Financial support from the Flanders Research Foundation FWO (contract n. 12C9112N to DL), the 7th Framework Programme of the European Commission (Marie Curie Network &#8220;C7&#8221;, contract n. 238214; ICT Future Emerging Technology &#8220;ENLIGHTENMENT&#8221; project, contract n. 306502), the Interuniversity Attraction Poles Program initiated by the Belgian Science Policy Office (contract n. IUAP-VII/20), and the University of Antwerp is kindly acknowledged.

The protocol described here complies with the recommendations and guidelines of the Ethics Committee of the Department of Biomedical Sciences of the University of Antwerp. This protocol requires the preparation of non-sentient material from the explanted brain of juvenile Wistar rats, obtained by approved humane euthanasia techniques.
1. Equipment Preparation
<ol>
	<li>Install and configure the data acquisition and stimulation system.
	<ol>
		<li>Use a personal computer (PC) equipped with a data acquisition (DAQ) card supported by Comedi to record signals and send analog control voltages to the electrophysiological amplifier. 
		NOTE: Comedi is a Linux module and library that supports a multitude of DAQ cards from the most common manufacturers: visit http://www.comedi.org for more information.</li>
		<li>In case a computer-controlled patch clamp amplifier is in use, employ a second PC besides the one dedicated to the amplifier control. 
		NOTE: While the latter may run a conventional operating system, the extra PC will be operating in real-time by means of a special operating system. Under these conditions, it is convenient to use a single monitor, mouse, and keyboard attached to the extra PC, while connecting by a remote desktop application to the dedicated PC.</li>
		<li>Download the ISO image of the Live CD containing a real-time Linux operating system with LCG preinstalled from&#160;http://www.tnb.ua.ac.be/software/LCG_Live_CD.iso and burn it on a blank CD or USB stick&#8221;.</li>
		<li>Simply insert the CD into the drive of the PC containing the DAQ card and start it. Alternatively, install LCG from its online source repository on a PC running the Linux operating system (e.g., Debian or Ubuntu). Consult the online manual for details on the installation procedure. The manual is available online at http://danielelinaro.github.io/dynclamp/lcg_manual.pdf.</li>
		<li>Boot from the live CD: this will automatically load a fully configured system. To do this, place the LCG Live CD in the computer CD-ROM drive and boot the computer from CD; select the real-time kernel (default option) as soon as the boot menu appears and wait for the system to initialize.</li>
		<li>Calibrate the DAQ card by typing at the command prompt: 
		sudo comedi_calibrate 
		or 
		sudo comedi_soft_calibrate 
		depending on whether the data-acquisition board supports hardware or software calibration, respectively (use the command sudo comedi_board_info to obtain information on the board).</li>
		<li>Set the appropriate analog-to-digital and digital-to-analog conversion factors: this requires having access to the manual of the cellular electrophysiological amplifier, and particularly to its specifications on its conversion factors.</li>
		<li>Use a text editor to specify the appropriate numerical values in the file /home/user/.lcg-env, for the environment variables AI_CONVERSION_FACTOR_CC, AI_CONVERSION_FACTOR_VC, AO_CONVERSION_FACTOR_CC, AO_CONVERSION_FACTOR_VC. 
		NOTE: These represent the input (AI) and output (AO) gains for current clamp (CC) and voltage clamp (VC) modes, and the conversion factors between the voltage commands provided by the computer and the current or voltages generated by the amplifier, respectively.</li>
		<li>Alternatively, use the LCG script provided (lcg-find-conversion-factors), to find the conversion factors of his or her system. 
		NOTE: The values computed by lcg-find-conversion-factors are guesses, which in some cases are required to be numerically truncated or rounded to reflect the exact values of the conversion factors.</li>
		<li>To use lcg-find-conversion-factors, start by connecting the &#39;model cell&#39; that often is purchased with the amplifier to the corresponding headstage. Then, open a terminal on the Linux machine where you are running the Live CD and enter the following command at the shell prompt: 
		lcg-find-conversion-factors -i $HOME/.lcg-env -o $HOME/.lcg-env&#160; 
		NOTE: In both cases (i.e., manual modification of /home/user/.lcg-env or usage of lcg-find-conversion-factors), close and open the terminal for the changes to take effect.</li>
		<li>If multiple headstages are used, set the conversion factors to the same values in all channels; if that is not possible, consult the LCG online manual to understand how to use multiple conversion factors in lcg-stimulus or how to produce configuration files that better suit the user&#8217;s needs.</li>
	</ol>
	</li>
</ol>
2. Preparation of Acute Brain Slices from the Somatosensory Cortex
<ol>
	<li>Preparation of solutions for electrophysiology.
	<ol>
		<li>Prepare Artificial Cerebro-Spinal Fluid (ACSF) by mixing (in mM) 125 NaCl, 2.5 KCl, 1.25 NaH2PO4, 26 NaHCO3, 25 glucose, 2 CaCl2, and 1 MgCl2. Prepare 10x&#160;stock solutions to reduce the preparation time on the day of the experiment. Prepare 2 L, of which one will be used for the preparation of the slices and the other for recording.</li>
		<li>Saturate the ACSF with 95% O2 and 5% CO2 for at least 30 min prior to the beginning of the procedure.</li>
		<li>For current clamp recordings, use an intracellular solution (ICS) containing (in mM) 115 K-gluconate, 20 KCl, 10 HEPES, 4 ATP-Mg, 0.3 Na2-GTP, 10 Na2-phosphocreatine. Prepare the solution in ice and filter it prior to the beginning of the recordings to eliminate the risk of clogging the pipette.</li>
	</ol>
	</li>
	<li>Brain extraction.
	<ol>
		<li>Anesthetize the animal placing the animal in an induction chamber with 4% Isoflurane and rapidly decapitate it using a guillotine or large scissors.</li>
		<li>Cut the skin along the midline and slide it to the ears.</li>
		<li>Using a fine pair of scissors cut the skull along the midline. Keep the blade as close as possible to the surface so as to minimize damage to the underlying brain. Open the skull with a pair of tweezers, use a spatula to sever the optic nerve and the brainstem and gently drop the brain in ice-cold ACSF.</li>
