DeAngelis, G. | bioRxiv Science

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

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↗