Biology subjects

Angelaki, D.

Publications and source records attributed to Angelaki, D..

23 records · Page 2Linked to original sources

bioRxiv2019

A gravity-based three-dimensional compass in the mouse brain

Head direction cells in the mammalian limbic system are thought to function as an allocentric neuronal compass. Although traditional views hold that the compass of ground-dwelling species is planar, we show that head-direction cells in the rodent thalamus, retrosplenial cortex and cingulum fiber bundle are tuned to conjunctive combinations of azimuth, pitch or roll, similarly to presubicular cells in flying bats. Pitch and roll orientation tuning is ubiquitous, anchored to gravity, and independent of visual landmarks. When head tilts, azimuth tuning is affixed to the head-horizontal plane, but also uses gravity to remain anchored to the terrestrial allocentric world. These findings suggest that gravity defines all three degrees of freedom of the allocentric orientation compass, and only the azimuth component can flexibly remap to local cues in different environments. Collectively, these results demonstrate that a three-dimensional, gravity-based, neural compass is likely a ubiquitous property of mammalian species, including ground-dwelling animals.

neuroscience↗

bioRxiv2019

A re-interpretation of the experimental data of Shinder and Taube \"Three-dimensional Tuning of Head Direction Cells in Rats\", Journal of Neurophysiology 121(1), 2019.

In a recent study, Shinder and Taube (2019) concluded that head direction cells in the anterior thalamus of rats are tuned to one-dimensional (1D, yaw-only) motion exclusively, in contrast to recent findings in bats (Finkelstein et al. 2015), mice (Angelaki et al. 2016; Cham et al. 2017; Laurens et al. 2017), and rats (Page et al. 2017). Here we re-interpret the authors experimental results using model comparison and demonstrate that, contrary to their conclusions, their data actually supports the dual-axis rule (Page et al. 2017) and tilted azimuth model (Laurens and Angelaki 2018), where head direction cells use gravity to integrate 3D rotation signals about all cardinal axes of the head. We further show that this study is inconclusive regarding the presence of vertical orientation tuning; i.e. whether head direction cells encode 3D orientation in the horizontal and vertical planes conjunctively. Using model simulations, we demonstrate that, even if 3D tuning existed, the experimental protocol and data analyses used by Shinder and Taube (2019) would not have revealed it. We conclude that the actual experimental data of Shinder and Taube (2019) are compatible with the 3D properties of head direction cells discovered by other groups, yet incorrect conclusions were reached because of incomplete and qualitative analyses.

neuroscience↗

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

Inferring decoding strategies for multiple correlated neural populations

Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.\n\nAuthor SummaryThe neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animals percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models can be distinguished by an experiment that measures correlations while inactivating different brain areas.

neuroscience↗