Saccadic Eye Movements Minimize the Consequences of Motor Noise

The durations and trajectories of our saccadic eye movements are remarkably stereotyped. We have no voluntary control over these properties but they are determined by the movement amplitude and, to a smaller extent, also by the movement direction and initial eye orientation. Here we show that the stereotyped durations and trajectories are optimal for minimizing the variability in saccade endpoints that is caused by motor noise. The optimal duration can be understood from the nature of the motor noise, which is a combination of signal-dependent noise favoring long durations, and constant noise, which prefers short durations. The different durations of horizontal vs. vertical and of centripetal vs. centrifugal saccades, and the somewhat surprising properties of saccades in oblique directions are also accurately predicted by the principle of minimizing movement variability. The simple and sensible principle of minimizing the consequences of motor noise thus explains the full stereotypy of saccadic eye movements. This suggests that saccades are so stereotyped because that is the best strategy to minimize movement errors for an open-loop motor system

Direction-dependent excitatory and inhibitory ocular vestibular-evoked myogenic potentials (oVEMPs) produced by oppositely directed accelerations along the midsagittal axis of the head

Oppositely directed displacements of the head need oppositely directed vestibulo-ocular reflexes (VOR), i.e. compensatory responses. Ocular vestibular-evoked myogenic potentials (oVEMPs) mainly reflect the synchronous extraocular muscle activity involved in the process of generating the VOR. The oVEMPs recorded beneath the eyes when looking up represent electro-myographic responses mainly of the inferior oblique muscle. We aimed: (1) to study the properties of these responses as they were produced by head acceleration impulses to the forehead and to the back of the head; (2) to investigate the relationships between these responses and the 3-D linear head accelerations that might reflect the true stimulus that acts on the vestibular hair cells. We produced backward- and forward-directed acceleration stimuli in four conditions (positive and negative head acceleration impulses to the hairline and to the inion) in 16 normal subjects. The oVEMPs produced by backward- and forward-directed accelerations of the head showed consistent differences. They were opposite in the phase. The responses produced by backward accelerations of the head began with an initial negativity, n11; conversely, those produced by accelerations directed forward showed initially a positive response, p11. There was a high inter-subject correlation of head accelerations along the head anteroposterior and transverse axes, but almost no correlation of accelerations along the vertical axis of the head. We concluded that backward-directed head accelerations produced an initial excitatory response, and forward-directed accelerations of the head were accompanied by an initial inhibitory response. These responses showed dependence on acceleration direction in the horizontal plane of the head. This could be consistent with activation of the utricle

Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets