Locomotor Modulation of Somatosensory Cortex During TactileObject Recognition

Jayesh SharmaNeuroscience Honors Thesis

Advisors: Andrew Hires, Andrew Erskine, Sarah BottjerSpring 2021

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AbstractSensorimotor integration is crucial to coordinate the everyday actions we perform in ourrespective environments. Locomotion modulates activity within the somatosensory cortex,enhancing neural activity induced by whisker exploration in rodents. However, the extent towhich this modulation influences the integration of multiple whiskers, and thus perception, isunclear. To address this question, our goal was to quantify encoding of object features bywhisker touch in primary somatosensory cortex (S1) with and without locomotion. We designedand implemented a freely-moving gap crossing task that required locomotion to approach,explore and discriminate a complex object using one or more whiskers. Using an overheadcamera and Bonsai tracking software, we were able to automatically track animal gap crossing,use successful crossings to trigger water reward, and quantify discrimination performance. Micelearned to associate gap crossing with water reward, but had difficulty discriminating objectswith any number of whiskers. We suspected this was due to failure to learn the more complexoperant aspects of the task. To overcome this challenge, we adapted the task to a head-fixedparadigm, which simplified training and increased experimental control over locomotion. Wepresented head-fixed mice on a treadmill with varying objects within reach of multiple whiskerswhile recording neural activity in S1 with a silicon probe. We used high speed videography totrack complex patterns of whisker stimulation, which we correlated with single-unit neuralresponses. This work relating neural activity patterns in S1 to whisker deflection patterns, andhow locomotion influences this relation, is ongoing. Through this project, we built a novelgap-crossing and behavioral tracking apparatus which aimed to teach object discrimination, andset up an electrophysiology experimental system which allowed us to observe activemulti-whisker sensory responses with and without locomotion.

IntroductionSensorimotor integration is the process of sensory information informing motor output, and viceversa (Wolpert et al., 1998). From picking up a glass of water to playing video games, thiscoordination between sensory and motor information is vital; especially since it is known thatintegrating sensory information is required for precise motor control (Borich et al., 2015). In theexample of picking up a cup of water, we require an internal model of where our fingers lie inspace and simultaneous input from mechanosensory pathways leading to motor command ofour tightening fingers in order to complete the task. While sensorimotor integration is easy totake for granted in our everyday lives, impairment of it can lead to substantial disruptions inphysical function. In the case of neurodegenerative spinal injury or stroke, this coordination canbe impaired to the point of immobilization and paralysis (Edwards et al., 2019). Exploring howthis neural system functions can lead us towards understanding how we perceive the worldaround us, and in turn how sensorimotor deficits develop.

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Figure 1- Model of the sensorimotor feedback stages involved in accomplishing a motor task like ankle movement.An internal model is used to predict the sensory and motor components that will best match the desired outcome(figure from Kurz et al., 2014).

The mouse whisker system is an excellent model for exploring sensorimotor integration(Petersen, 2007). Mice actively sweep their whiskers back and forth while exploring their localenvironment, constantly generating sensory inputs (Diamond et al., 2008). These sensorysignals then alter the movements of the whiskers themselves, which is useful for improving theacquisition of tactile features (Crochet et al., 2011). For example, the position of the whiskers(motor output) must be integrated with touch (sensory input) to determine shape and location ofan object (Moore et al., 2013). This model system has been used to show how motor neuronscan control sensory information flow, and conversely, how sensory information controls whiskerkinetics and motion kinematics (Tsur et al., 2019). It has also been used to elucidate the circuitfor movement initiation in the whisker motor cortex (Sreenivasan et al., 2016).

Active whisking better reflects how animals whisk in their natural environments compared topassive whisker deflection tasks (Mosconi et al., 2010). It is an important component in a varietyof tactile tasks, such as texture discrimination (Carvell and Simons, 1990), object localization(Kleinfeld and Deschênes, 2011), and distance judgment (Krupa et al., 2001). In studies like theKleinfeld and Deschênes paper, the relative position of whiskers and objects are not changingsince these animals are head-fixed. A more natural setting for active whisking is when mice arefreely moving, where whisking induces different stimulation patterns from the same object(Alloway and Chakrabarti, 2009) due to variations in head angle and approach direction. Thisimplies that exploring tactile sense in the freely moving setting might provide a more completeunderstanding of how these sensorimotor circuits function.

