Multi-Sensory Coding of Head Velocity in the Retrosplenial Cortex During Navigation

2021 ◽  
Author(s):  
Sepiedeh Keshavarzi ◽  
Edward Bracey ◽  
Richard Faville ◽  
Dario Campagner ◽  
Adam Tyson ◽  
...  
Neuron ◽  
2021 ◽  
Author(s):  
Sepiedeh Keshavarzi ◽  
Edward F. Bracey ◽  
Richard A. Faville ◽  
Dario Campagner ◽  
Adam L. Tyson ◽  
...  

2021 ◽  
Author(s):  
Sepiedeh Keshavarzi ◽  
Edward F. Bracey ◽  
Richard A. Faville ◽  
Dario Campagner ◽  
Adam L. Tyson ◽  
...  

The extent to which we successfully navigate the environment depends on our ability to continuously track our heading direction. Neurons that encode the speed and the direction of head turns during navigation, known as angular head velocity (AHV) cells, are fundamental to this process, but the sensory computations underlying their activity remain unknown. By performing chronic single-unit recordings in the retrosplenial cortex (RSP) of the mouse and tracking the activity of individual AHV neurons between freely moving and head-restrained conditions, we find that vestibular inputs dominate AHV signalling. In addition, we discover that self-generated optic flow input onto these neurons increases the gain and signal-to-noise ratio of angular velocity coding during navigation. Psychophysical experiments and neural decoding further reveal that vestibular-visual integration increases the perceptual accuracy of egocentric angular velocity and the fidelity of its representation by RSP ensembles. We propose that while AHV coding is dependent on vestibular cues, it also utilises vision to maximise navigation accuracy in nocturnal and diurnal environments.


1984 ◽  
Vol 45 (5) ◽  
pp. 939-943 ◽  
Author(s):  
J. Grilhé ◽  
N. Junqua ◽  
F. Tranchant ◽  
J. Vergnol

2011 ◽  
Vol 29 (supplement) ◽  
pp. 352-377 ◽  
Author(s):  
Seon Hee Jang ◽  
Frank E Pollick

The study of dance has been helpful to advance our understanding of how human brain networks of action observation are influenced by experience. However previous studies have not examined the effect of extensive visual experience alone: for example, an art critic or dance fan who has a rich experience of watching dance but negligible experience performing dance. To explore the effect of pure visual experience we performed a single experiment using functional Magnetic Resonance Imaging (fMRI) to compare the neural processing of dance actions in 3 groups: a) 14 ballet dancers, b) 10 experienced viewers, c) 12 novices without any extensive dance or viewing experience. Each of the 36 participants viewed short 2-second displays of ballet derived from motion capture of a professional ballerina. These displays represented the ballerina as only points of light at the major joints. We wished to study the action observation network broadly and thus included two different types of display and two different tasks for participants to perform. The two different displays were: a) brief movies of a ballet action and b) frames from the ballet movies with the points of lights connected by lines to show a ballet posture. The two different tasks were: a) passively observe the display and b) imagine performing the action depicted in the display. The two levels of display and task were combined factorially to produce four experimental conditions (observe movie, observe posture, motor imagery of movie, motor imagery of posture). The set of stimuli used in the experiment are available for download after this paper. A random effects ANOVA was performed on brain activity and an effect of experience was obtained in seven different brain areas including: right Temporoparietal Junction (TPJ), left Retrosplenial Cortex (RSC), right Primary Somatosensory Cortex (S1), bilateral Primary Motor Cortex (M1), right Orbitofrontal Cortex (OFC), right Temporal Pole (TP). The patterns of activation were plotted in each of these areas (TPJ, RSC, S1, M1, OFC, TP) to investigate more closely how the effect of experience changed across these areas. For this analysis, novices were treated as baseline and the relative effect of experience examined in the dancer and experienced viewer groups. Interpretation of these results suggests that both visual and motor experience appear equivalent in producing more extensive early processing of dance actions in early stages of representation (TPJ and RSC) and we hypothesise that this could be due to the involvement of autobiographical memory processes. The pattern of results found for dancers in S1 and M1 suggest that their perception of dance actions are enhanced by embodied processes. For example, the S1 results are consistent with claims that this brain area shows mirror properties. The pattern of results found for the experienced viewers in OFC and TP suggests that their perception of dance actions are enhanced by cognitive processes. For example, involving aspects of social cognition and hedonic processing – the experienced viewers find the motor imagery task more pleasant and have richer connections of dance to social memory. While aspects of our interpretation are speculative the core results clearly show common and distinct aspects of how viewing experience and physical experience shape brain responses to watching dance.


2021 ◽  
Vol 207 (3) ◽  
pp. 303-319
Author(s):  
Heiner Römer

AbstractTo perform adaptive behaviours, animals have to establish a representation of the physical “outside” world. How these representations are created by sensory systems is a central issue in sensory physiology. This review addresses the history of experimental approaches toward ideas about sensory coding, using the relatively simple auditory system of acoustic insects. I will discuss the empirical evidence in support of Barlow’s “efficient coding hypothesis”, which argues that the coding properties of neurons undergo specific adaptations that allow insects to detect biologically important acoustic stimuli. This hypothesis opposes the view that the sensory systems of receivers are biased as a result of their phylogeny, which finally determine whether a sound stimulus elicits a behavioural response. Acoustic signals are often transmitted over considerable distances in complex physical environments with high noise levels, resulting in degradation of the temporal pattern of stimuli, unpredictable attenuation, reduced signal-to-noise levels, and degradation of cues used for sound localisation. Thus, a more naturalistic view of sensory coding must be taken, since the signals as broadcast by signallers are rarely equivalent to the effective stimuli encoded by the sensory system of receivers. The consequences of the environmental conditions for sensory coding are discussed.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Gomes de Almeida-Filho ◽  
Bruna Del Vechio Koike ◽  
Francesca Billwiller ◽  
Kelly Soares Farias ◽  
Igor Rafael Praxedes de Sales ◽  
...  

AbstractHippocampal (HPC) theta oscillation during post-training rapid eye movement (REM) sleep supports spatial learning. Theta also modulates neuronal and oscillatory activity in the retrosplenial cortex (RSC) during REM sleep. To investigate the relevance of theta-driven interaction between these two regions to memory consolidation, we computed the Granger causality within theta range on electrophysiological data recorded in freely behaving rats during REM sleep, both before and after contextual fear conditioning. We found a training-induced modulation of causality between HPC and RSC that was correlated with memory retrieval 24 h later. Retrieval was proportional to the change in the relative influence RSC exerted upon HPC theta oscillation. Importantly, causality peaked during theta acceleration, in synchrony with phasic REM sleep. Altogether, these results support a role for phasic REM sleep in hippocampo-cortical memory consolidation and suggest that causality modulation between RSC and HPC during REM sleep plays a functional role in that phenomenon.


2021 ◽  
pp. 096372142199033
Author(s):  
Katherine R. Storrs ◽  
Roland W. Fleming

One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.


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