This work is supported by grants from the National Institutes of Mental Health NRSA F32 MH120887-03 (to G.J.B.) and R01 NS045193 and R01 MH115750 (to S.S-H.W.). We thank Drs. Bas Koekkoek and Henk-Jan Boele for helpful discussions for optimizing the DEC setup and Drs. Yue Wang and Xiaoying Chen for helpful discussions for optimizing the DTSC setup.

The animal protocols described here have been approved by the Animal Care and Use Committees of Princeton University.
1. Setting up the SBC
<ol>
	<li>Connect the camera serial interface (CSI) cable to the Raspberry NoIR V2 camera and the camera port on the SBC.</li>
	<li>Download the operating system for the SBC onto the host computer. Write the operating system image to a micro secure digital (microSD) card. 
	NOTE: Detailed instructions for these procedures for a Raspberry Pi SBC can be found elsewhere23. The system has been tested using the following operating systems: Stretch, Buster, Bullseye.</li>
	<li>To enable secure shell communication, create an extensionless file called &#34;ssh&#34; in the boot partition of the microSD card. Once this is done, eject the microSD card from the host machine and insert it into the SBC microSD card slot. Power the SBC by plugging in its power supply.</li>
	<li>Prepare the SBC to accept a wired connection to the host.
	<ol>
		<li>Attach a monitor with an appropriate cable to the SBC. Open a terminal, type the command ifconfig and record the ethernet IP address of the SBC. 
		NOTE: Raspberry Pi model 3B+ has an HDMI display port, while model 4B has a micro-HDMI port.</li>
		<li>Go to the Interface tab of the Raspberry Pi configuration setting and enable the options for Camera, secure shell network protocol (SSH), and Virtual Network Computing (VNC).</li>
	</ol>
	</li>
	<li>Establish a wired connection between the host computer and the SBC.
	<ol>
		<li>Connect an ethernet cable to the ethernet port on the SBC and a host computer. Attach the other end of these cables to an ethernet switch.</li>
		<li>Use a virtual network computing client such as VNC viewer24 and access the desktop using the SBC IP address and the default authentication (user = &#34;pi&#34;, password = &#34;raspberry&#34;).</li>
	</ol>
	</li>
	<li>Download required software included in the protocol steps. 
	CAUTION: Change the default username and password to prevent unauthorized access to the SBC.
	<ol>
		<li>Enter the following command in the SBC terminal to download the rig software: 
		git clone --depth=1&#160;https://github.com/gerardjb/assocLearnRig</li>
		<li>Enter the following commands to download the necessary python libraries. 
		cd assocLearnRig 
		python3 setup.py</li>
		<li>To allow direct control over the microcontroller, connect to the SBC and download the microcontroller integrated development environment (IDE) following steps 1.6.4-1.6.7.</li>
		<li>Open the web browser on the SBC desktop and navigate to&#160;https://arduino.cc/en/software. Download the latest Linux ARM 32 bit version of the IDE.</li>
		<li>Open a terminal window on the SBC desktop and navigate to the downloads directory by typing cd Downloads/</li>
		<li>To install the IDE, type the following commands in the terminal: 
		tar -xf arduino-&#60;version&#62;-linuxarm.tar.xz 
		sudo mv arduino-&#60;version&#62; /opt 
		sudo /opt/arduino-&#60;version&#62;/install.sh 
		(here &#60;version&#62; is the version of the downloaded IDE)</li>
		<li>Open an instance of the microcontroller IDE on the SBC desktop. Select menu option Tools &#62; Manage Libraries. Install the &#34;Encoder&#34; library from Paul Stoffregen.</li>
	</ol>
	</li>
	<li>Expand SBC onboard memory with a USB thumb drive.
	<ol>
		<li>Insert a thumb drive to a USB port on the SBC. Use a USB 3.0 port if available.</li>
		<li>Type in the terminal ls -l /dev/disk/by-uuid/ to find the thumb drive and its unique reference (UUID). Record the UUID.</li>
		<li>To allow the pi user to write to the USB device, type the following commands one by one into the terminal: 
		sudo mkdir /media/usb 
		sudo chown -R pi:pi /media/usb 
		sudo mount /dev/sda1 /media/usb -o uid=pi,gid=pi 
		NOTE: The thumb drive can be added as a device that will auto-mount when the SBC restarts by adding the following line to the end of the fstab file at /etc/fstab: 
		&#8203;UUID=&#60;UUID from step 1.7.2&#62; /media/usb vfat auto,nofail,noatime,users,rw,uid=pi,gid=pi 0 0</li>
	</ol>
	</li>
</ol>
2. Wiring stimulus hardware and assembling stage
<ol>
	<li>Connect and prepare microcontrollers.
	<ol>
		<li>Connect the SBC to the programming port of the microcontroller (Arduino Due) with a USB2 type A to USB2 micro cable. 
		&#8203;NOTE: Use a high-quality cable such as the product in the Table of Materials to ensure proper operation.</li>
		<li>Locate &#34;dueAssocLearn.ino&#34; in the downloaded project repository. Open the sketch with the microcontroller IDE and upload it to the microcontroller connected to the SBC.</li>
		<li>Download and install the appropriate version of the Arduino IDE on the host computer.</li>
		<li>Connect the host computer to the microcontroller (Arduino Uno) with a USB2 type B to USB2 type A cable.</li>
		<li>Go to the GitHub repository (https://github.com/gerardjb/assocLearnRig) and&#160;download the &#34;DTSC_US.ino&#34; sketch to the host computer.</li>
		<li>On the host computer, run the microcontroller IDE and open the &#34;DTSC_US.ino&#34; sketch, then upload it to the microcontroller.</li>
	</ol>
	</li>
	<li>Attach wires to the microcontrollers, breadboard, LEDs, rotary encoder, stepper motor with driver, and solenoid valve with driver as indicated in the Fritzing diagram in Figure 2.</li>
	<li>Power the stepper motor and solenoid valve.
	<ol>
		<li>Properly wire one channel of a power supply to the +V and GND pins of the stepper motor driver.</li>
		<li>Turn on the power supply and set the attached channel voltage to 25 V. 
		&#8203;NOTE: If the connections between the stepper motor, driver, and power supply are correctly configured, a green indicator LED on the stepper motor driver will turn on.</li>
		<li>Properly wire the positive lead of a power supply to the solenoid valve driver hold voltage pin and the other positive lead to the spike voltage pin.</li>
		<li>Attach the negative leads to a ground shared with the control signal.</li>
		<li>Turn on the power supply and set the channel connected to the hold voltage to about 2.5 V and the channel connected to spike voltage to about 12 V.</li>
	</ol>
	</li>
	<li>Connect an air source regulated to a pressure of ~20 PSI to the solenoid valve using the luer adapter.</li>
	<li>Test that all stimulus components and camera are functioning properly.
	<ol>
		<li>Open a terminal on the SBC and type cd ~/assocLearnRig to navigate to the cloned GitHub repository.</li>
		<li>In the terminal, type python3 assocLearnRig_app.py to start the control graphical user interface.</li>
		<li>Start the camera stream by hitting the Stream button.</li>
		<li>Select the DEC Radio button, upload to the microcontroller, and start a session with default parameters by hitting the Start Session button. 
		&#8203;NOTE: After this step, a printout of the data log should appear in the terminal, the message on the camera stream should disappear, and the LED CS and solenoid valve US should turn on and off at appropriate times during each trial.</li>
		<li>After the session ends, repeat the previous steps with the DTSC Radio button selected. 
		NOTE: Sketches in the GitHub repository (&#34;testStepper.ino&#34;, &#34;testRotary.ino&#34;, and &#34;testSolenoid.ino&#34;) can be used to test individual components if the above steps do not provide satisfactory results.</li>
	</ol>
	</li>
	<li>Make the running wheel.
	<ol>
		<li>Cut a 3&#34; wheel from a foam roller. Drill a 1/4&#34; hole in the exact wheel center so that the wheel will not wobble when turned by the mouse&#39;s locomotion.</li>
		<li>Insert a 1/4&#34; shaft into the wheel and fix it in place using clamping hubs placed on each side of the wheel.</li>
	</ol>
	</li>
	<li>Affix the rotary encoder to a 4.5&#34; aluminum channel using an M3 bolt. Stabilize the aluminum channel on the aluminum breadboard using a right angle bracket with a 1/4&#34; bolt, nut, and washer as shown.</li>
	<li>Attach the wheel and rotary encoder using a shaft-coupling sleeve.</li>
	<li>Stabilize the free side of the wheel shaft with a bearing inserted in a right-angle end clamp installed on a breadboard-mounted optical post. 
	&#8203;NOTE: Ensure the wheel spins freely without wobbling when rotated by hand.</li>
	<li>Position the stimulus hardware, head restraint, infrared light array, and picamera around the assembled wheel.
	<ol>
		<li>Position the head restraints using optical posts and right angle post clamps so that the head posts are 1.5 cm in front of the wheel axle and 2 cm above the wheel surface. (Values are for a 20 g mouse).</li>
		<li>Position the CS LED and solenoid valve outlet used for the DEC US less than 1 cm from the eye used for DEC.</li>
		<li>Mount the stepper motor used for the DTSC US</li>
		<li>Mount the picamera on an optical post ~10 cm from where the animal will be. 
		&#8203;NOTE: The design for the picamera mount can be made on a 3D printer from the file in &#34;RaspPiCamMount1_1.stl&#34; in the GitHub repository.</li>
		<li>Place the infrared light array slightly above and directly facing the position of the face on the same side as the picamera.</li>
		<li>Make a tactile stimulus for DTSC by taping foam to the edge of a piece of acrylic mounted to a 1/4&#34; shaft using a clamping hub. Attach the tactile stimulus to the stepper motor shaft. 
		&#8203;NOTE: The design for the acrylic piece can be laser cut following the pattern in &#34;TactileStimDesign.pdf&#34; in the GitHub repository.</li>
	</ol>
	</li>
</ol>
3. Preparing and running behavior experiments
<ol>
	<li>Implanting mouse headplate.
	<ol>
		<li>Anesthetize a mouse using 2% isoflurane and head fix in a stereotactic frame.</li>
		<li>Apply an ophthalmic ointment to the eyes.</li>
		<li>Shave the scalp using soapy water and a sterile scalpel. Inject lidocaine directly underneath the skin of the incision site and clean the surgical site with povidone.</li>
		<li>Make an incision with a scalpel along the midline of the scalp from the back edge of the eyes to the back edge of the skull, being careful not to press too hard on the skull.</li>
		<li>Spread the incision open and clamp both sides with sterile hemostats to hold it open. Gently remove the periosteum using a cotton swab dipped with ethanol and allow the surface of the exposed skull to dry.</li>
		<li>Position the headplate level on the skull, making sure to position the front of the headplate posterior to the eyes. Use cyanoacrylate glue to attach the headplate to the skull and allow the glue to dry fully.</li>
		<li>Mix the dental cement powder (1 scoop), solvent (2 drops), and catalyst (1 drop) in a mixing dish and apply to all areas of exposed bone. Add layers until the surface is flush with the top edge of the headplate, making sure the headplate is securely attached to the skull.</li>
		<li>Suture the skin closed behind and in front of the headplate if necessary.</li>
		<li>Inject post-operative analgesia such as carprofen&#160;per institutional guidelines while allowing the animal to recover for at least 5 days.</li>
	</ol>
	</li>
	<li>Preparing for behavior sessions.
	<ol>
		<li>Allow the test animals to habituate to the platform by mounting them in the head restraint for 30-min sessions for 5 days preceding experiments. 
		NOTE: By the end of the habituation sessions, animals should run comfortably on the wheel.</li>
		<li>(DEC only) Prior to sessions, ensure that the solenoid valve outlet is centered on the target eye positioned &#62;1 cm away.</li>
		<li>(DEC only) Manually actuate an air puff using the push button. Ensure that the mouse promptly produces a blink without showing overt signs of stress such as adopting a hunched posture or grabbing the affected periocular region with the ipsilateral forepaw.</li>
		<li>(DTSC only) Prior to sessions, ensure that the tactile stimulus is centered on the animal&#39;s nose positioned ~1.5 cm away. 
		NOTE: When a DTSC behavioral session is not running, the stepper motor is automatically inactivated to allow manual repositioning.</li>
		<li>(DTSC only) In the SBC terminal, type python3 assocLearnRig_app.py to start the GUI.</li>
		<li>(DTSC only) Run a test session of three trials with the default parameters by hitting the Start Session button in the GUI.</li>
		<li>(DTSC only) Ensure that the logged data that prints to the terminal show a deflection of greater than 20 but less than 100 steps logged on the rotary encoder following the US on each trial. 
		&#8203;CAUTION: To avoid harm and reduce stress to the animal, start the stimulus farther from the animal and move it closer until the required conditions are met.</li>
	</ol>
	</li>
	<li>Running behavioral sessions with data logging.
	<ol>
		<li>Mount a mouse to the head restraint.</li>
		<li>In the terminal of the SBC, type python3 assocLearnRig_app.py to start the GUI.</li>
		<li>To allow camera recordings during the behavioral trials, hit the Stream button. 
		NOTE: Sessions can be run without a camera. In this case, only data from the rotary encoder and stimulus presentation timestamps are logged.</li>
		<li>Input identifying information for the animal into the Animal ID field and hit the Set button.</li>
		<li>Select either the DEC or DTSC from the radio button under the Session Type heading depending on which behavioral paradigm is desired.</li>
		<li>Input the desired experiment parameters to the fields below the Animal ID field and hit the Upload to Arduino button. 
		&#8203;NOTE: Details of the experiment parameters can be found in the GitHub repository README section.</li>
		<li>Hit the Start Session button to begin the session.</li>
		<li>When a session is initialized, data will begin logging in a new directory created in &#34;/media/usb&#34; in the SBC thumb drive mount point.</li>
	</ol>
	</li>
</ol>
4. Exporting and analyzing data
<ol>
	<li>To export all the recorded sessions to the host computer, open a command prompt and input the command pscp -r pi@Pi_IP_address:/media/usb* host_computer_destination, then authenticate with the SBC password. 
	NOTE: The above command is for a Windows machine. On Mac and Linux machines, use terminal and replace &#34;pscp&#34; with &#34;scp&#34;.</li>
	<li>Install Anaconda25 or another python package manager (PPM) on the host computer.</li>
	<li>Go to the GitHub repository and download &#34;analyzeSession.py&#34;, &#34;summarizeSessions.py&#34;, &#34;session2mp4s.py&#34;, and &#34;requirementsHost.txt&#34;.</li>
	<li>Open a PPM prompt and type conda install --file directory_containing_requirementsHostrequirements Host.txt to ensure that the Python package installation has the required python libraries.</li>
	<li>In the prompt, type cd directory_containing_analyzeData to navigate to the directory containing &#34;analyzeData.py&#34; and &#34;session2mp4s.py&#34;. Run the analysis program by typing python analyzeSession.py 
	NOTE: An error message will be generated if using a Python 2 version as python. To check the version, type python -V in the prompt.</li>
	<li>Select the directory containing the data when prompted. Directories with multiple subdirectories will be analyzed sequentially.</li>
	<li>For DEC sessions, for each session directory analyzed, select a region of interest (ROI) containing the mouse&#39;s eye from a trial average image. 
	NOTE: Final analysis data files and summary graphs will populate to a subdirectory of each analyzed session directory.</li>
	<li>Type python summarizeSessions.py to generate summary data across multiple sessions.</li>
	<li>Type in the prompt python session2mp4s.py to convert imaging data files into viewable .mp4 files.</li>
</ol>

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S., Li, L., Scott, B. W., Frankland, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tactile,+acoustic+and+vestibular+systems+sum+to+elicit+the+startle+reflex.">Tactile, acoustic and vestibular systems sum to elicit the startle reflex.</a> Neuroscience and Biobehavioral Reviews. 26 (1), 1-11 (2002).</li><li>. Raspberry Pi Operating system images Available from: <a target="_blank" href="https://www.raspberrypi.com/software/operationg-systems/">https://www.raspberrypi.com/software/operationg-systems/</a> (2021)</li><li>. VNC Server. VNC&#174; Connect Available from: <a target="_blank" href="https://www.realvnc.com/en/connect/download/vnc/">https://www.realvnc.com/en/connect/download/vnc/</a> (2021)</li><li>. Anaconda: The world's most popular data science platform Available from: <a target="_blank" href="https://xddebuganaconda.xdlab.co/">https://xddebuganaconda.xdlab.co/</a> (2021)</li><li>De Zeeuw, C. I., Ten Brinke, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motor+learning+and+the+cerebellum.">Motor learning and the cerebellum.</a> Cold Spring Harbor Perspectives in Biology. 7 (9), 021683 (2015).</li><li>Badura, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Normal+cognitive+and+social+development+require+posterior+cerebellar+activity.">Normal cognitive and social development require posterior cerebellar activity.</a> eLife. 7, 36401 (2018).</li><li>Koekkoek, S. K. E., Den Ouden, W. L., Perry, G., Highstein, S. M., De Zeeuw, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+kinetic+and+frequency-domain+properties+of+eyelid+responses+in+mice+with+magnetic+distance+measurement+technique.">Monitoring kinetic and frequency-domain properties of eyelid responses in mice with magnetic distance measurement technique.</a> Journal of Neurophysiology. 88 (4), 2124-2133 (2002).</li><li>Kloth, A. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cerebellar+associative+sensory+learning+defects+in+five+mouse+autism+models.">Cerebellar associative sensory learning defects in five mouse autism models.</a> eLife. 4, 06085 (2015).</li><li>Boele, H. -. J., Koekkoek, S. K. E., De Zeeuw, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cerebellar+and+extracerebellar+involvement+in+mouse+eyeblink+conditioning:+the+ACDC+model.">Cerebellar and extracerebellar involvement in mouse eyeblink conditioning: the ACDC model.</a> Frontiers in Cellular Neuroscience. 3, (2010).</li><li>Lin, C., Disterhoft, J., Weiss, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whisker-signaled+eyeblink+classical+conditioning+in+head-fixed+Mice.">Whisker-signaled eyeblink classical conditioning in head-fixed Mice.</a> Journal of Visualized Experiments: JoVE. (109), e53310 (2016).</li><li>Pereira, T. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+animal+pose+estimation+using+deep+neural+networks.">Fast animal pose estimation using deep neural networks.</a> Nature Methods. 16 (1), 117-125 (2019).</li><li>Mathis, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepLabCut:+markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning.">DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.</a> Nature Neuroscience. 21 (9), 1281-1289 (2018).</li></ol>

The authors have no conflicts of interest to disclose.

The platform with associated protocols outlined here can be used to reliably track animal behavior in two sensory associative learning tasks. Each task depends on intact communication through the climbing fiber pathway. In the design described here, we incorporate elements to facilitate learning and recording/perturbation of cerebellar response. These include a wheel to allow for free locomotion11,18 as well as head fixation. The wheel allows mouse subjects to lo...

Pavlovian conditioning of sub-second association between stimuli to elicit a conditioned response has long been used to probe cerebellar-dependent learning. For example, in classical delay eyeblink conditioning (DEC), animals learn to make a well-timed protective blink in response to a neutral conditional stimulus (CS; e.g., a flash of light or auditory tone) when it is paired repeatedly with an unconditional stimulus (US; e.g., a puff of air applied to the cornea) which always elicits a reflex blink, and which comes at or near the end of the CS. The learned response is referred to as a conditioned response (CR), while the reflex response is referred to as the unconditioned response (UR). In rabbits, cerebellum-specific lesions disrupt this form of learning1,2,3,4. Further, Purkinje cell complex spikes, driven by their climbing fiber inputs5, provide a necessary6,7 and sufficient8,9 signal for the acquisition of properly-timed CRs.
More recently, climbing fiber-dependent associative learning paradigms have been developed for head-fixed mice. DEC was the first associative learning paradigm to be adapted to this configuration10,11. DEC in head-fixed mice has been used to identify cerebellar regions11,12,13,14,15,16,17 and circuit elements11,12,13,14,15,18,19 that are required for task acquisition and extinction. This approach has also been used to demonstrate how the cellular-level physiological representation of task parameters evolves with learning13,15,16.
In addition to eyeblink, the delayed startle tactile conditioning (DTSC) paradigm was recently developed as a novel associative learning task for head-fixed mice20. Conceptually similar to DEC, DTSC involves the presentation of a neutral CS with a US, a tap to the face sufficient in intensity to engage a startle reflex21,22 as the UR. In the DTSC paradigm, both the UR and CR are read out as backward locomotion on a wheel. DTSC has now been used to uncover how associative learning alters cerebellar activity and patterns of gene expression20.
In this work, a method was developed for flexibly applying DEC or DTSC in a single platform. The stimulus and platform attributes are schematized in Figure 1. The design incorporates the capacity to track animal behavior with a camera as well as a rotary encoder to track mouse locomotion on a wheel. All aspects of data logging and trial structure are controlled by paired microcontrollers (Arduino) and a single-board computer (SBC; Raspberry Pi). These devices can be accessed through a provided graphical user interface. Here, we present a workflow for setup, experiment preparation and execution, and a customized analysis pipeline for data visualization.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>&#34;B&#34; Quick Base For C&#38;B METABOND - 10 mL bottle</td>
			<td>Parkell</td>
			<td>S398</td>
			<td>Dental cement solvent</td>
		</tr>
		<tr>
			<td>&#34;C&#34; Universal TBB Catalyst - 0.7 mL</td>
			<td>Parkell</td>
			<td>S371</td>
			<td>Catalyst</td>
		</tr>
		<tr>
			<td>#8 Washers</td>
			<td>Thorlabs</td>
			<td>W8S038</td>
			<td>Washers</td>
		</tr>
		<tr>
			<td>0.250&#34; (1/4&#34;) x 8.00&#34; Stainless Steel Precision Shafting</td>
			<td>Servocity</td>
			<td>634172</td>
			<td>1/4&#34; shaft</td>
		</tr>
		<tr>
			<td>0.250&#8221; (0.770&#34;) Clamping Hub</td>
			<td>Servocity</td>
			<td>545588</td>
			<td>Clamping hub</td>
		</tr>
		<tr>
			<td>1/4&#34; to 6 mm Set Screw Shaft Coupler- 5 pack</td>
			<td>Actobotics</td>
			<td>625106</td>
			<td>Shaft-coupling sleeve</td>
		</tr>
		<tr>
			<td>1/4&#34;-20 Cap Screws, 3/4&#34; Long</td>
			<td>Thorlabs</td>
			<td>SH25S075</td>
			<td>1/4&#34; bolt</td>
		</tr>
		<tr>
			<td>100 pcs 5 mm 395&#8211;400 nm UV Ultraviolet LED Light Emitting Diode Clear Round Lens 29 mm Long Lead (DC 3V) LEDs Lights +100 pcs Resistors</td>
			<td>EDGELEC</td>
			<td>&#8206;ED_YT05_U_100Pcs</td>
			<td>CS LEDs</td>
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		<tr>
			<td>2 m Micro HDMI to DVI-D Cable - M/M - 2 m Micro HDMI to DVI Cable - 19 pin HDMI (D) Male to DVI-D Male - 1920 x 1200 Video</td>
			<td>Star-tech</td>
			<td>&#8206;HDDDVIMM2M</td>
			<td>Raspberry Pi4B to monitor cable</td>
		</tr>
		<tr>
			<td>256 GB Ultra Fit USB 3.1 Flash Drive</td>
			<td>SanDisk</td>
			<td>&#8206;SDCZ430-256G-G46</td>
			<td>USB thumb drive</td>
		</tr>
		<tr>
			<td>3.3 V&#8211;5 V 4 Channels Logic Level Converter Bi-Directional Shifter Module</td>
			<td>Amazon</td>
			<td>B00ZC6B8VM</td>
			<td>Logic level shifter</td>
		</tr>
		<tr>
			<td>32 GB 95 MB/s (U1) microSDHC EVO Select Memory Card</td>
			<td>Samsung</td>
			<td>&#8206;MB-ME32GA/AM</td>
			<td>microSD card</td>
		</tr>
		<tr>
			<td>4.50&#34; Aluminum Channel</td>
			<td>Servocity</td>
			<td>585444</td>
			<td>4.5&#34; aluminum channel</td>
		</tr>
		<tr>
			<td>48-LED CCTV Ir Infrared Night Vision Illuminator</td>
			<td>Towallmark</td>
			<td>SODIAL</td>
			<td>Infrared light array</td>
		</tr>
		<tr>
			<td>4PCS Breadboards Kit Include 2PCS 830 Point 2PCS 400 Point Solderless Breadboards for Proto Shield Distribution Connecting Blocks</td>
			<td>REXQualis</td>
			<td>B07DL13RZH</td>
			<td>Breadboard</td>
		</tr>
		<tr>
			<td>5 Port Gigabit Unmanaged Ethernet Network Switch</td>
			<td>TP-Link</td>
			<td>&#8206;TL-SG105</td>
			<td>Ethernet switch</td>
		</tr>
		<tr>
			<td>5 V 2.5 A Raspberry Pi 3 B+ Power Supply/Adapter</td>
			<td>Canakit</td>
			<td>&#8206;DCAR-RSP-2A5</td>
			<td>Power supply for Raspberry Pi 3B+</td>
		</tr>
		<tr>
			<td>5-0 ETHILON BLACK 1 x 18&#34; C-3</td>
			<td>Ethicon</td>
			<td>668G</td>
			<td>Sutures</td>
		</tr>
		<tr>
			<td>6 mm Shaft Encoder 2000 PPR Pushpull Line Driver Universal Output Line Driver Output 5-26 V dc Supply</td>
			<td>Calt</td>
			<td>&#160;B01EWER68I</td>
			<td>Rotary encoder</td>
		</tr>
		<tr>
			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 1&#34;, 5 Pack</td>
			<td>Thorlabs</td>
			<td>TR1-P5</td>
			<td>Optical posts</td>
		</tr>
		<tr>
			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 2&#34;, 5 Pack</td>
			<td>Thorlabs</td>
			<td>TR2-P5</td>
			<td>Optical posts</td>
		</tr>
		<tr>
			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 4&#34;, 5 Pack</td>
			<td>Thorlabs</td>
			<td>TR4-P5</td>
			<td>Optical posts</td>
		</tr>
		<tr>
			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 6&#34;, 5 Pack</td>
			<td>Thorlabs</td>
			<td>TR6-P5</td>
			<td>Optical posts</td>
		</tr>
		<tr>
			<td>&#216;1/2&#34; Post Holder, Spring-Loaded Hex-Locking Thumbscrew, L = 2&#34;</td>
			<td>Thorlabs</td>
			<td>PH2</td>
			<td>Optical post holder</td>
		</tr>
		<tr>
			<td>Adapter-062-M X LUER LOCK-F</td>
			<td>The Lee Co.</td>
			<td>TMRA3201950Z</td>
			<td>Solenoid valve luer adapter</td>
		</tr>
		<tr>
			<td>Aeromat Foam Roller Size: 36&#34; Length</td>
			<td>Aeromat</td>
			<td>B002H3CMUE</td>
			<td>Foam roller</td>
		</tr>
		<tr>
			<td>Aluminum Breadboard 10&#34; x 12&#34; x 1/2&#34;, 1/4&#34;-20 Taps</td>
			<td>Thorlabs</td>
			<td>MB1012</td>
			<td>Aluminum breadboard</td>
		</tr>
		<tr>
			<td>Amazon Basics HDMI to DVI Adapter Cable, Black, 6 Feet, 1-Pack</td>
			<td>Amazon</td>
			<td>HL-007347</td>
			<td>Raspberry Pi3B+ to monitor cable</td>
		</tr>
		<tr>
			<td>Arduino&#160; Uno R3</td>
			<td>Arduino</td>
			<td>A000066</td>
			<td>Arduino Uno (microcontroller board)</td>
		</tr>
		<tr>
			<td>Arduino Due</td>
			<td>Arduino</td>
			<td>&#8206;A000062</td>
			<td>Arduino Due (microcontroller board)</td>
		</tr>
		<tr>
			<td>Bench Power Supply, Single, Adjustable, 3 Output, 0 V, 24 V, 0 A, 2 A</td>
			<td>Tenma</td>
			<td>72-8335A</td>
			<td>Power supply</td>
		</tr>
		<tr>
			<td>Clear Scratch- and UV-Resistant Cast Acrylic Sheet, 12&#34; x 24&#34; x 1/8&#34;</td>
			<td>McMaster Carr</td>
			<td>8560K257</td>
			<td>Acrylic sheet</td>
		</tr>
		<tr>
			<td>CNC Stepper Motor Driver 1.0&#8211;4.2 A 20&#8211;50 V DC 1/128 Micro-Step Resolutions for Nema 17 and 23 Stepper Motor</td>
			<td>Stepper Online</td>
			<td>B06Y5VPSFN</td>
			<td>Stepper motor driver</td>
		</tr>
		<tr>
			<td>Compact Compressed Air Regulator, Inline Relieving, Brass Housing, 1/4 NPT</td>
			<td>McMaster Carr</td>
			<td>6763K13</td>
			<td>Air source regulator</td>
		</tr>
		<tr>
			<td>Cotton Swab</td>
			<td>Puritan</td>
			<td>806-WC</td>
			<td>Cotton swab</td>
		</tr>
		<tr>
			<td>Dell 1908FP 19&#34; Flat Panel Monitor - 1908FPC</td>
			<td>Dell</td>
			<td>1908FPC</td>
			<td>Computer monitor</td>
		</tr>
		<tr>
			<td>Flex Cable for Raspberry Pi Camera</td>
			<td>Adafruit</td>
			<td>2144</td>
			<td>camera serial interface cable</td>
		</tr>
		<tr>
			<td>High Torque Nema 17 Bipolar Stepper Motor 92 oz&#183;in/65 N&#183;cm 2.1 A Extruder Motor</td>
			<td>Stepper Online</td>
			<td>17HS24-2104S</td>
			<td>Stepper motor</td>
		</tr>
		<tr>
			<td>Isoflurane</td>
			<td>Henry Schein</td>
			<td>66794001725</td>
			<td>Isoflurane</td>
		</tr>
		<tr>
			<td>Krazy Maximum Bond Permanent Glue, 0.18 oz.</td>
			<td>Krazy Glue</td>
			<td>KG483</td>
			<td>Cyanoacrylate glue</td>
		</tr>
		<tr>
			<td>Lidocaine HCl</td>
			<td>VetOne</td>
			<td>510212</td>
			<td>Lidocaine</td>
		</tr>
		<tr>
			<td>Low-Strength Steel Hex Nut, Grade 2, Zinc-Plated, 1/4&#34;-20 Thread Size</td>
			<td>McMaster Carr</td>
			<td>90473A029</td>
			<td>Nuts</td>
		</tr>
		<tr>
			<td>M3 x 50 mm Partially Threaded Hex Key Socket Cap Head Screws 10 pcs</td>
			<td>Uxcell</td>
			<td>A16040100ux1380</td>
			<td>M3 bolt</td>
		</tr>
		<tr>
			<td>NEMA 17 Stepper Motor Mount</td>
			<td>ACTOBOTICS</td>
			<td>555152</td>
			<td>Stepper motor mount</td>
		</tr>
		<tr>
			<td>Official Raspberry Pi Power Supply 5.1 V 3 A with USB C - 1.5 m long</td>
			<td>Adafruit</td>
			<td>4298</td>
			<td>Power supply for Raspberry Pi 4B</td>
		</tr>
		<tr>
			<td>Optixcare Dog &#38; Cat Eye Lube Lubricating Gel, 0.70-oz tube</td>
			<td>Optixcare</td>
			<td>142422</td>
			<td>Opthalimic ointment</td>
		</tr>
		<tr>
			<td>Precision Stainless Steel Ball Bearing, Shielded, Trade No. R188-2Z, 13000 rpm Maximum Speed</td>
			<td>McMaster-Carr</td>
			<td>3759T57</td>
			<td>Bearing</td>
		</tr>
		<tr>
			<td>Premium Female/Female Jumper Wires - 40 x 6&#34;</td>
			<td>Adafruit</td>
			<td>266</td>
			<td>Wires</td>
		</tr>
		<tr>
			<td>Premium Female/Male &#39;Extension&#39; Jumper Wires - 40 x 6&#34; (150 mm)</td>
			<td>Adafruit</td>
			<td>826</td>
			<td>Wires</td>
		</tr>
		<tr>
			<td>Premium Male/Male Jumper Wires - 40 x 6&#34;</td>
			<td>Adafruit</td>
			<td>758</td>
			<td>Wires</td>
		</tr>
		<tr>
			<td>Radiopaque L-Powder for C&#38;B METABOND - 5 g</td>
			<td>Parkell</td>
			<td>S396</td>
			<td>Dental cement powder</td>
		</tr>
		<tr>
			<td>Raspberry Pi (3B+ or 4B)</td>
			<td>Adafruit</td>
			<td>3775 or 4295</td>
			<td>Raspberry Pi</td>
		</tr>
		<tr>
			<td>Raspberry Pi NoIR Camera Module V2 - 8MP 1080P30</td>
			<td>Raspberry Pi Foundation</td>
			<td>RPI3-NOIR-V2</td>
			<td>Raspberry NoIR V2 camera</td>
		</tr>
		<tr>
			<td>Right-Angle Bracket, 1/4&#34; (M6) Counterbored Slot, 8-32 Taps</td>
			<td>Thorlabs</td>
			<td>AB90E</td>
			<td>Right-angle bracket</td>
		</tr>
		<tr>
			<td>Right-Angle Clamp for &#216;1/2&#34; Posts, 3/16&#34; Hex</td>
			<td>Thorlabs</td>
			<td>RA90</td>
			<td>Right-angle optical post clamp</td>
		</tr>
		<tr>
			<td>Right-Angle End Clamp for &#216;1/2&#34; Posts, 1/4&#34;-20 Stud and 3/16&#34; Hex</td>
			<td>Thorlabs</td>
			<td>RA180</td>
			<td>Right-angle end clamp</td>
		</tr>
		<tr>
			<td>RJ45 Cat-6 Ethernet Patch Internet Cable</td>
			<td>Amazon</td>
			<td>&#8206;CAT6-7FT-5P-BLUE</td>
			<td>Ethernet cable</td>
		</tr>
		<tr>
			<td>Rotating Clamp for &#216;1/2&#34; Posts, 360&#176; Continuously Adjustable, 3/16&#34; Hex</td>
			<td>Thorlabs</td>
			<td>SWC</td>
			<td>Rotating optical post clamps</td>
		</tr>
		<tr>
			<td>Spike &#38; Hold Driver-0.1 TO 5 MS</td>
			<td>The Lee Co.</td>
			<td>IECX0501350A</td>
			<td>Solenoid valve driver</td>
		</tr>
		<tr>
			<td>Swivel Base Adapter</td>
			<td>Thorlabs</td>
			<td>UPHA</td>
			<td>Post holder adapter</td>
		</tr>
		<tr>
			<td>USB 2.0 A-Male to Micro B Cable, 6 feet</td>
			<td>Amazon</td>
			<td>&#8206;7T9MV4</td>
			<td>USB2 type A to USB2 micro cable</td>
		</tr>
		<tr>
			<td>USB 2.0 Printer Cable - A-Male to B-Male, 6 Feet (1.8 m)</td>
			<td>Amazon</td>
			<td>B072L34SZS</td>
			<td>USB2 type B to USB2 type A cable</td>
		</tr>
		<tr>
			<td>VHS-M/SP-12 V</td>
			<td>The Lee Co.</td>
			<td>INKX0514900A</td>
			<td>Solenoid valve</td>
		</tr>
		<tr>
			<td>Zinc-Plated Steel 1/4&#34; washer, OD 1.000&#34;</td>
			<td>McMaster Carr</td>
			<td>91090A108</td>
			<td>Washers</td>
		</tr>
	</tbody>
</table>