Multiple studies have investigated whisker-object interactions for a single intact whisker(Deutsch et al., 2012, Hubatz et al., 2020), but these studies are likely missing critical aspects of

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how information is integrated between multiple whiskers. Progressive trimming of whiskersdisrupts distance judgement (Krupa et al., 2001), but with sufficient training, performance canoften reach levels comparable to a full field of whiskers (Pammer et al., 2013). Theseinconsistencies across studies suggest that rodents may adapt their whisking strategies, orattend to different whisker features, to maintain discrimination ability with reduced whiskercounts. Sensory discrimination with reduced whisker numbers is also ethologically relevant;dominant mice often aggressively trim the whiskers of non-dominant males in a groupenvironment (Strozik and Festing, 1981). An open question that remains unclear is how miceadapt their motor strategies and sensory integration to variations in whisker availability duringobject identification.

Hypothesis 1: Mice can differentiate between complex object topology using their whiskers,requiring multiple whiskers to do so effectively.

Animals can discriminate between simple object features like distance and angle by touchingwith single whiskers (Kleinfeld and Deschênes, 2011, Kim et al., 2020). In the context of objectrecognition however, multiple whiskers could offer more comprehensive information throughspatial summation (Brown et al., 2020), allowing more accurate or rapid recognition. The utilityof multiple whiskers for object identification may also depend on object complexity. Wehypothesized that mice could discriminate objects with single whiskers, but performance wouldbe higher with multiple whiskers. We further hypothesized that the difference in performancebetween single and multiple whisker investigations would increase when discriminating objectswith complex features, such as curves or protrusions.

To test our hypotheses, we developed a task where mice discriminated between objects ofvarying complexity using a varying number of whiskers. A significant portion of our work lay inmodifying the classic gap-crossing task (Jenkinson and Glickstein, 2000) to include a 2-choiceresponse component. These modifications are described below in the Results section,beginning with the methodology and reasoning behind the training set up. In brief, the classicgap-crossing experiment forces the animal to use its whiskers alone, rather than other touchsensors (e.g. paws, face) to reach across a gap, determine its distance, and cross for reward.Likewise, in our new task, mice extended over a gap to touch objects with only their largemystacial whiskers. Rather than cross, they signaled their perception of object identity bynavigating back to one of two water spots to claim a water reward. Animals learned through fourtraining stages inside our behavioral setup, which we call the Freely Moving ObjectDiscrimination Box. An advantage of this approach was that it allowed animals to be freelymoving while discriminating between varying objects, which is most similar to the many degreesof exploratory freedom found in natural conditions.

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Figure 2- Example of an animal reaching across the gap to interact with a presented object.This space forces the animal to use its whiskers and not other tactile sensors, like its paws. This is an example of atrial in stage 2 of training (Figure 6B1), where the lit lick port has been lighted after the animal crosses the gap. If theanimal proceeds to poke its snout into the port, it will receive water reward. Conversely, if the animal pokes its snoutinto the unlit port, the trial will reset and no water reward will be given until the animal crosses the gap again, lightingup the associated lick port.

Discrimination behaviors are accomplished through coordinated activity across distributedneural circuits (Deolindo et al., 2018). Within the context of somatosensory behavior, theprimary somatosensory cortex (S1) and motor cortex (M1) are key circuits which integratesensory and motor information (Ahissar and Kleinfeld, 2003, Bosman et al., 2011). In humans,these circuits are crucial for functions such as joint homeostasis (Riemann and Lephart, 2002)and hand movement (Yokoi et al., 2018). In mice, they also coordinate active whiskerexploration (Pais-Vieira et al., 2013). S1 neurons encode both the location and angle of stimulipresented to mouse whiskers (Cheung et al., 2019, Kim et al., 2020). These are the primarycomponents needed for object shape discrimination. Surprisingly, neural representations ofangle in S1 were found to be quite similar across layers 2-4 (Kim et al., 2020). We hypothesizedthat this similarity may be due to the use of only single whiskers in that discrimination task. Thegreater lateral connectivity between barrels of L2/3 vs. L4 suggests that differences in sensoryencoding across those layers may only emerge during multi-whisker touches. This hypothesis issupported by a recent report of non-linear summation of multi-whisker sensory input in L2/3 ofS1 (Lyall et al., 2020).