		<li>Separate the cerebellum and the two hemispheres with a scalpel (blade 24).</li>
		<li>Remove excess water from one of the two hemispheres and glue it on an inclined platform using a drop of superglue. Quickly add a few drops of ACSF over the brain and transfer it to the vibratome chamber. 
		NOTE: When preparing sagittal slices, the angle of the platform is important to avoid damaging the dendrites of pyramidal cells during the slicing procedure.</li>
	</ol>
	</li>
	<li>Preparation of the slices.
	<ol>
		<li>Position the blade over the brain and discard the first 2.5 -&#160;3 mm. Adjust the speed and frequency to limit damage to the surface of the slice while at the same time minimizing the time required for the slicing procedure.</li>
		<li>Set the thickness to 300 &#181;m and begin slicing. Once the blade has gone past the cortex, use a razor blade or a bent needle to cut above the hippocampus and at the edges of the cortical area of interest.</li>
		<li>Place the slices in a multi-well incubation chamber kept at&#160;32 - 34 &#176;C.</li>
		<li>Retract the blade and repeat points 2.3.2 and 2.3.3 until 5 - 8 slices are cut. The best slices are usually the ones where the blood vessels are parallel to the surface.</li>
		<li>Incubate the slices for 30 min after the last slice is placed in the chamber.</li>
	</ol>
	</li>
</ol>
3. Patch-clamp Recordings from Layer 5 Pyramidal Neurons
<ol>
	<li>Place a slice in the recording chamber and search for healthy cells. These cells usually have lower contrast, a smooth appearance and are not swollen.</li>
	<li>Inspect the slice under the microscope with the 40X magnification lens and search for cells in layer 5, located approximately 600 to 1,000 &#181;m from the surface of the brain.</li>
	<li>Once a suitable cell is found, load one third of the micropipette with ICS and place it in the headstage.</li>
	<li>On the personal computer running the live CD or the pre-configured Linux operating system, launch a command shell (e.g., bash) and at its prompt type the command lcg-zero. This ensures that the DAQ board is not driving the amplifier.</li>
	<li>Apply 30 - 50 mbar of positive pressure by pressing on the piston of a common syringe, connected by tubing to the pipette holder and, with the help of the microscope, place the pipette approximately 100 &#181;m above the slice. 
	NOTE: Place the pipette in a position that allows a direct route to the target cell, preferably using the approach mode of the micromanipulator.</li>
	<li>Acting on the controls the electrophysiology amplifier, adjust the pipette offset and output a test pulse (10 mV) in voltage clamp mode.</li>
	<li>Reduce the pressure to 10 - 30 mbar (depending on the pipette size) by withdrawing the piston of the syringe; gently approach the cell and check for the formation of a dimple by observing the image on the video camera monitor. Monitor the test pulse for an increase in resistance at all times, by watching the current waveform displayed on the oscilloscope connected to the electrophysiology amplifier (alternatively you can use the command lcg-seal-test to monitor the pipette resistance).</li>
	<li>Release the pressure and if necessary apply gentle negative pressure to the pipette to help seal formation when you notice an increase in pipette resistance and the formation of a &#39;dimple&#39; on the cell.</li>
	<li>While the seal forms, gradually decrease the holding potential to -70 mV.</li>
	<li>Once a gigaohm seal has been obtained, ensure that the holding current is between 0 -&#160;30 pA. Apply short pulses of negative pressure (suction) to break the membrane and establish the whole-cell configuration. Alternatively, you can inject strong and brief pulses of voltage (i.e., using the &#39;ZAP&#39; command on the amplifier or holding the cell at very negative) to rupture the membrane, depending on the preparation and glass pipette used.</li>
	<li>Switch to current clamp mode and verify that the resting membrane potential is typical of a healthy cell. For cortical pyramidal neurons using a potassium-gluconate-based solution, this value is usually between -65 and -75 mV.</li>
</ol>
4. Semi-automatic Characterization of a Neuron&#8217;s Electrical Response Properties
<ol>
	<li>Create a directory to store user&#8217;s data. In order to do this the employ a script included in the LCG live CD that creates folders based on the date. To use it, type at the command prompt 
	cd ~/experiments 
	lcg-create-experiment-folder -s psp,in_vivo_like 
	This will create a folder where the data for that cell will be saved (and a &#39;psp&#39; and &#39;in_vivo_like&#39; subfolders) and it will print its name to the terminal window; it is also possible to store additional information such as pipette resistance and cell type using this script.</li>
	<li>Change directory to the newly created folder using the command 
	cd ~/&#60;foldername&#62; 
	The folder name is the one displayed by the command lcg-create-experiment-folder and will have the timestamp of the current day (i.e., year-month-day), as in 20140331A01.</li>
	<li>Make sure that the amplifier is set to operate in current clamp mode, that the cables are connected and the external voltage command of the amplifier, if present, is enabled.</li>
	<li>Enter the command lcg-ecodeat the command prompt. This calls a series of commands (namely lcg-ap, lcg-vi, lcg-ramp, lcg-tau and lcg-steps), used to characterize basic response properties of the cell. lcg-ecode requires that the user specify two parameters: the amplitude of the 1 ms-long pulse of current used to elicit a single spike in the cell, and the maximum amplitude of the current ramp injected into the cell to find its rheobase. 
	Use the following command syntax: 
	lcg-ecode --pulse-amplitude X --ramp-amplitude Y 
	with a choice of the values X and Y (in pA) that are sufficient to make the cell fire in response to a 1 ms-long pulse and a sustained injection of current, respectively. 