a flexible platform for monitoring cerebellum-dependent sensory associative learning

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

Workflow for DEC experiments and analysis 
Proper experimental parameter selection is important for successful delay eyeblink conditioning (DEC) training. For the data presented here, the GUI was used to choose a CS duration of 350 ms and a US duration of 50 ms. This pairing results in an inter-stimulus interval of 300 ms: long enough to prevent low-amplitude CR production10 and short enough to avoid getting into the regime of poor learning or trace conditioning, a process th...

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A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in ...

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Universidad de Cartagena UDEC

Research

JoVE Journal

Neuroscience

3.3K Views.  Princeton University. We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

Video: A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays

The memory of experiences and learned information is critical for organisms to make choices that aid their survival. C. elegans navigates its environment through neuron-specific detection of food and chemical odors1, 2, and can associate nutritive states with chemical odors3, temperature4, and the pathogenicity of a food source5.
Here, we describe assays of C. elegans associative learning and short- and long-term associative memory. We modified an aversive olfactory learning paradigm6 to instead produce a positive response; the assay involves starving ~400 worms, then feeding the worms in the presence of the AWC neuron-sensed volatile chemoattractant butanone at a concentration that elicits a low chemotactic index (similar to Toroyama et al.7). A standard population chemotaxis assay1 tests the worms' attraction to the odorant immediately or minutes to hours after conditioning.
After conditioning, wild-type animals' chemotaxis to butanone increases ~0.6 Chemotaxis Index units, its "Learning Index". Associative learning is dependent on the presence of both food and butanone during training. Pairing food and butanone for a single conditioning period ("massed training") produces short-term associative memory that lasts ~2 hours. Multiple conditioning periods with rest periods between ("spaced training") yields long-term associative memory (&lt;40 hours), and is dependent on the cAMP Response Element Binding protein (CREB),6 a transcription factor required for long-term memory across species.8
Our protocol also includes image analysis methods for quick and accurate determination of chemotaxis indices. High-contrast images of animals on chemotaxis assay plates are captured and analyzed by worm counting software in MatLab. The software corrects for uneven background using a morphological tophat transformation.9 Otsu's method is then used to determine a threshold to separate worms from the background.10 Very small particles are removed automatically and larger non-worm regions (plate edges or agar punches) are removed by manual selection. The software then estimates the size of single worm by ignoring regions that are above a specified maximum size and taking the median size of the remaining regions. The number of worms is then estimated by dividing the total area identified as occupied by worms by the estimated size of a single worm.
We have found that learning and short- and long-term memory can be distinguished, and that these processes share similar key molecules with higher organisms.6,8 Our assays can quickly test novel candidate genes or molecules that affect learning and short- or long-term memory in C. elegans that are relevant across species.