A consistent finding across cortical sensory regions in mice is an upregulation of activity duringlocomotion (Niell and Stryker, 2010). Moreover, locomotor activity differentially modulatesencoding of touch and whisker motion across layers of S1 (Ayaz et al., 2019). This suggests

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that locomotion may influence the degree of multi-whisker integration during tactilediscrimination. To investigate this question, we asked how multi-whisker touch is neurallyrepresented in S1, and what specific effects locomotive activity has on encoding those multiplesensory signals (i.e. how locomotion affects multi-whisker responses in S1).

Figure 3- Mouse brain cortical areas.A. Rostro-caudal location of cortical areas. M1 encompasses the presumptive motor area, V1 is the primary visualarea, and S1 is the Somatosensory region within which we were targeting the barrel field that receives sensoryinformation from the large mystacial whiskers.B. Coronal hemisection- cortical transfer from the thalamic barreloids to the barrels in S1’s layer IV.(figure from Lokmane and Garel, 2014).

Hypothesis 2: Locomotion increases non-linearities in multi-whisker touch summation in L2/3 ofS1, resulting in more separable neural representations between touched objects.

To test this hypothesis we designed an awake, head-fixed preparation in which mice werepresented with complex multi-whisker stimulation patterns while voluntarily running on atreadmill. Silicon polytrodes and high-speed videography were used to simultaneously recordS1 neuronal population activity across cortical layers, and whisker motion respectively. Byrelating S1 responses across cortical layers to multi-whisker deflection patterns, we canexamine the integration of sensory information from multiple whiskers and its relationship totactile shape. Moreover, we can compare these responses between stationary and locomotorperiods to understand how locomotion modulates this integration.

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MethodsHypothesis 1:

Behavioral taskWater-restricted animals were trained to approach the end of a raised platform, with a gapbetween the edge and a presented object. This gap forced the animal to interact with eachobject using their whiskers only, as only their extended head and whiskers could extend acrossthe gap. Depending on the identity of the object, water became available in one of two lickportson the sides of the area following tactile investigation. Five animals progressed through 4 stagesof the behavior training. All animals were wild-type BL6 mice, 8-weeks old when received.

Freely Moving Object Discrimination Box setupThe body of the freely moving and gap crossing setup (Figure 4) was a walkway for the animalsto approach the end of, where a gap between the platform and the respective object wasapparent. Before the edge, there were two lick ports on opposing sides at the end of the maze.With this shape, a left/right choice determines which objects the animals recognize and respondto. This was a 2-choice task, with opposing object classes (concave and convex-modeledobjects) held 90 degrees from each other on a Zaber-motorized presenter (model NEMA 08).Object presentation was done by either spinning the presenter 180 or 90 degrees (within avariance of 2 degrees to randomize rotation and vary motor sounds to the animals). Object typeone was matched with the right lick port, so if the animal is presented with that object type, theright lick port dispensed a water reward if the animal poked its head into that respective port(with the same logic existing for object type two and the left port). Conversely, if the animalinteracted with the wrong lick port after object presentation, no water would be rewarded andgap crossing had to occur again in order for the next trial to start and water reward to beachievable. Over time, animals were conditioned to associate a presented object with acorresponding water port (in the final stage of training).

Recording of the animal’s movements as it moved around the box and encountered the objectswas done with an overhead camera (Logitech C270). The gap was backlit with far-red LEDsmounted in a PVC enclosure with diffusing film, which maximized camera visibility with uniformlight illumination. Mice were given 3-4 μl of water reward for correct trials and no reward forincorrect trials, trained in a stepwise manner for differentiating between the two object classes.Lickports consisted of a plastic housing with space for the animals’ snout and a water spoutinside. A light beam trigger detected when animals reached for the water spout and triggeredwater release on appropriate trials.

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Figure 4 - Freely Moving Box workflow and designA. The workflow and motion tracking was done through Bonsai. First, a camera view of the walkway was captured.The view was then converted to gray scale, cropped, and directed contours from where to judge an animal enteringthe gap between the platform and presented object (as shown in Figure 5C2).B. Structure modeled in CAD before the physical structure was built.C. Freely moving gap setup was set up to give animals a left/right choice. The animals walk up to an object presenter,which in this case is oscillating between concave and convex shaped objects. Each of the two object types areassigned to one of the two lick ports, which each give a water reward if the animal matches that pairing.