	NOTE: These protocols require performing the numerical estimate of the &#39;electrode kernel&#39; in order to use the Active Electrode Compensation (AEC)15. A noisy current injection is used to estimate the kernel and the user is prompted to confirm the number of samples that make up the kernel. See 15 for detailed information on the meaning of the electrode kernel and how to choose the number of kernel samples.</li>
</ol>
5. Injection of Conductance through Simulated Synapses and Simulation of In Vivo-like Background Activity
<ol>
	<li>Injection of simulated excitatory post-synaptic potentials
	<ol>
		<li>Change to the directory where you will save the next experiment, by typing the following command at the command prompt of the shell: 
		cd psp/01</li>
		<li>Copy an LCG configuration file to the current directory and open it with a text editor (Nano in this example) by typing the following commands at the command prompt of the shell (this example configuration file is included in the source code and the live cd): 
		cp ~/local/src/lcg/configurations/epsp.xml&#160; 
		nano epsp.xml 
		NOTE: This is simply a text file with different entities connected to each other. For more details see the Representative Results section.</li>
		<li>If necessary edit the inputChannel, outputChannel, the inputConversionFactor and the outputConversionFactor in this file to match the user&#8217;s setup.</li>
		<li>Compute the electrode kernel needed to perform the active electrode compensation &#39; the method used by LCG to perform single electrode dynamic clamp &#39; by issuing the command 
		lcg-kernel 
		This will prompt for the number of points in the kernel. Again, select a number so that the electrode kernel covers the end of the exponential decay tail.</li>
		<li>Perform the dynamic clamp experiment using the command 
		lcg-experiment -c epsp.xml</li>
		<li>List the files and visualize the results by using the command 
		ls -l 
		lcg-plot-file -f last</li>
	</ol>
	</li>
	<li>Injection of simulated inhibitory post-synaptic potentials
	<ol>
		<li>Create a folder and copy the epsp.xml file to it by typing the following commands at the command prompt of the shell: 
		mkdir ../02 
		cp epsp.xml ../02/ipsp.xml 
		cd ../02</li>
		<li>Edit the configuration file by using a text editor: change the synaptic reversal potential and rise and decay time constants of the model synapse Exp2Synapse to the following: 
		parameters&#62; 
		&#60;E&#62;-80&#60;/E&#62; 
		&#60;tauRise&#62;0.8e-3&#60;/tauRise&#62; 
		&#60;tauDecay&#62;10e-3&#60;/tauDecay&#62; 
		&#60;parameters&#62; 
		Quit the text editor.</li>
		<li>Compute the electrode kernel and perform the experiment as in 5.1, by typing the following commands at the command prompt of the shell: 
		lcg-kernel 
		lcg-experiment -c ipsp.xml</li>
		<li>List the files and visualize the results, by typing the following commands at the command prompt of the shell: 
		ls -l 
		lcg-plot-file -f &#60;filename.h5&#62;</li>
	</ol>
	</li>
	<li>Simulation of in vivo-like background activity:
	<ol>
		<li>Change to the directory where you want to save the following experiment, as previously shown, by typing the following commands at the command prompt of the shell: 
		cd ../../in_vivo_like/01</li>
		<li>Copy the configuration file from LCG source directory, by typing the following commands at the command prompt of the shell: 
		cp ~/local/src/lcg/configurations/in_vivo_like.xml&#160; 
		nano in_vivo_like.xml 
		NOTE: This file is simply the concatenation of the previous ones; two Poisson-point processes that generate spike trains, which in turn feed inhibitory and excitatory model synapses, generate the background activity.</li>
		<li>Adjust the DAQ configuration parameters for the user&#8217;s setup, as described in 5.1.3 and exit the editor.</li>
		<li>Compute the electrode kernel and perform the experiment as in 5.1, by typing the following commands at the command prompt of the shell: 
		lcg-kernel 
		lcg-experiment -c in_vivo_like.xml -n 10 -i 3 
		The &#39;-n 10&#39; and &#39;-i 3&#39; switches indicate that the stimulation should be repeated 10 times at intervals of three sec.</li>
		<li>Visualize the raw traces by using the following command at the command prompt of the shell: 
		lcg-plot-file -f all</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Saleem, A. B., Ayaz, A., Jeffery, K. J., Harris, K. D., Carandini, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integration+of+visual+motion+and+locomotion+in+mouse+visual+cortex.">Integration of visual motion and locomotion in mouse visual cortex.</a> Nature neuroscience. 16, 1864-1869 (2013).</li><li>Ahrens, M. B., Li, J. M., 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=Brain-wide+neuronal+dynamics+during+motor+adaptation+in+zebrafish.">Brain-wide neuronal dynamics during motor adaptation in zebrafish.</a> Nature. 485 (7399), 471-477 (2012).</li><li>Paz, J. T., Davidson, T. J., 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=Closed-loop+optogenetic+control+of+thalamus+as+a+tool+for+interrupting+seizures+after+cortical+injury.">Closed-loop optogenetic control of thalamus as a tool for interrupting seizures after cortical injury.</a> Nature neuroscience. 16 (1), 64-70 (2013).</li><li>Wallach, A., Eytan, D., Gal, A., Zrenner, C., Marom, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuronal+response+clamp.">Neuronal response clamp.</a> Frontiers in neuroengineering. 3 (April), 3 (2011).</li><li>Linaro, D., Couto, J., Giugliano, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Command-line+cellular+electrophysiology+for+conventional+and+real-time+closed-loop+experiments.">Command-line cellular electrophysiology for conventional and real-time closed-loop experiments.</a> Journal of neuroscience. 230, 5-19 (2014).</li><li>Sharp, A., O&#8217;Neil, M., Abbott, L. F., Marder, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+clamp:+computer-generated+conductances+in+real+neurons.">Dynamic clamp: computer-generated conductances in real neurons.</a> Journal of neurophysiology. 69 (3), 992-995 (1993).</li><li>Robinson, H. P., Kawai, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Injection+of+digitally+synthesized+synaptic+conductance+transients+to+measure+the+integrative+properties+of+neurons.">Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons.</a> Journal of neuroscience methods. 49 (3), 157-165 (1993).