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays

Here we describe methods to test C. elegans associative learning and short- and long-term associative memory. These population assays employ the worms abilities to chemotax toward volatile odorants, and form positive associations upon pairing food with the chemoattractant butanone. Increasing the number of conditioning periods induces long-term memory.

The memory of experiences and learned information is critical for organisms to make choices that aid their survival. C. elegans navigates its environment ...

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs)1. Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue2. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (&gt;97% spike detection efficiency), has a low rate of false positive spike detection (&lt; 0.003 spikes/sec), and a high temporal precision (about 3 msec) 3. This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo3,4.

Understanding the function of the vertebrate central nervous system requires recordings from many neurons because cortical function arises on the level of populations of neurons. Here we describe an optical method to record suprathreshold neural activity with single-cell and single-spike resolution, dithered random-access scanning. This method records somatic fluorescence calcium signals from up to 100 neurons with high temporal resolution. A maximum-likelihood algorithm deconvolves the underlying suprathreshold neural activity from the somatic fluorescence calcium signals. This method reliably detects spikes with high detection efficiency and a low rate of false positives and can be used to study neural populations in vitro and in vivo.

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also ...

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.

We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Appetitive Associative Olfactory Learning in Drosophila Larvae

In the following we describe the methodological details of appetitive associative olfactory learning in Drosophila larvae. The setup, in combination with genetic interference, provides a handle to analyze the neuronal and molecular fundamentals of specifically associative learning in a simple larval brain.
Organisms can use past experience to adjust present behavior. Such acquisition of behavioral potential can be defined as learning, and the physical bases of these potentials as memory traces1-4. Neuroscientists try to understand how these processes are organized in terms of molecular and neuronal changes in the brain by using a variety of methods in model organisms ranging from insects to vertebrates5,6. For such endeavors it is helpful to use model systems that are simple and experimentally accessible. The Drosophila larva has turned out to satisfy these demands based on the availability of robust behavioral assays, the existence of a variety of transgenic techniques and the elementary organization of the nervous system comprising only about 10,000 neurons (albeit with some concessions: cognitive limitations, few behavioral options, and richness of experience questionable)7-10.
Drosophila larvae can form associations between odors and appetitive gustatory reinforcement like sugar11-14. In a standard assay, established in the lab of B. Gerber, animals receive a two-odor reciprocal training: A first group of larvae is exposed to an odor A together with a gustatory reinforcer (sugar reward) and is subsequently exposed to an odor B without reinforcement 9. Meanwhile a second group of larvae receives reciprocal training while experiencing odor A without reinforcement and subsequently being exposed to odor B with reinforcement (sugar reward). In the following both groups are tested for their preference between the two odors. Relatively higher preferences for the rewarded odor reflect associative learning - presented as a performance index (PI). The conclusion regarding the associative nature of the performance index is compelling, because apart from the contingency between odors and tastants, other parameters, such as odor and reward exposure, passage of time and handling do not differ between the two groups9.

Appetitive Associative Olfactory Learning in Drosophila Larvae

Drosophila larvae are able to associate odor stimuli with gustatory reward. Here we describe a simple behavioral paradigm that allows the analysis of appetitive associative olfactory learning.

In the following we describe the methodological  details of appetitive associative olfactory learning in Drosophila larvae. The setup, in combination with ...

Production and Isolation of Axons from Sensory Neurons for Biochemical Analysis Using Porous Filters

Neuronal axons use specific mechanisms to mediate extension, maintain integrity, and induce degeneration. An appropriate balance of these events is required to shape functional neuronal circuits. The protocol described here explains how to use cell culture inserts bearing a porous membrane (filter) to obtain large amounts of pure axonal preparations suitable for examination by conventional biochemical or immunocytochemical techniques. The functionality of these filter inserts will be demonstrated with models of developmental pruning and Wallerian degeneration, using explants of embryonic dorsal root ganglion. Axonal integrity and function is compromised in a wide variety of neurodegenerative pathologies. Indeed, it is now clear that axonal dysfunction appears much earlier in the course of the disease than neuronal soma loss in several neurodegenerative diseases, indicating that axonal-specific processes are primarily targeted in these disorders. By obtaining pure axonal samples for analysis by molecular and biochemical techniques, this technique has the potential to shed new light into mechanisms regulating the physiology and pathophysiology of axons. This in turn will have an impact in our understanding of the processes that drive degenerative diseases of the nervous system.

This article describes a neuronal culture system that can be used to obtain large quantities of pure axonal samples for biochemical and immunocytochemical analyses. This technique will allow an improved understanding of normal axonal physiology and signaling pathways leading to neurodegeneration.

Neuronal axons use specific mechanisms to mediate extension, maintain integrity, and induce degeneration. An appropriate balance of these events is ...

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer

A key challenge in neurobiology is to understand how neural circuits function to guide appropriate animal behaviors. Drosophila melanogaster is an excellent model system for such investigations due to its complex behaviors, powerful genetic techniques, and compact nervous system. Laboratory behavioral assays have long been used with Drosophila to simulate properties of the natural environment and study the neural mechanisms underlying the corresponding behaviors (e.g. phototaxis, chemotaxis, sensory learning and memory)1-3. With the recent availability of large collections of transgenic Drosophila lines that label specific neural subsets, behavioral assays have taken on a prominent role to link neurons with behaviors4-11. Versatile and reproducible paradigms, together with the underlying computational routines for data analysis, are indispensable for rapid tests of candidate fly lines with various genotypes. Particularly useful are setups that are flexible in the number of animals tested, duration of experiments and nature of presented stimuli. The assay of choice should also generate reproducible data that is easy to acquire and analyze. Here, we present a detailed description of a system and protocol for assaying behavioral responses of Drosophila flies in a large four-field arena. The setup is used here to assay responses of flies to a single olfactory stimulus; however, the same setup may be modified to test multiple olfactory, visual or optogenetic stimuli, or a combination of these. The olfactometer setup records the activity of fly populations responding to odors, and computational analytical methods are applied to quantify fly behaviors. The collected data are analyzed to get a quick read-out of an experimental run, which is essential for efficient data collection and the optimization of experimental conditions.

