Biology subjects

Laurens, J.

Publications and source records attributed to Laurens, J..

3 recordsLinked to original sources

bioRxiv2018

Neuronal thresholds and correlations in the peripheral vestibular system during rotation discrimination

Neuronal and behavioral thresholds were measured simultaneously as trained male macaques performed a yaw rotation discrimination task in darkness. When corrected to account for variations in neuronal direction preferences, neurons in the vestibular nuclei and semicircular canal afferents had discrimination thresholds that were only two-fold smaller than behavioral thresholds. There was no significant trial-by-trial correlation between neuronal activity and perceptual decisions, despite the presence of significant pair-wise noise correlations. The lack of choice-related activity during rotation discrimination contrasts with the robust correlations observed previously between brainstem neurons and choices during translation perception. These results suggest task-dependent differences in subcortical processing of vestibular signals, as well as how signals related to perceptual decisions may propagate back to early stages of sensory processing.\n\nSIGNIFICANCE STATEMENTThis is the first ever simultaneous recordings of neural and behavioral thresholds during rotation discrimination. Its importance lies on the fact that the vestibular system provides an excellent model to probe origins of perception because directional selectivity signals are similar at many levels of processing, from afferents to cortex. The findings of similar neuronal and behavioral discrimination thresholds, significant inter-neuronal correlations, but lack of correlations between behavior and neuronal activity of both afferents and central brainstem neurons are intriguing and suggest task-dependent organization of early sensory areas.

neuroscience↗

bioRxiv2017

The brain compass: a perspective on how self-motion updates the head direction cell attractor

Head Direction cells form an internal compass that signals head azimuth orientation even in the absence of visual landmarks. It is well accepted that head direction properties are generated through a ring attractor that is updated using rotation self-motion cues. The properties and origin of this self-motion velocity drive remain, however, unknown. We propose a unified, quantitative framework whereby the attractor velocity input represents a multisensory self-motion estimate computed through an internal model that uses sensory prediction error based on vestibular, visual, and somatosensory cues to improve on-line motor drive. We show how context-dependent strength of recurrent connections within the attractor itself, rather than the self-motion input, explain differences in head direction cell firing between free foraging and restrained movements. We also summarize recent findings on how head tilt relative to gravity influences the azimuth coding of head direction cells, and explain why and how these effects reflect an updating self-motion velocity drive that is not purely egocentric. Finally, we highlight recent findings that the internal compass may be three-dimensional and hypothesize that the additional vertical degrees of freedom are defined based on global allocentric gravity cues.

neuroscience↗

bioRxiv2017

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally-generated ( passive) movements. However, these neurons show reduced responses during self-generated ( active) movements, indicating that the brain computes the predicted sensory consequences of motor commands in order to cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations established during passive movements remain unknown. Here we construct a Kalman filter that incorporates motor commands into a previously-established model of optimal passive self-motion estimation. We find that the simulated sensory error and feedback signals match experimentally measured neuronal response during active and passive head and trunk rotations and translations. We conclude that a single internal model of head motion can process motor commands and sensory afferent signals optimally, and we describe how previously identified neural responses in the brainstem and cerebellum may represent distinct nodes in these computations.

neuroscience↗