Spatio-temporal convergence (STC) in otolith neurons

Abstract

It has been recently demonstrated that some primary otolith afferents and most otolith-related vestibular nuclei neurons encode two spatial dimensions that can be described by two vectors in temporal and spatial quadrature. These cells are called broadly-tuned neurons. They are characterized by a non-zero tuning ratio which is defined as the ratio of the minimum over the maximum sensitivity of the neuron. Broadly-tuned neurons exhibit response gains that do not vary according to the cosine of the angle between the stimulus direction and the cell's maximum sensitivity vector and response phase values that depend on stimulus orientation. These responses were observed during stimulation with pure linear acceleration and can be explained by spatio-temporal convergence (STC) of primary otolith afferents and/or otolith hair cells. Simulations of STC of the inputs to primary otolith afferents and vestibular nuclei neurons have revealed interesting characteristics: First, in the case of two narrowly-tuned input signals, the largest tuning ratio is achieved when the input signals are of equal gain. The smaller the phase difference between the input vectors, the larger the orientation differences that are required to produce a certain tuning ratio. Orientation and temporal phase differences of 30-40 degrees create tuning ratios of approximately 0.10-0.15 in target neurons. Second, in the case of multiple input signals, the larger the number of converging inputs, the smaller the tuning ratio of the target neuron. The tuning ratio depends on the number of input units, as long as there are not more than about 10. For more than 10-20 input vectors, the tuning ratio becomes almost independent of the number of inputs. Further, if the inputs comprise two populations (with different gain and phase values at a given stimulus frequency), the largest tuning ratio is obtained when the larger population has a smaller gain. These findings are discussed in the context of known anatomical and physiological characteristics of innervation patterns of primary otolith afferents and their possible convergence onto vestibular nuclei neurons.

A model for the characterization of the spatial properties in vestibular neurons

“…Under the assumption that converging inputs to a neuron add linearly, it has been proposed (Baker et al 1984a) and theoretically demonstrated (Angelaki 1991b) that "non-cosine-like" tuning characteristics can arise from inputs which exhibit "cosine-like" spatial properties only if the inputs differ in both their temporal and spatial properties. An interesting question is whether the otolith afferents show "non-cosine-like" spatial properties.…”

“…It has been shown that some otolith afferents innervating the peripheral extrastriola may branch and innervate several hair cells within a terminal field diameter of approximately 100 ~t (Fernandez et al 1990). The non-zero sensitivity of primary otolith afterents in "null" orientations, whenever present, could be created by different temporal characteristics of inputs from hair cells which have somewhat different polarization vectors and are innervated by a single afferent fiber (Angelaki 1991b).…”

Detection of rotating gravity signals

“…It has been shown that simple convergence between regular and irregular otolith afferents with different polarization vectors result in "two-dimensional" neurons with one vector encoding linear acceleration and the other responding to the derivative of linear acceleration (jerk; Angelaki 1992c). It should be added that the response vectors $1 and $2 do not necessarily represent the converging otolith afferent vectors (Angelaki 1992a). Generally, STC between any number of otolith afferents having polarization vectors with different orientations (not necesarily orthogonal) can create "two-dimensional" neurons.…”

Frequency-Dependent Spatiotemporal Tuning Properties of Non–Eye Movement Related Vestibular Neurons to Three-Dimensional Translations in Squirrel Monkeys

“…The advantages of 3D over 2D translational experiments have been previously demonstrated (Chen-Huang and Peterson 2006). As predicted by geometry, the tuning ratio, a measurement of spatiotemporal tuning properties (Angelaki 1992;Schor and Angelaki 1992), derived from 2D-only data sets might overestimate or underestimate the 3D ratio. Further, the likelihood of classifying simple (cosine-tuned) neurons as complex (STC) type increases if data, small in modulation and large in noise, are available only from a nonpreferred plane (Chen-Huang and Peterson 2006).…”

“…In other words, the sensitivity of translation-related responses becomes broadly tuned, whereas the phase changes gradually with stimulus direction. This complex tuning response has been successfully modeled as resulting from the summation of translation-related inputs that are in spatial and temporal quadrature (Angelaki 1992) or spatiotemporal convergence (STC).…”