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer

We describe here a behavioral setup and data analysis method for assaying olfactory responses of up to 100 vinegar flies (Drosophila melanogaster). This system may be used with single or multiple olfactory stimuli, and adaptable for optogenetic activation or silencing of neuronal subsets.

A key challenge in neurobiology is to understand how neural circuits function to guide appropriate animal behaviors. Drosophila melanogaster is an ...

Fabrication of High Contact-Density, Flat-Interface Nerve Electrodes for Recording and Stimulation Applications

Many attempts have been made to manufacture multi-contact nerve cuff electrodes that are safe, robust and reliable for long term neuroprosthetic applications. This protocol describes a fabrication technique of a modified cylindrical nerve cuff electrode to meet these criteria. Minimum computer-aided design and manufacturing (CAD and CAM) skills are necessary to consistently produce cuffs with high precision (contact placement 0.51 &#177; 0.04 mm) and various cuff sizes. The precision in spatially distributing the contacts and the ability to retain a predefined geometry accomplished with this design are two criteria essential to optimize the cuff&#39;s interface for selective recording and stimulation. The presented design also maximizes the flexibility in the longitudinal direction while maintaining sufficient rigidity in the transverse direction to reshape the nerve by using materials with different elasticities. The expansion of the cuff&#39;s cross sectional area as a result of increasing the pressure inside the cuff was observed to be 25% at 67 mm Hg. This test demonstrates the flexibility of the cuff and its response to nerve swelling post-implant. The stability of the contacts&#39; interface and recording quality were also examined with contacts&#39; impedance and signal-to-noise ratio metrics from a chronically implanted cuff (7.5 months), and observed to be 2.55 &#177; 0.25 k&#8486; and 5.10 &#177; 0.81 dB respectively.

This article provides a detailed description on the fabrication process of a high contact-density flat interface nerve electrode (FINE). This electrode is optimized for recording and stimulating neural activity selectively within peripheral nerves.

Many attempts have been made to manufacture multi-contact nerve cuff electrodes that are safe, robust and reliable for long term neuroprosthetic ...

Drosophila Courtship Conditioning As a Measure of Learning and Memory

Many insights into the molecular mechanisms underlying learning and memory have been elucidated through the use of simple behavioral assays in model organisms such as the fruit fly, Drosophila melanogaster. Drosophila is useful for understanding the basic neurobiology underlying cognitive deficits resulting from mutations in genes associated with human cognitive disorders, such as intellectual disability (ID) and autism. This work describes a methodology for testing learning and memory using a classic paradigm in Drosophila known as courtship conditioning. Male flies court females using a distinct pattern of easily recognizable behaviors. Premated females are not receptive to mating and will reject the male&#39;s copulation attempts. In response to this rejection, male flies reduce their courtship behavior. This learned reduction in courtship behavior is measured over time, serving as an indicator of learning and memory. The basic numerical output of this assay is the courtship index (CI), which is defined as the percentage of time that a male spends courting during a 10 min interval. The learning index (LI) is the relative reduction of CI in flies that have been exposed to a premated female compared to na&#239;ve flies with no previous social encounters. For the statistical comparison of LIs between genotypes, a randomization test with bootstrapping is used. To illustrate how the assay can be used to address the role of a gene relating to learning and memory, the pan-neuronal knockdown of Dihydroxyacetone phosphate acyltransferase (Dhap-at) was characterized here. The human ortholog of Dhap-at, glyceronephosphate O-acyltransferase (GNPT), is involved in rhizomelic chondrodysplasia punctata type 2, an autosomal-recessive syndrome characterized by severe ID. Using the courtship conditioning assay, it was determined that Dhap-at is required for long-term memory, but not for short-term memory. This result serves as a basis for further investigation of the underlying molecular mechanisms.

Drosophila Courtship Conditioning As a Measure of Learning and Memory

This protocol describes a Drosophila learning and memory assay called courtship conditioning. This classic assay is based on a reduction of male courtship behavior after sexual rejection by a non-receptive premated female. This natural form of behavioral plasticity can be used to test learning, short-term memory, and long-term memory.

Many insights into the molecular mechanisms underlying learning and memory have been elucidated through the use of simple behavioral assays in model ...

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

A High-content Assay for Monitoring AMPA Receptor Trafficking

Postsynaptic trafficking of receptors to and from the cell surface is an important mechanism by which neurons modulate their responsiveness to different stimuli. The &#945;-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, which are responsible for fast excitatory synaptic transmission in neurons, are trafficked to and from the postsynaptic surface to dynamically alter neuronal excitability. AMPA receptor trafficking is essential for synaptic plasticity and can be disrupted in neurological disease. However, prevalent approaches for quantifying receptor trafficking ignore entire receptor pools, are overly time- and labor-intensive, or potentially disrupt normal trafficking mechanisms and therefore complicate the interpretation of resulting data. We present a high-content assay for the quantification of both surface and internal AMPA receptor populations in cultured primary hippocampal neurons using dual fluorescent immunolabeling and a near-infrared fluorescent 96-well microplate scanner. This approach facilitates the rapid screening of bulk internalized and surface receptor densities while minimizing sample material. However, our method has limitations in obtaining single-cell resolution or conducting live cell imaging. Finally, this protocol may be amenable to other receptors and different cell types, provided proper adjustments and optimization.

Neuronal excitability can be modulated through a dynamic process of endo- and exocytosis of excitatory ionotropic glutamate receptors. Described here is an accessible, high-content assay for quantifying surface and internal receptor population pools.

Postsynaptic trafficking of receptors to and from the cell surface is an important mechanism by which neurons modulate their responsiveness to different ...

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

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Chapters in this video

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Appetitive Associative Olfactory Learning in Drosophila Larvae

Production and Isolation of Axons from Sensory Neurons for Biochemical Analysis Using Porous Filters

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer

Fabrication of High Contact-Density, Flat-Interface Nerve Electrodes for Recording and Stimulation Applications

Drosophila Courtship Conditioning As a Measure of Learning and Memory

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

A High-content Assay for Monitoring AMPA Receptor Trafficking