Object design and productionThe objects were created through 3D modeling and 3D printing. These objects were modeledsuch that they were on 4 arms, so that we could present the different objects by rotating theZaber offset at 90 degrees between each of the objects.

BonsaiThe program Bonsai (Lopes et al., 2015) was utilized for mouse tracking, with the BPod platform(a hardware state machine) and MATLAB (MathWorks) used for experiment control (throughmetrics such as mouse reward/ punishment on each trial). Bonsai allowed us to set conditionsto trigger water reward only when the animal crossed the gap between the platform and thepresented object. The decision to cross the gap is forced by the two lickports the animal caninteract with, as the experiment would not progress to the next trial until the animal crossed thegap and made a choice between the ports.

The workflow in Bonsai involved animal tracking with the camera to capture the observable field,changing the color input into grayscale (since the animal’s body is opaque against our far-redbacklight), and threshold setting to compare and establish contours to differentiate when ananimal’s head crossed the gap (Figure 4A).

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Bpod was synchronized with Bonsai to trigger water reward only when the animal had poked itshead into the gap between the platform and the presented objects (as demonstrated in Figure5C2). We used Bonsai to map the camera field with a clear color contrast trigger in order tosynchronize behavior to reward output. This model was built to ensure that some interactionwith the animal’s whiskers and the presented object was done.

Animals were water restricted in this study in order to motivate them towards obtaining waterreward. After restriction, health status was assessed daily using a reported guideline (Guo et al.,2014). All procedures were performed in accordance with the University of Southern CaliforniaInstitutional Animal Care and Use Committee Protocols 20731 and 20788.

Figure 5- Animal tracking through BonsaiA. Far-red LEDs used in animal setup. Wiring and spacing was done to ensure maximum visibility in the camera field.B. Bpod and behavioral control tracking animal movements and lick port activation.Bpod controls the lickport dispensing while Bonsai is in the background sending a trigger to Bpod whenever theanimal reaches across the gap.C. Bonsai triggers a signal when an object crosses the red/green labeled box. Usually, an animal’s head would triggeran output signal when it entered the green box’s frame (as seen in RoiActivityDetected), but C2. a pen is used todemonstrate the stimulus here.

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Hypothesis 2:

To test our second hypothesis, a head fixed animal was placed on a treadmill, which monitoredlocomotion, and had multiple whiskers stimulated by a 3D printed wheel. A silicon electroderecorded S1 neural activity during whisker stimulation, allowing us to match neural response tothe physical stimulation.

Head fixing surgery protocolOur surgery methodology for head fixing the animal followed this procedure:Mice were anesthetized under isoflurane (3% induction, 1%–1.5% maintenance) and placedover a heated pad until steady breathing was observed. Vaseline applied to the eyes preventeddrying out during the procedure. Hair removal cream was placed on the scalp and left for 3minutes. Using a cotton tip swab, the cream was removed from the scalp until all fur wasdetached from the surgical area. For further analgesia during the procedure, dexamethasonewas injected intraperitoneal at 0.5 mg/kg, marcaine was injected under the scalp local to theincision site at 5 mg/kg, and buprenorphine was injected subcutaneously at 0.05 mg/kg.Alternate cleans in the area were done with ethanol and iodopovidone (3 times each). The scalpwas removed to expose an area of skull over S1 regions and the lambda and bregma points.Connective tissue was cut and scraped away until the skull was dry, using a light vacuum ifneeded. The area was roughed with a scalpel to enhance fixation. A head fixation post wasattached contralateral to our planned probe recording position, with the tip of the post alignedwith bregma, and affixed to the skull with alternating layers of superglue and dental cement.

After the head bar was secure, we drilled a 1x1 mm hole in the skull for electrophysiologicalrecordings. Drilling was done based on established stereotaxic coordinates for S1 (3.5mmlateral, 1.5 posterior to bregma; Cheung et al., 2019). The skull piece around the drilled areawas removed gently with forceps under 1% PBS.

Single unit recording during locomotionWe used silicon electrodes (single shank and 32 channels from NeuroNexus) to record singleneuron activity across multiple channels in S1 through the craniotomy. This was combined withthe Open Ephys GUI (Siegle et al., 2017) to obtain the neural activity recordings and matchobserved whisker motion to neuronal activity. A Zaber-rotated 3D-printed wheel with extendededges stimulated the whiskers of the head-fixed animal. A treadmill with a rotary encoderquantified speed of locomotion during whisker stimulation.

