What the Whiskers Tell the Brain

Campagner, Dario; Evans, Mathew H.; Loft, Michaela; Petersen, Rasmus S.

doi:10.1016/j.neuroscience.2017.08.005

Cited by 38 publications

(54 citation statements)

References 107 publications

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“…An important feature of video-based whisker tracking is that allows non-invasive measurement not only of kinematics but also of mechanical forces/moments acting on the whiskers (4,12,19,21). The physical basis for this is that the shape of a whisker contains information about these mechanical factors.…”

Section: Resultsmentioning

confidence: 99%

“…Substantial progress towards the long-standing ambition of measuring “total movements made by the intact animal” (1) is coming from the application of powerful machine vision methods to video recordings of behaving animals (2). Since the whisker system is a major experimental model in neuroscience and since the whiskers are readily imageable (3,4), the whisker system is ideally suited to this endeavour. Tracking the whiskers of mice/rats has already deepened our understanding of active sensation and refined our capacity to relate behaviour to neural mechanisms (5–10) Our aim here was to develop a method to track whisker movements and whisker shape in 3D in behaving mice at millisecond temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Starting with the first “cinematographic” study of whisking by Welker in 1964, there is a 50 year history of increasingly sophisticated efforts to measure whisker movement from behaving animals (4). Most studies have measured whisker movement only in the horizontal plane, using either linear CCD arrays (16,17) or high-speed imaging (6,18–20) However, horizontal plane imaging provides direct measurement of only one of the 3 angles that define 3D whisker orientation.…”

Section: Introductionmentioning

confidence: 99%

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A system for tracking whisker kinematics and whisker shape in three dimensions

Petersen

Rodriguez

Evans

et al. 2019

Preprint

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25Quantification of behaviour is essential for systems neuroscience. Since the whisker system is a major model system for 26 investigating the neural basis of behaviour, it is important to have methods for measuring whisker movements from 27 behaving animals. Here, we developed a high-speed imaging system that measures whisker movements simultaneously 28 from two vantage points. We developed an algorithm that uses the 'stereo' video data to track multiple whiskers by 29 fitting 3D curves to the basal section of each target whisker. By using temporal information to constrain the fits, the 30 algorithm is able to track multiple whiskers in parallel with low error rate. We used the output of the tracker to produce a 31 3D description of each tracked whisker, including its 3D orientation and 3D shape, as well as bending-related mechanical 32 force. In conclusion, we present an automatic system to track whiskers in 3D from high-speed video, creating the 33 opportunity for comprehensive 3D analysis of sensorimotor behaviour and its neural basis. 34 Author summary 35The great ethologist Niko Tinbergen described a crucial challenge in biology to measure the "total movements made by 36 the intact animal". Advances in high-speed video and machine analysis of such data have made it possible to make 37 profound advances. Here, we target the whisker system. The whisker system is a major experimental model in 38 neurobiology and, since the whiskers are readily imageable, the system is ideally suited to machine vision. Rats and mice 39 explore their environment by sweeping their whiskers to and fro. It is important to measure whisker movements in 3D, 40 since whiskers move in 3D and since the mechanical forces that act on them are 3D. However, the problem of 41 automatically tracking whiskers in 3D from video has generally been regarded as prohibitively difficult. Our innovation 42 here is to extract 3D information about whiskers using a two-camera, high-speed imaging system and to develop 43 computational methods to infer 3D whisker state from the imaging data. Our hope is that this study will facilitate 44 comprehensive, 3D analysis of whisker behaviour and, more generally, contribute new insight into brain mechanisms of 45 perception and behaviour. 46 47 48 50 Substantial progress towards the long-standing ambition of measuring "total movements made by the intact animal" (1) is 51 coming from the application of powerful machine vision methods to video recordings of behaving animals (2). Since the 52 whisker system is a major experimental model in neuroscience and since the whiskers are readily imageable (3,4), the 53 whisker system is ideally suited to this endeavour. Tracking the whiskers of mice/rats has already deepened our 54 understanding of active sensation and refined our capacity to relate behaviour to neural mechanisms (5-10) Our aim here 55 was to develop a method to track whisker movements and whisker shape in 3D in behaving mice at millisecond temporal 56 resolution. 57Whisker movement is 3D. During each whisking...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A system for tracking whisker kinematics and whisker shape in three dimensions

Petersen

Rodriguez

Evans

et al. 2019

Preprint

Self Cite

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“…The most frequently found types ( Table 1) were 'sensory cue' neurons responding to the onset of stimulation ( Figure 3D) and 'decision/action/lick' cells activated just before goal-directed licking, the animal's learnt response to the target sequence ( Figure 3E). We note that GO and NOGO sequences shared a common initial segment, so that a neuron sensitive to stimulus onset and with a strongly adapting response to sustained stimulation would 'view' the sequences as being identical; many layer 2/3 neurons labelled as 'sensory cue' in the present task would likely appear as touch sensitive neurons in situations where an animal encounters objects with its whiskers [37][38][39].…”

Section: Neuronal Responses In Well-trained S1bf During Sequence Discmentioning

confidence: 86%

Learning a tactile sequence induces selectivity to action decisions and outcomes in the mouse somatosensory cortex

Bale

Bitzidou

Giusto

et al. 2020

Preprint

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Sequential temporal ordering and patterning are key features of natural signals used by the brain to decode stimuli and perceive them as sensory objects. To explore how cortical neuronal activity underpins sequence recognition, we developed a task in which mice distinguished between tactile 'words' constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Mice could respond to the earliest possible cues allowing discrimination, effectively solving the task as a 'detection of change'problem, but enhanced their performance when deliberating for longer. Optogenetic inactivation showed that both primary somatosensory 'barrel' cortex (S1bf) and secondary somatosensory cortex were necessary for sequence recognition. Twophoton imaging of calcium activity in S1bf layer 2/3 revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal's action decision and the trial outcome (presence or absence of a predicted reward). A large proportion of neurons were activated preceding goal-directed licking, thus reflecting the animal's learnt response to the target sequence rather than the sequence itself; these neurons were found in S1bf as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory responses: neurons responding to stimulus features were already found in naïve mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf sequence learning results in neurons whose activity reflects the learnt association between the target sequence and licking, rather than a refined representation of sensory features.

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“…Our study builds on previous work which developed whisker-based object localisation in head-fixed mice, along with a mechanics framework and experimental methods for estimating the mechanical forces associated with whisker-pole interaction (Birdwell et al, 2007;O'Connor et al, 2010a;Clack et al, 2012;Pammer et al, 2013;Campagner et al, 2016Campagner et al, , 2017. Our task is novel compared to previous rodent object localisation tasks in that it is a three-choice task.…”

Section: A New Task For Investigation Of Active Perceptual Decision Mmentioning

confidence: 99%

Prediction of Choice From Competing Mechanosensory and Choice-Memory Cues During Active Tactile Decision Making

Campagner

Evans

Chlebikova

et al. 2018

Preprint

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Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Mice were trained to localise a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision we estimated whisker-object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosenory and choice-memory signals; and provides a new task, well-suited for future study of the neural basis of active perceptual decisions.

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What the Whiskers Tell the Brain

Cited by 38 publications

References 107 publications

A system for tracking whisker kinematics and whisker shape in three dimensions

A system for tracking whisker kinematics and whisker shape in three dimensions

Learning a tactile sequence induces selectivity to action decisions and outcomes in the mouse somatosensory cortex

Prediction of Choice From Competing Mechanosensory and Choice-Memory Cues During Active Tactile Decision Making

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