</li><li>Vervaeke, K., Hu, H., Graham, L. J., Storm, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Contrasting+effects+of+the+persistent+Na++current+on+neuronal+excitability+and+spike+timing.">Contrasting effects of the persistent Na+ current on neuronal excitability and spike timing.</a> Neuron. 49 (2), 257-270 (2006).</li><li>White, J. A., Klink, R., Alonso, A., Kay, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noise+from+voltage-gated+ion+channels+may+influence+neuronal+dynamics+in+the+entorhinal+cortex.">Noise from voltage-gated ion channels may influence neuronal dynamics in the entorhinal cortex.</a> Journal of neurophysiology. 80 (1), 262-269 (1998).</li><li>Destexhe, a., Rudolph, M., Fellous, J. M., Sejnowski, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluctuating+synaptic+conductances+recreate+in+vivo-like+activity+in+neocortical+neurons.">Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons.</a> Neuroscience. 107 (1), 13-24 (2001).</li><li>Fellous, J. -. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+Persistent+Activity+by+Background+Inhibition+in+an+In+Vitro+Model+of+a+Cortical+Microcircuit.">Regulation of Persistent Activity by Background Inhibition in an In Vitro Model of a Cortical Microcircuit.</a> Cerebral Cortex. 13 (11), 1232-1241 (2003).</li><li>Gal, A., Eytan, D., Wallach, A., Sandler, M., Schiller, J., Marom, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics+of+excitability+over+extended+timescales+in+cultured+cortical+neurons.+The+Journal+of+neuroscience.">Dynamics of excitability over extended timescales in cultured cortical neurons. The Journal of neuroscience.</a> the official journal of the Society for Neuroscience. 30 (48), 16332-16342 (2010).</li><li>Wang, Y., Toledo-Rodriguez, M., 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=Anatomical,+physiological+and+molecular+properties+of+Martinotti+cells+in+the+somatosensory+cortex+of+the+juvenile+rat.">Anatomical, physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat.</a> The Journal of physiology. 561 (Pt 1), 65-90 (2004).</li><li>Wang, Y., Gupta, A., Toledo-Rodriguez, M., Wu, C. Z., Markram, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anatomical,+physiological,+molecular+and+circuit+properties+of+nest+basket+cells+in+the+developing+somatosensory+cortex).">Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex).</a> Cerebral cortex (New York, N.Y). 12 (4), 395-410 (1991).</li><li>Brette, R., Piwkowska, Z., 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=High-resolution+intracellular+recordings+using+a+real-time+computational+model+of+the+electrode.">High-resolution intracellular recordings using a real-time computational model of the electrode.</a> Neuron. 59 (3), 379-391 (2008).</li><li>Rutishauser, U., Kotowicz, A., Laurent, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+closed-loop+presentation+of+sensory+stimuli+conditional+on+the+internal+brain-state+of+awake+animals.">A method for closed-loop presentation of sensory stimuli conditional on the internal brain-state of awake animals.</a> Journal of neuroscience. 215 (1), 139-155 (2013).</li><li>Margrie, T., Brecht, M., Sakmann, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo,+low-resistance,+whole-cell+recordings+from+neurons+in+the+anaesthetized+and+awake+mammalian+brain.">In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain.</a> Pflugers Archiv European Journal of Physiology. 444 (4), 491-498 (2002).</li><li>Graham, L., Schramm, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+Vivo+Dynamic-Clamp+Manipulation+of+Extrinsic+and+Intrinsic+Conductances:+Functional+Roles+of+Shunting+Inhibition+and+I+BK+in+Rat+and+Cat+Cortex.">In Vivo Dynamic-Clamp Manipulation of Extrinsic and Intrinsic Conductances: Functional Roles of Shunting Inhibition and I BK in Rat and Cat Cortex.</a> Dynamic Clamp: From Principles to Applications. , (2008).</li><li>Sakmann, B., Neher, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Single-channel recording. , (1995).</li><li>Molleman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Patch Clamping. , (2002).</li><li>Davie, J. T., Kole, M. H. P., 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=Dendritic+patch-clamp+recording.">Dendritic patch-clamp recording.</a> Nature Protocols. 1 (3), 1235-1247 (2006).</li><li>Gold, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Axon Guide for Electrophysiolog., &amp; Biophysics Laboratory Techniques... , (2007).</li><li>Mainen, Z. F., Sejnowski, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliability+of+spike+timing+in+neocortical+neurons.">Reliability of spike timing in neocortical neurons.</a> Science. 268 (5216), 1503-1506 (1995).</li><li>Buzs&#225;ki, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Action+potential+threshold+of+hippocampal+pyramidal+cells+in+vivo+is+increased+by+recent+spiking+activity.">Action potential threshold of hippocampal pyramidal cells in vivo is increased by recent spiking activity.</a> Neuroscience. 105 (1), 121-130 (2001).</li><li>Koch, C., Segev, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methods in Neuronal Modeling: From Synapses to Networks. , (1988).</li><li>Silberberg, G., Markram, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Disynaptic+inhibition+between+neocortical+pyramidal+cells+mediated+by+Martinotti+cells.">Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells.</a> Neuron. 53 (5), 735-746 (2007).</li><li>Berger, T. K., Silberberg, G., Perin, R., Markram, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brief+bursts+self-inhibit+and+correlate+the+pyramidal+network.">Brief bursts self-inhibit and correlate the pyramidal network.</a> PLoS biology. 8 (9), (2010).</li><li>Tsodyks, M., Pawelzik, K., Markram, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+networks+with+dynamic+synapses.">Neural networks with dynamic synapses.</a> Neural computation. 10 (4), 821-835 (1998).</li><li>Kapfer, C., Glickfeld, L. L., Atallah, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Supralinear+increase+of+recurrent+inhibition+during+sparse+activity+in+the+somatosensory+cortex.">Supralinear increase of recurrent inhibition during sparse activity in the somatosensory cortex.</a> Nature. 10 (6), 743-753 (2007).</li></ol>