Whisker trackingDeepLabCut (DLC) is a 3D markerless pose estimation program (Mathis et al., 2018) that weused for whisker tracking. Matching this with our electrophysiological recordings allowed us tosee simultaneous physical activity matched with electrical neural firing. We compared thistracking with Janelia’s Whisk (Clack et al., 2012) whisker tracking program. Once the video wasimported, select frames were manually labeled using DLC and Whisk’s respective GUIs. ForDLC, these labeled frames were then used to train the software on motion tracking the rest of

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the video’s whisker motion, while Whisk continued frame-by-frame labeling with intermittentmanual intervention to ensure the correct video characteristics were being tracked.

Data analysisMatlab was primarily used for data analysis, with one-tailed t-test being the primary modeutilized for statistical significance of learning.

Statement of involvement: I was directly involved with the setup of the freely moving, gapcrossing setup, including its construction, program setup in Bonsai motion tracking, and figuringout the training logic. I was also responsible for the stage coding, maintenance, animal waterrestriction, and data analysis. For the electrophysiological experiments, I helped build therecording setup and was primarily responsible for the surgery, electrical recordings, and dataanalysis.

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ResultsHypothesis 1

Testing our first hypothesis, that complex objects require touches with multiple whiskers toachieve high discrimination performance, required the development of a novel behavioral taskand construction of a new behavior system from scratch. Our hypothesis was dependent on theanimals primarily using their whiskers to explore both their environment and the objects we werepresenting to them. Simultaneously, we needed to minimize the use of senses like sight todetermine how important whisker use was in object discrimination. The elevated platform webuilt helped to accomplish the task of having a gap between the animals’ walkway and theobject they needed to interact with (Figure 4B). The far-red LED setup was constructed to havethe platform be illuminated in a way where we could observe and record animal movements, butthe animals themselves couldn’t see (Figure 5A); mice are dichromatic and unable to view thefar-red wavelength. Besides altering object position and angle, the slightly-varying rotation onthe Zaber presenter ensured constancy of each respective object’s identification; the animalswere never presented with the exact same position of any of the objects throughout the trials, sothey couldn’t cheat by memorizing touch points on the respective objects referenced to thebehavioral arena.

Because it was difficult to force the animal to interact with the object with their whiskers, weprovided progressive incentives for them to do so. The operant training occurred in the followingstages:

Stage 1: Alternating Reward, no Gap Cross (Figure 6A1)We first trained mice to investigate the lickports to acquire water rewards. We cued the

mice visually with an illuminated white LED above the single lickport where water was availableon each trial. To enforce exploration of both lickports, the water-rewarding port was alternated.Mice rapidly solved this simple task, establishing an association between lickport and rewardwithin two sessions of training (Figure 6A2).

Stage 2: Gap Cross + Alternating Reward (Figure 6B1)The next challenge was to coax mice into exploring the object prior to receiving the

award. In this stage, the rewarded port was not yet contingent on the object identity. However,reward only became available following a gap cross. Mice reaching across the gap to explorethe object triggered part of our camera field (as shown in Figure 5C2), and a lickport becameactive. The rewarded port was again cued with a white LED, and the identity of the reward portalternated across trials. Mice rapidly made this association between gap cross and reward, andconnected it to the prior alternating water reward (Figure 6B2).

Stage 3: Gap Crossing + Random RewardThe animals then needed to be dishabituated from the lick ports lighting up alternatively.

The lighting order was randomized here to train the idea of non-repetitive reward associations.Animals mastered this task variation almost immediately, consistently obtaining water rewardfrom the lighted lick ports (Figure 6B3).

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Stage 4: Full Recognition with No LED (Figure 7A1)The final stage required weaning mice off of the LED reward cue, and establishing a new

stimulus reward association between the touched object identity and the lickports. The LEDswere turned off and the requirement to gap cross to the object was retained. Mice had muchmore difficulty learning in this final stage (Figure 7A2). The first mouse was trained for 1,973trials across 22 sessions without ever performing above chance. This showed a failure totransfer the reward association from the visual cue to the object. However, a follow-up cohort ofmice showed more promising results, maintaining >60% correct within 120 trials of training(Figure 7A3).

