scholarly journals How a mouse licks a spout: Cortex-dependent corrections as the tongue reaches for, and misses, targets

2019 ◽  
Author(s):  
Tejapratap Bollu ◽  
Brendan Ito ◽  
Sam C. Whitehead ◽  
Brian Kardon ◽  
James Redd ◽  
...  

Abstract:Precise tongue control is necessary for drinking, eating, and vocalizing1, 2. Yet because tongue movements are fast and difficult to resolve, neural control of lingual kinematics remains poorly understood. Here we combine kilohertz frame-rate imaging and a deep learning based neural network to resolve 3D tongue kinematics in mice drinking from a water spout. Successful licks required previously unobserved corrective submovements (CSMs) which, like online corrections during primate reaches3–10, occurred after the tongue missed unseen, distant, or displaced targets. Photoinhibition of anterolateral motor cortex (ALM) impaired online corrections, resulting in hypometric licks that missed the spout. ALM neural activity reflected upcoming, ongoing, and past CSMs, as well as errors in predicted spout contact. Though less than a tenth of a second in duration, a single mouse lick exhibits hallmarks of online motor control associated with a primate reach, including cortex-dependent corrections after misses.

2021 ◽  
Vol 9 (7_suppl3) ◽  
pp. 2325967121S0015
Author(s):  
Dustin R. Grooms ◽  
Jed A. Diekfuss ◽  
Alexis B. Slutsky-Ganesh ◽  
Cody R. Criss ◽  
Manish Anand ◽  
...  

Background: Anterior cruciate ligament (ACL) injury is secondary to a multifactorial etiology encompassing anatomical, biological, mechanical, and neurological factors. The nature of the injury being primarily due to non-contact mechanics further implicates neural control as a key injury-risk factor, though it has received considerably less study. Purpose: To determine the contribution of neural activity to injury-risk mechanics in ecological sport-specific VR landing scenarios. Methods: Ten female high-school soccer players (15.5±0.85 years; 165.0±6.09 cm; 59.1±11.84 kg) completed a neuroimaging session to capture neural activity during a bilateral leg press and a 3D biomechanics session performing a header within a VR soccer scenario. The bilateral leg press involved four 30 s blocks of repeated bilateral leg presses paced to a metronome beat of 1.2 Hz with 30 s rest between blocks. The VR soccer scenario simulated a corner-kick, requiring the participant to jump and head a virtual soccer ball into a virtual goal (Figure 1A-E). Initial contact and peak knee flexion and abduction angles were extracted during the landing from the header as injury-risk variables of interest and were correlated with neural activity. Results: Evidenced in Table 1 and Figure 1 (bottom row), increased initial contact abduction, increased peak abduction, and decreased peak flexion were associated with increased sensory, visual-spatial, and cerebellar activity (r2= 0.42-0.57, p corrected < .05, z max > 3.1, table & figure 1). Decreased initial contact flexion was associated with increased frontal cortex activity (r2= 0.68, p corrected < .05, z max > 3.1). Conclusion: Reduced neural efficiency (increased activation) of key regions that integrate proprioceptive, visual-spatial, and neurocognitive activity for motor control may influence injury-risk mechanics in sport. The regions found to increase in activity in relation to higher injury-risk mechanics are typically activated to assist with spatial navigation, environmental interaction, and precise motor control. The requirement for athletes to increase their activity for more basic knee motor control may result in fewer neural resources available to maintain knee joint alignment, allocate environmental attention, and handle increased motor coordination demands. These data indicate that strategies to enhance efficiency of visual-spatial and cognitive-motor control during high demand sporting activities is warranted to improve ACL injury-risk reduction. [Figure: see text][Table: see text]


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Soulef Bouaafia ◽  
Seifeddine Messaoud ◽  
Randa Khemiri ◽  
Fatma Elzahra Sayadi

With the rapid advancement in many multimedia applications, such as video gaming, computer vision applications, and video streaming and surveillance, video quality remains an open challenge. Despite the existence of the standardized video quality as well as high definition (HD) and ultrahigh definition (UHD), enhancing the quality for the video compression standard will improve the video streaming resolution and satisfy end user’s quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high-quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360-degree multimedia compared to its predecessor high efficiency video coding (HEVC). Given its valuable results, the emerging field of deep learning is attracting the attention of scientists and prompts them to solve many contributions. In this study, we investigate the deep learning efficiency to the new VVC standard in order to improve video quality. However, in this work, we propose a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Thus, the VVC conventional in-loop filtering will be replaced by the suggested WSE-DCNN technique that is expected to eliminate the compression artifacts in order to improve visual quality. Numerical results demonstrate the efficacy of the proposed model achieving approximately − 2.85 % , − 8.89 % , and − 10.05 % BD-rate reduction of the luma (Y) and both chroma (U, V) components, respectively, under random access profile.


2014 ◽  
Vol 57 (2) ◽  
pp. 374-388 ◽  
Author(s):  
Natalia Zharkova ◽  
Nigel Hewlett ◽  
William J. Hardcastle ◽  
Robin J. Lickley

Purpose In this study, the authors compared coarticulation and lingual kinematics in preadolescents and adults in order to establish whether preadolescents had a greater degree of random variability in tongue posture and whether their patterns of lingual coarticulation differed from those of adults. Method High-speed ultrasound tongue contour data synchronized with the acoustic signal were recorded from 15 children (ages 10–12 years) and 15 adults. Tongue shape contours were analyzed at 9 normalized time points during the fricative phase of schwa-fricative-/a/ and schwa-fricative-/i/ sequences with the consonants /s/ and /ʃ/. Results There was no significant age-related difference in random variability. Where a significant vowel effect occurred, the amount of coarticulation was similar in the 2 groups. However, the onset of the coarticulatory effect on preadolescent /ʃ/ was significantly later than on preadolescent /s/, and also later than on adult /s/ and /ʃ/. Conclusions Preadolescents have adult-like precision of tongue control and adult-like anticipatory lingual coarticulation with respect to spatial characteristics of tongue posture. However, there remains some immaturity in the motor programming of certain complex tongue movements.


2020 ◽  
Author(s):  
Nils Wagner ◽  
Fynn Beuttenmueller ◽  
Nils Norlin ◽  
Jakob Gierten ◽  
Juan Carlos Boffi ◽  
...  

Light-field microscopy (LFM) has emerged as a powerful tool for fast volumetric image acquisition in biology, but its effective throughput and widespread use has been hampered by a computationally demanding and artefact-prone image reconstruction process. Here, we present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our network delivers high-quality reconstructions at video-rate throughput and we demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity.


2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.


2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.


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