The authors have nothing to disclose.

In this text a full protocol for the implementation of real-time, closed-loop single cell electrophysiological experiments was described, using the patch clamp technique and a recently developed software toolbox called LCG. To optimize the quality of the recordings it is crucial that the recording setup be properly grounded, shielded and vibration free: this ensures stable and lasting whole-cell access to the cell, which, together with the possibility of automating entire sections of the stimulation protocols, allows for...

In recent years, cellular electrophysiology has evolved from the traditional open-loop paradigm employed in voltage and current clamp experiments to modern closed-loop protocols. The best known closed-loop technique is perhaps the dynamic clamp6,7, which enabled the synthetic injection of artificial voltage-gated ion channels to determine the neuronal membrane voltage8, the in-depth study of the effects of non-deterministic flickering on ion channels on neuronal response dynamics9, as well as the recreation in vitro of realistic in vivo-like synaptic background activity10.
Other closed-loop paradigms that have been proposed include the reactive clamp11, to study in vitro the generation of self-sustained persistent activity, and the response clamp4,12, to investigate the cellular mechanisms underlying neuronal excitability.
Here we describe a powerful framework that allows applying a variety of closed-loop electrophysiological protocols in the context of whole-cell patch clamp recordings performed in acute brain slices. We show how to record somatic membrane voltage by means of patch clamp recordings in pyramidal neurons from the somatosensory cortex of juvenile rats and apply three different closed-loop protocols using LCG, a command-line-based software toolbox developed in the laboratory of Theoretical Neurobiology and Neuroengineering.
Briefly, the described protocols are, first&#160;the automated injection of a series of current clamp stimulus waveforms, relevant for the characterization of a large set of active and passive membrane properties. These have been suggested to capture the electrophysiological phenotype of a cell in terms of its response properties to a stereotyped series of stimulus waveforms. Known as the e-code of a cell (e.g., see&#160;13,14), such a collection of electrical responses is used by several laboratories to objectively classify neurons on the basis of their electrical properties. This includes the analysis of the stationary input-output transfer relationship (f-I curve), by an innovative technique that involves the closed-loop, real-time control of the rate of firing by means of a proportional-integral-derivative (PID) controller, second the recreation of realistic in vivo-like&#160;background synaptic activity in in vitro preparations10 and , third the artificial connection in real-time of two simultaneously recorded pyramidal neurons by means of a virtual GABAergic interneuron, which is simulated by the computer.
Additionally, LCG implements the technique known as Active Electrode Compensation (AEC)15, which allows implementing dynamic clamp protocols using a single electrode. This allows compensating undesired effects (artifacts) of the recording electrode that arise when it is used for delivering intracellular stimuli. The method is based on a non-parametric estimate of the equivalent electrical properties of the recording circuit.
The techniques and experimental protocols described in this paper can be readily applied in conventional open-loop voltage and current clamp experiments and can be extended to other preparations, such as extracellular4,16 or intracellular recordings in vivo17,18. The careful assembly of the setup for whole cell patch clamp electrophysiology is a very important step for stable, high quality recordings. In the following we assume that such an experimental setup is already available to the experimenter, and focus our attention on describing the usage of LCG. The reader is pointed to 19&#8211;22 for additional tips about optimization and debugging.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Tissue slicer</td>
			<td>Leica</td>
			<td>VT-1000S</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette puller</td>
			<td>Sutter</td>
			<td>P-97</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipettes</td>
			<td>WPI</td>
			<td>1B150F-4</td>
			<td>1.5/0.84 mm OD/ID, with filament</td>
		</tr>
		<tr>
			<td>Vibration isolation table</td>
			<td>TMC</td>
			<td>20 Series</td>
			<td></td>
		</tr>
		<tr>
			<td>Microscope</td>
			<td>Leica</td>
			<td>DMLFS</td>
			<td>40X Immersion Objective</td>
		</tr>
		<tr>
			<td>Manipulators</td>
			<td>Scientifica</td>
			<td>PatchStar</td>
			<td></td>
		</tr>
		<tr>
			<td>Amplifiers</td>
			<td>Axon Instruments</td>
			<td>MultiClamp 700B</td>
			<td>Computer controlled</td>
		</tr>
		<tr>
			<td>Data acquisition card</td>
			<td>National Instruments</td>
			<td>PCI-6229</td>
			<td>Supported by Comedi Linux Drivers</td>
		</tr>
		<tr>
			<td>Desktop computer</td>
			<td>Dell</td>
			<td>Optiplex 7010 Tower</td>
			<td>OS: real-time Linux</td>
		</tr>
		<tr>
			<td>Oscilloscopes</td>
			<td>Tektronix</td>
			<td>TDS-1002</td>
			<td></td>
		</tr>
		<tr>
			<td>Perfusion Pump</td>
			<td>Gibson</td>
			<td>MINIPULS3</td>
			<td>Used with R4 Pump head (F117606)</td>
		</tr>
		<tr>
			<td>Temperature controller</td>
			<td>Multichannel Systems</td>
			<td>TC02</td>
			<td>PH01 Perfusion Cannula</td>
		</tr>
		<tr>
			<td>Manometer</td>
			<td>Testo</td>
			<td>510</td>
			<td>Optional</td>
		</tr>
		<tr>
			<td>Incubator</td>
			<td>Memmert</td>
			<td>WB14</td>
			<td></td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>Sigma</td>
			<td>71376</td>
			<td>ACSF</td>
		</tr>
		<tr>
			<td>KCl</td>
			<td>Sigma</td>
			<td>P9541</td>
			<td>ACSF, ICS</td>
		</tr>
		<tr>
			<td>NaH2PO4</td>
			<td>Sigma</td>
			<td>S3139</td>
			<td>ACSF</td>
		</tr>
		<tr>
			<td>NaHCO3</td>
			<td>Sigma</td>
			<td>S6014</td>
			<td>ACSF</td>
		</tr>
		<tr>
			<td>CaCl2</td>
			<td>Sigma</td>
			<td>C1016</td>
			<td>ACSF</td>
		</tr>
		<tr>
			<td>MgCl2</td>
			<td>Sigma</td>
			<td>M8266</td>
			<td>ACSF</td>
		</tr>
		<tr>
			<td>Glucose</td>
			<td>Sigma</td>
			<td>G7528</td>
			<td>ACSF</td>
		</tr>
		<tr>
			<td>K-Gluconate</td>
			<td>Sigma</td>
			<td>G4500</td>
			<td>ICS</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>Sigma</td>
			<td>H3375</td>
			<td>ICS</td>
		</tr>
		<tr>
			<td>Mg-ATP</td>
			<td>Sigma</td>
			<td>A9187</td>
			<td>ICS</td>
		</tr>
		<tr>
			<td>Na2-GTP</td>
			<td>Sigma</td>
			<td>51120</td>
			<td>ICS</td>
		</tr>
		<tr>
			<td>Na2-Phosphocreatine</td>
			<td>Sigma</td>
			<td>P7936</td>
			<td>ICS</td>
		</tr>
	</tbody>
</table>

real-time electrophysiology: using closed-loop protocols to probe neuronal dynamics and beyond