In summary, animals learned how to associate water reward with light-cued lick ports (Figure6A2), cross over the gap to get reward (Figure 6A4), and cross over to get reward fromrandomly-lighted lick ports (Figure 6A5). However, mice had more difficulty learning the laststage of training, which was crossing over and getting water reward from the lick ports (no lightcue) associated with the presented object (Figure 7A2). This may reflect an inability todiscriminate between objects with whiskers, or more likely a failure to transfer the learnedreward association from a visual stimulus to tactile stimulus. However, recent training in youngermice showed promising signs of learning occurring in the last stage (Figure 7A3), though moretraining sessions are required to assess the maximal performance of these mice. Onceachieved, we will assess the impact of whisker trimming on discrimination performance (toobserve whether learning would fall to chance after trimming, implying that learning wasoccurring directly because of whisker use), and perform control experiments where the objectwill be moved out of reach to demonstrate this is a touch dependent behavior.

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Figure 6 - Training stages 1-3, Alternating reward learningA1. Schematic for stage.A2. Animal trial results for stage 1, where learning is observed across two days. These results are for a single animal.B1. Schematic for stage 2 and 3.B2. Animal trial results for stage 2, where learning is observed across two days.B3. Animal trial results for stage 3, where learning is observed across two days.The data is plotted as fraction correct vs trial number. Fraction correct represents how often the animal made thecorrect choice (i.e. how often the animal correctly puts their nose into the intended lick port) over the prior 50 trials; inthis case, it is the lick port that lights up after the animal moves their nose over the setup boundary. Stage 1 trainedfor recognizing that the lit-up port was the correct one to go for the water reward (A1), and stage 2-3 placed thecondition of needing to cross the boundary and then alternating between the lit-up lick ports (B1).

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Figure 7 - Training stage 4 - Full recognition learningA1. Schematic for stage 4.A2. Animal trial results for stage 4. There were a few times when the animal attained recognition above chance(one-sample t test against chance with p value<0.05) but there was no consistent learning observed.A3. Trial examples where learning above chance occurred in the last stage.These are two animals that started training in stage 4. Preliminary testing in new animals shows some amount oflearning occurring (* signifies when learning above chance is occurring, analysis done with T test p value < 0.05).

Hypothesis 2

Head-Fixed Multi-Whisker StimulationIn order to understand how multi-whisker integration is represented in the cortex, we needed toobserve an animal’s neural response from whisker stimulation. Our design methodology isdetailed in the methods. The most significant achievement in this portion of the project wasestablishing a framework for our future locomotion experiments in the head-fixed setting.

We set up a head-fixed animal and placed a silicon probe in S1. We then stimulated theanimal’s whiskers using a 3D-printed wheel with a set assortment of edges and grooves(moving the whiskers in different directions), recording single units as time passed. We usedelectrophysiology, DeepLabCut (DLC), and Whisk to match single units with whisker stimulationand tracking, feeding in whisker motion video into DLC and Whisk. We created a pipeline ofextracting spike data from electrophysiology using the spike sorting program KiloSort (Pachitariuet al., 2016). With this, we have shown the synchronization between whisker touches and neuralspikes leads to multiple stable units recorded across S1 (Figure 8B1). Whisker touches werealso matched with single units to show electrophysiological output from physical stimulation(Figure 9b). Whisker tracking is in progress using DeepLabCut and Whisk (Figure 9a). Theseresults don’t yet answer the questions hypothesis 2 poses, but the setup is operational andready to execute the experiments needed to provide those answers.

Technical innovation: We set up the animal in a way where we can monitor bothelectrophysiological recordings and in which whiskers are being stimulated at a given timethrough our videography. In this way, ephys and whisker tracked DeepLabCut integratingtogether gives an interesting dataset where we can relate neural activity to complexmulti-whisker deflection patterns.

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Figure 8- Ephys rig experimental setupA1. Experimental setup- the animal was immobilized by head fixing and an electrode for ephys was inserted. Whilethe whiskers were stimulated by a 3D-printed wheel, whisker movements were tracked from a camera and angledmirror.A2. experiment schematic showing silicon probs going into S1. Recording example of electrophysiology traces shown(Siegle et al., 2017).B1. Single unit recordings across multiple channelsStable units with standard depolarization recorded over multiple probe channels, which allow us to distinguish activityoccurring because of whisker stimulation. In each pair, the left (autocorrelation) shows a contaminated unit whereasthe right (spike waveform) has a more isolated recording. Left is measured as autocorrelation vs spike lag time, whileright is measured as voltage vs time, centered around spike time. Multiple waveforms are overlaid on top of oneanother.

