection thresholds were significantly greater than those of individual afferents. This is because VO neurons display greater variability, which is detrimental to information transmission and signal detection. Combining activities from multiple VO neurons increased information transmission. However, the information rates were still much lower than those of equivalent afferent populations. Furthermore, combining responses from multiple VO neurons led to lower velocity detection threshold values approaching those measured from behavior (∼ 2.5 vs. 0.5-1°/s). Our results suggest that the detailed time course of vestibular stimuli encoded by afferents is not transmitted by VO neurons. Instead, they suggest that higher vestibular pathways must integrate information from central vestibular neuron populations to give rise to behaviorally observed detection thresholds.

Frequency dependence of vestibuloocular reflex thresholds

“…Because of a dearth of information, there is little evidence for or against perceptual discrimination pathways that parallel the motion perception pathways. If such parallel perceptual pathways exist, the increase in perceptual thresholds at lower frequencies might be consistent with thresholds in VO neurons, which also show a tendency to increase at lower frequencies (Massot et al 2011).…”

Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

“…Specifically, the fact that some sensory fibers are nearly as sensitive as behavior would imply either information-limiting correlations or massively suboptimal decoding to account for behavior (42,45,46). We consider it more likely that the total information encoded by the otolith afferent population is constrained by information-limiting correlations (40), in which case it is inappropriate to use the square root law to predict behavioral thresholds as previous studies have done (26,37). Unfortunately, it is unlikely that measurements of correlated noise from pairs of neurons provide a good estimate of the informationlimiting correlations (40).…”

“…An important question is how combining the responses of many sensory neurons determines the overall sensitivity of the population code. Some previous studies have attempted to determine the number of neurons (pool size) necessary to predict human perceptual thresholds from neuronal thresholds measured in macaques, assuming that neuronal population thresholds decrease as a function of the square root of pool size (26,37). We believe that this approach has major flaws because strong assumptions must be made regarding the shared variability among neurons (correlated noise) and the efficiency of population decoding, both of which remain largely unknown.…”

Sensory coding in oscillatory electroreceptors of paddlefish

“…Therefore, RR coherence is used to estimate an upper bound of mutual information rate. 24,45 Closeness of the SR coherence to the square root of the RR coherence indicates that a linear encoding scenario is optimal, whereas large departures indicate that a linear encoding model is not appropriate.…”

“…Our coherence analyses elucidated the role of internal oscillations and transduction processes in shaping the 0.5-20 Hz best frequency tuning of these electroreceptors, to match the electrical signals emitted by zooplankton prey. Stimulus-response coherence fell off above approximately 20 Hz, apparently due to intrinsic limits of transduction, but was detectable up to [40][41][42][43][44][45][46][47][48][49][50] Hz. Aligned with this upper fall off was a narrow band of intense internal noise at $25 Hz, due to prominent membrane potential oscillations in cells of sensory epithelia, which caused a narrow deadband of external insensitivity.…”