Experimental neuroscience is witnessing an increased interest in the development and application of novel and often complex, closed-loop protocols, where the stimulus applied depends in real-time on the response of the system. Recent applications range from the implementation of virtual reality systems for studying motor responses both in mice1 and in zebrafish2, to control of seizures following cortical stroke using optogenetics3. A key advantage of closed-loop techniques resides in the capability of probing higher dimensional properties that are not directly accessible or that depend on multiple variables, such as neuronal excitability4 and reliability, while at the same time maximizing the experimental throughput. In this contribution and in the context of cellular electrophysiology, we describe how to apply a variety of closed-loop protocols to the study of the response properties of pyramidal cortical neurons, recorded intracellularly with the patch clamp technique in acute brain slices from the somatosensory cortex of juvenile rats. As no commercially available or open source software provides all the features required for efficiently performing the experiments described here, a new software toolbox called LCG5 was developed, whose modular structure maximizes reuse of computer code and facilitates the implementation of novel experimental paradigms. Stimulation waveforms are specified using a compact meta-description and full experimental protocols are described in text-based configuration files. Additionally, LCG has a command-line interface that is suited for repetition of trials and automation of experimental protocols.

In the previous sections, we have described how to use the software toolbox LCG to characterize the electrophysiological properties of L5 pyramidal cells and to recreate in vivo-like synaptic activity in a slice preparation. The use of a command-line interface and semi-automated protocol favor the reproducibility and efficiency of the experiment, which can have a large impact on the output and quality of the data produced. Additionally, since the data is saved in a consistent way, it is easy to extend the analys...

Watch this Scientific Journal Video about Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond at JoVE.com

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Closed-loop protocols are becoming increasingly widespread in modern day electrophysiology. We present a simple, versatile and inexpensive way to perform complex electrophysiological protocols in cortical pyramidal neurons in vitro, using a desktop computer and a digital acquisition board.

Experimental neuroscience is witnessing an increased interest in the development and application of novel and often complex, closed-loop protocols, where ...

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11.3K Views.  University of Antwerp. Closed-loop protocols are becoming increasingly widespread in modern day electrophysiology. We present a simple, versatile and inexpensive way to perform complex electrophysiological protocols in cortical pyramidal neurons in vitro, using a desktop computer and a digital acquisition board. 

Video: Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine interface to assess the robustness of different control-laws applied to a closed-loop image stabilization task. Taking advantage of the well-characterized fly visuomotor pathway we record the electrical activity from an identified, motion-sensitive neuron, H1, to control the yaw rotation of a two-wheeled robot. The robot is equipped with 2 high-speed video cameras providing visual motion input to a fly placed in front of 2 CRT computer monitors. The activity of the H1 neuron indicates the direction and relative speed of the robot's rotation. The neural activity is filtered and fed back into the steering system of the robot by means of proportional and proportional/adaptive control. Our goal is to test and optimize the performance of various control laws under closed-loop conditions for a broader application also in other brain machine interfaces.

We use a closed-loop fly-machine interface to investigate general principles in neuronal control.

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine ...

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

The cortex is spontaneously active, even in the absence of any particular input or motor output. During development, this activity is important for the migration and differentiation of cortex cell types and the formation of neuronal connections1. In the mature animal, ongoing activity reflects the past and the present state of an animal into which sensory stimuli are seamlessly integrated to compute future actions. Thus, a clear understanding of the organization of ongoing i.e. spontaneous activity is a prerequisite to understand cortex function. 
Numerous recording techniques revealed that ongoing activity in cortex is comprised of many neurons whose individual activities transiently sum to larger events that can be detected in the local field potential (LFP) with extracellular microelectrodes, or in the electroencephalogram (EEG), the magnetoencephalogram (MEG), and the BOLD signal from functional magnetic resonance imaging (fMRI). The LFP is currently the method of choice when studying neuronal population activity with high temporal and spatial resolution at the mesoscopic scale (several thousands of neurons). At the extracellular microelectrode, locally synchronized activities of spatially neighbored neurons result in rapid deflections in the LFP up to several hundreds of microvolts. When using an array of microelectrodes, the organizations of such deflections can be conveniently monitored in space and time. 
Neuronal avalanches describe the scale-invariant spatiotemporal organization of ongoing neuronal activity in the brain2,3. They are specific to the superficial layers of cortex as established in vitro4,5, in vivo in the anesthetized rat 6, and in the awake monkey7. Importantly, both theoretical and empirical studies2,8-10 suggest that neuronal avalanches indicate an exquisitely balanced critical state dynamics of cortex that optimizes information transfer and information processing.
In order to study the mechanisms of neuronal avalanche development, maintenance, and regulation, in vitro preparations are highly beneficial, as they allow for stable recordings of avalanche activity under precisely controlled conditions. The current protocol describes how to study neuronal avalanches in vitro by taking advantage of superficial layer development in organotypic cortex cultures, i.e. slice cultures, grown on planar, integrated microelectrode arrays (MEA; see also 11-14).

A robust way to study neuronal avalanches, i.e. scale-invariant spatio-temporal activity bursts, indicative of critical state dynamics in cortex. Avalanches emerge spontaneously in developing superficial layers of cultured cortex which allows for long-term measurements of the activity with planar integrated multi-electrode arrays (MEA) under precisely controlled conditions.

The cortex is spontaneously active, even in the absence of any particular input or motor output.  During development, this activity is important for the ...