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Figure 9a - Whisker trackingA1. Predicted whisker tracking positions from the deep-learning model in DeepLabCut.A2. Whisker tracking done in Whisk. Scale bar shows a length of 5 mm.

Figure 9b -Whisker motion with matched unitsWhisker video synchronized to unit firing in S1. Each black bar represents spike time, with more output seen whenwhiskers are stimulated by rotation of the wheel. Each row represents a channel in the silicon probe.

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DiscussionWith our new behavioral task, we were able to see the animals master the basics of gapcrossing and reward association, but tactile discrimination between presented objects was notconsistently achieved. Implementing these experiments in younger animals has shownpromising results, with multiple animals showing instances of learning above chance in the laststage (Figure 7A3). However, optimization of this learning setup and more training is needed tosee if our hypothesis, that multiple whiskers are required to discriminate convex from concaveobjects, is true. A recent preprint reports that multiple whiskers are required for this type ofdiscrimination in a head-fixed context (Rodgers et al., 2020), but our freely moving context mayshow differences due to the greater degrees of exploratory freedom which head motion allows.

Training animals to distinguish between a binary choice has its own set of difficulties andextraneous variables which need to be explored and discussed. The challenges we wentthrough in the stage 4 training may have been partly due to our initial training set coming from asingle, older animal, or they may be inherent to our choice of training progression. We need toinvestigate whether learning issues were due to age (Wallis et al., 2016) or other factorsdiminishing learning, and implement those lessons into our future trials. Other studies haveshown more extensive training setups, such as separated training regions giving animals clear‘reward zones’ (Hu et al., 2018) and specifically-timed trials with clear ending cutoffs (Montuoriand Honey, 2016), that we can apply to our gap-cross setting. We are currently progressing newanimals through our learning stages, with a goal to see if discrimination learning is easier toacquire with a more distinct set of objects, rather than just the convex/concave objects we havein use. We have developed a rich set of complex 3D object classes (created by iterativeprogramming and modeling software, PTC Creo) which we can use to extend discriminationexperiments once high performance is achieved. These classes involve sub-divisions, such as3D-modeled cones with small variations between each iteration to test if multi-whisker touch ismore important for differentiation of complex object shapes.

While we have designed, constructed and used two brand new behavior and electrophysiologysystems in the course of this research, I have acquired S1 electrophysiological recordings fromonly a single animal to investigate how multi-whisker inputs drive neural firing. We are alsoconstrained by only one type of whisker stimulation, an open-loop structured wheel. Successfultargeting of the whisker field in S1 to record electrical activity is a process that needs to be moreconsistent. Despite issues of behavioral variance and learning in the first part of ourinvestigation, the head-fixed setting showed clear sensory signals being derived frommechanical stimulation of whiskers; we observe change in S1 activity when active tactilestimulation occurs i.e. whisker stimulation vs. not (Figure 9b). Increasing our sample size wouldallow us to assess how repeatable and representative these synchronized stimulations andrecordings are in the barrel cortex. Synchronizing whisker-tracking with theseelectrophysiological responses gives us a large dataset to make quantitative predictions of howS1 responds to whisker touch and touch forces. This would then allow assessment of non-linearsummation of multi-whisker stimulation.

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During head-fixed electrophysiology, the mouse was able to run freely on a wheel with a rotaryencoder that monitored running speed. In future analysis, we will use this signal to divide theneuronal response data into periods where the animal was stationary vs. locomoting. We canthen compare S1 responses to multi-whisker deflection patterns in these two states and askhow locomotion affects non-linear summation of whisker inputs; and how this modulation differsacross cortical layers. Given that S1 L4 neurons are primarily sensitive to touch events (Hires etal., 2015) and L2/3 cells combine this response with motor feedback and inputs from adjacentbarrel columns (Hooks et al., 2011), we might hypothesize that the degree of non-linear whiskersummation may be selectively modulated in L2/3 over L4 neurons. Showing this would providevaluable insight into the separation of function across cortical layers. Regardless of the outcomeof this future work, the behaviors and systems developed here have the potential to lead to adeeper understanding of the mechanisms of sensorimotor integration in the mammalian brain.

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