Simultaneous Electroencephalography, Real-time Measurement of Lactate Concentration and Optogenetic Manipulation of Neuronal Activity in the Rodent Cerebral Cortex

Although the brain represents less than 5% of the body by mass, it utilizes approximately one quarter of the glucose used by the body at rest1. The function of non rapid eye movement sleep (NREMS), the largest portion of sleep by time, is uncertain. However, one salient feature of NREMS is a significant reduction in the rate of cerebral glucose utilization relative to wakefulness2-4. This and other findings have led to the widely held belief that sleep serves a function related to cerebral metabolism. Yet, the mechanisms underlying the reduction in cerebral glucose metabolism during NREMS remain to be elucidated.
	One phenomenon associated with NREMS that might impact cerebral metabolic rate is the occurrence of slow waves, oscillations at frequencies less than 4 Hz, in the electroencephalogram5,6. These slow waves detected at the level of the skull or cerebral cortical surface reflect the oscillations of underlying neurons between a depolarized/up state and a hyperpolarized/down state7. During the down state, cells do not undergo action potentials for intervals of up to several hundred milliseconds. Restoration of ionic concentration gradients subsequent to action potentials represents a significant metabolic load on the cell8; absence of action potentials during down states associated with NREMS may contribute to reduced metabolism relative to wake.
	Two technical challenges had to be addressed in order for this hypothetical relationship to be tested. First, it was necessary to measure cerebral glycolytic metabolism with a temporal resolution reflective of the dynamics of the cerebral EEG (that is, over seconds rather than minutes). To do so, we measured the concentration of lactate, the product of aerobic glycolysis, and therefore a readout of the rate of glucose metabolism in the brains of mice. Lactate was measured using a lactate oxidase based real time sensor embedded in the frontal cortex. The sensing mechanism consists of a platinum-iridium electrode surrounded by a layer of lactate oxidase molecules. Metabolism of lactate by lactate oxidase produces hydrogen peroxide, which produces a current in the platinum-iridium electrode. So a ramping up of cerebral glycolysis provides an increase in the concentration of substrate for lactate oxidase, which then is reflected in increased current at the sensing electrode. It was additionally necessary to measure these variables while manipulating the excitability of the cerebral cortex, in order to isolate this variable from other facets of NREMS.
	We devised an experimental system for simultaneous measurement of neuronal activity via the elecetroencephalogram, measurement of glycolytic flux via a lactate biosensor, and manipulation of cerebral cortical neuronal activity via optogenetic activation of pyramidal neurons. We have utilized this system to document the relationship between sleep-related electroencephalographic waveforms and the moment-to-moment dynamics of lactate concentration in the cerebral cortex. The protocol may be useful for any individual interested in studying, in freely behaving rodents, the relationship between neuronal activity measured at the electroencephalographic level and cellular energetics within the brain.

A procedure is described for manipulating the activity of cerebral cortical pyramidal neurons optogenetically while the electroencephalogram, electromyogram, and cerebral lactate concentration are monitored. Experimental recordings are performed on cable-tethered mice while they undergo spontaneous sleep/wake cycles. Optogenetic equipment is assembled in our laboratory; recording equipment is commercially available.

Although the brain represents less than 5% of the body  by mass, it utilizes approximately one quarter of the glucose used by the body  at rest1. The ...

Real-Time Impedance-based Cell Analyzer as a Tool to Delineate Molecular Pathways Involved in Neurotoxicity and Neuroprotection in a Neuronal Cell Line

Many brain-related disorders have neuronal cell death involved in their pathophysiology. Improved in vitro models to study neuroprotective or neurotoxic effects of drugs and downstream pathways involved would help gain insight into the molecular mechanisms of neuroprotection/neurotoxicity and could potentially facilitate drug development. However, many existing in vitro toxicity assays have major limitations &#8211; most assess neurotoxicity and neuroprotection at a single time point, not allowing to observe the time-course and kinetics of the effect. Furthermore, the opportunity to collect information about downstream signaling pathways involved in neuroprotection in real-time would be of great importance. In the current protocol we describe the use of a real-time impedance-based cell analyzer to determine neuroprotective effects of serotonin 2A (5-HT2A) receptor agonists in a neuronal cell line under label-free and real-time conditions using impedance measurements. Furthermore, we demonstrate that inhibitors of second messenger pathways can be used to delineate downstream molecules involved in the neuroprotective effect. We also describe the utility of this technique to determine whether an effect on cell proliferation contributes to an observed neuroprotective effect. The system utilizes special microelectronic plates referred to as E-Plates which contain alternating gold microelectrode arrays on the bottom surface of the wells, serving as cell sensors. The impedance readout is modified by the number of adherent cells, cell viability, morphology, and adhesion. A dimensionless parameter called Cell Index is derived from the electrical impedance measurements and is used to represent the cell status. Overall, the real-time impedance-based cell analyzer allows for real-time, label-free assessment of neuroprotection and neurotoxicity, and the evaluation of second messenger pathways involvement, contributing to more detailed and high-throughput assessment of potential neuroprotective compounds in vitro, for selecting therapeutic candidates.

Improved in vitro neurotoxicity assays would aid the identification of new neuroprotective compounds. The utility of a real-time impedance-based cell analyzer to determine cytotoxicity and cytoprotection in neuronal cell lines and to delineate the involvement of second messenger pathways, thus gaining insight in the mechanism of neuroprotection is presented.

Many brain-related disorders have neuronal cell death involved in their pathophysiology. Improved in vitro models to study neuroprotective or neurotoxic ...

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation of a given stimulus might be dependent on context and history. If this is actually the case, bi-directional interactions between the brain (or if need be a reduced model of it) and sensory-motor system can shed a light on how encoding and decoding of information is performed. Here an experimental system is introduced and described in which the activity of a neuronal element (i.e., a network of neurons extracted from embryonic mammalian hippocampi) is given context and used to control the movement of an artificial agent, while environmental information is fed back to the culture as a sequence of electrical stimuli. This architecture allows a quick selection of diverse encoding, decoding, and learning algorithms to test different hypotheses on the computational properties of neuronal networks.

In this paper, an experimental framework to perform closed-loop experiments is presented, in which information processing (i.e., coding and decoding) and learning of neuronal assemblies are studied during the continuous interaction with a robotic body.

Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation ...

Prolonged Incubation of Acute Neuronal Tissue for Electrophysiology and Calcium-imaging

Acute neuronal tissue preparations, brain slices and retinal wholemount, can usually only be maintained for 6 - 8 h following dissection. This limits the experimental time, and increases the number of animals that are utilized per study. This limitation specifically impacts protocols such as calcium imaging that require prolonged pre-incubation with bath-applied dyes. Exponential bacterial growth within 3 - 4 h after slicing is tightly correlated with a decrease in tissue health. This study describes a method for&#160;limiting the proliferation of bacteria in acute preparations to maintain viable neuronal tissue for prolonged periods of time (&#62;24 h) without the need for antibiotics, sterile procedures, or tissue culture media containing growth factors. By cycling the extracellular fluid through UV irradiation and keeping the tissue in a custom holding chamber at 15 - 16 &#176;C, the tissue shows no difference in electrophysiological properties, or calcium signaling through intracellular calcium dyes at &#62;24 h postdissection. These methods will not only extend experimental time for those using acute neuronal tissue, but will reduce the number of animals required to complete experimental goals, and will set a gold standard for acute neuronal tissue incubation.

Once removed from the body, neuronal tissue is greatly affected by environmental conditions, leading to eventual degradation of the tissue after 6 - 8 h. Using a unique incubation method, which closely monitors and regulates the extracellular environment of the tissue, tissue viability can be significantly extended for &#62;24 h.

Acute neuronal tissue preparations, brain slices and retinal wholemount, can usually only be maintained for 6 - 8 h following dissection. This limits the ...

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Closed-loop neurophysiological systems use patterns of neuronal activity to trigger stimuli, which in turn affect brain activity. Such closed-loop systems are already found in clinical applications, and are important tools for basic brain research. A particularly interesting recent development is the integration of closed-loop approaches with optogenetics, such that specific patterns of neuronal activity can trigger optical stimulation of selected neuronal groups. However, setting up an electrophysiological system for closed-loop experiments can be difficult. Here, a ready-to-apply Matlab code is provided for triggering stimuli based on the activity of single or multiple neurons. This sample code can be easily modified based on individual needs. For instance, it shows how to trigger sound stimuli and how to change it to trigger an external device connected to a PC serial port. The presented protocol is designed to work with a popular neuronal recording system for animal studies (Neuralynx). The implementation of closed-loop stimulation is demonstrated in an awake rat.

This protocol demonstrates how to use an electrophysiological system for closed-loop stimulation triggered by neuronal activity patterns. Sample Matlab code that can be easily modified for different stimulation devices is also provided.

Closed-loop neurophysiological systems use patterns of neuronal activity to trigger stimuli, which in turn affect brain activity. Such closed-loop systems ...

Open-Source Real-Time Closed-Loop Electrical Threshold Tracking for Translational Pain Research

Nociceptors are a class of primary afferent neurons that signal potentially harmful noxious stimuli. An increase in nociceptor excitability occurs in acute and chronic pain conditions. This produces abnormal ongoing activity or reduced activation thresholds to noxious stimuli. Identifying the cause of this increased excitability is required for the development and validation of mechanism-based treatments. Single-neuron electrical threshold tracking can quantify nociceptor excitability. Therefore, we have developed an application to allow such measurements and demonstrate its use in humans and rodents. APTrack provides real-time data visualization and action potential identification using a temporal raster plot. Algorithms detect action potentials by threshold crossing and monitor their latency after electrical stimulation. The plugin then modulates the electrical stimulation amplitude using an up-down method to estimate the electrical threshold of the nociceptors. The software was built upon the Open Ephys system (V0.54) and coded in C++ using the JUCE framework. It runs on Windows, Linux, and Mac operating systems. The open-source code is available (<a data-saferedirecturl="https://www.google.com/url?q=https://github.com/Microneurography/APTrack&amp;source=gmail&amp;ust=1680789829274000&amp;usg=AOvVaw0nMk9J_EI2jUn8SlmC5JJm" href="https://github.com/Microneurography/APTrack" target="_blank">https://github.com/Microneurography/APTrack</a>). The electrophysiological recordings were taken from nociceptors in both a mouse skin-nerve preparation using the teased fiber method in the saphenous nerve and in healthy human volunteers using microneurography in the superficial peroneal nerve. Nociceptors were classified by their response to thermal and mechanical stimuli, as well as by monitoring the activity-dependent slowing of the conduction velocity. The software facilitated the experiment by simplifying the action potential identification through the temporal raster plot. We demonstrate real-time closed-loop electrical threshold tracking of single-neuron action potentials during&#160;in vivo&#160;human microneurography, for the first time, and during&#160;ex vivo&#160;mouse electrophysiological recordings of C-fibers and A&#948;-fibers. We establish proof of principle by showing that the electrical threshold of a human heat-sensitive C-fiber nociceptor is reduced by heating the receptive field. This plugin enables the electrical threshold tracking of single-neuron action potentials and allows the quantification of changes in nociceptor excitability.

APTrack is a software plugin developed for the Open Ephys platform that enables real-time data visualization and the closed-loop electrical threshold tracking of neuronal action potentials. We have successfully used this in microneurography for human C-fiber nociceptors and mouse C-fiber and A&#948;-fiber nociceptors.

Nociceptors are a class of primary afferent neurons that signal potentially harmful noxious stimuli. An increase in nociceptor excitability occurs in ...

Deep Brain Stimulation for Treatment of Major Depression

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment 

Deep brain stimulation triggered by a patient-specific neural biomarker of a high-symptom state may better control symptoms of major depressive disorder compared to continuous, open-loop stimulation. This protocol provides a workflow for identifying a patient-specific neural biomarker and controlling the delivery of therapeutic stimulation based on the identified biomarker.

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major ...

Programming Device Detector Settings for Closed-Loop Neurostimulation

Begin by logging onto the patient Data Management System, using the programmer. Next, select the correct Patient, Programming, and Change Detection. Based on the selected personalized biomarker, select the correct channel for detecting the activity.For activity detection, select Rhythmic Activity, which connotes a band pass detector. Click More Controls in Advanced Settings. Select the desired minimum and maximum frequency.Ensure to turn on the band pass and turn off the line length in Area Settings. Additionally, set the inversion logic to Not Inverted. Program the detector by setting the minimum amplitude to 0.8%and the minimum duration to 0.64 seconds.After selecting all the detector settings, click the Done button to close all programming windows. Place the wand over the patient's implantable neurostimulator, and select Review All Changes in Program RNS. The biomarker discovery of symptoms was performed based on the neurospectral power between the low depression and the high depression states.It was found that the best choice for a frequency-specific biomarker in this example is 13 to 30 hertz of OFC, three to four.