AK wishes to thank Gerry Loeb for useful discussions about the proposed Turing-like handshake test. AK and IN wish to thank Nathaniel Leibowitz and Lior Botzer who contributed to the design of the very first version of this protocol back in 2007. This research was supported by the Israel Science Foundation (grant No. 1018/08). SL is supported by a Kreitman Foundation postdoctoral fellowship. IN is supported by the Kreitman foundation and the Clore Scholarship Program.

1. Preparing the System
<ol>
<li>Hardware requirements:
<ul>
<li>Two PHANTOM desktop robots by SensAble Technologies, Inc.</li>
<li>2 Parallel cards.</li>
<li>Minimum system requirements: Intel or AMD-based PCs; Windows 2000/XP, 250 MB of disc space.</li>
</ul>
</li>
<li>Software requirements:
<ul>
<li>Driver- 
Download drivers from the SensAble technologies website <a href="http://www.sensable.com">http://www.sensable.com</a> according to the computer operating system.</li>
<li>H3DAPI- 
Download the H3DAPI source code and install according to the instructions displayed in the installation walkthrough section in <a href="http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation">http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation</a> 
We updated the code to fit our requirements that were not met in the existing code. The updated files can be downloaded from the handshake tournament website <a href="http://www.bgu.ac.il/~akarniel/HANDSHAKE//index.html">http://www.bgu.ac.il/~akarniel/HANDSHAKE//index.html</a> 
Each file should be placed in the appropriate folder as described in the website above. 
After performing the changes, download CMake and compile the code. The compiling instructions can be found in <a href="http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation">http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation</a></li>
<li>X3D and python 
<ul>
<li>Download python 2.5 from <a href="http://www.python.org/download/releases/2.5.5/">http://www.python.org/download/releases/2.5.5/</a></li>
<li>Download the python and x3d codes from the handshake website and place the code files in a dedicated directory, for instance: "C:\codeDirectory"</li>
<li>Each handshake force model should be written in a separate python file. See Figure 1 for an example of a spring force model.</li>
</ul>
</li>
<li>psignifit toolbox version 2.5.6 for Matlab, available at <a href="http://www.bootstrap-software.org/psignifit/">http://www.bootstrap-software.org/psignifit/</a></li>
</ul>
</li>
</ol>
<img src="/files/ftp_upload/2492/2492fig1.jpg" alt="Figure 1" /> 
Figure 1. Force function in python. An example of a spring force model for a handshake

2. Experimental Protocol
<ul>
<li>Open the command window and change to the python and x3d code directory by typing: cd C:\codeDirectory.</li>
<li>Create a folder with the subjects' names in C:\codesDirectory.</li>
<li>In order to run the experiment type: h3dload code_name.x3d.</li>
<li>Create a new random file by typing: random_file_name.txt. The random file defines the order in which the different handshakes appear.</li>
<li>Enter the subjects' names exactly as in the previously created folder.</li>
<li>Following the original concept of the classical Turing test, each experiment consists of 3 entities: a human, a computer, and an interrogator. Two subjects (human and interrogator) each hold the stylus of one Phantom haptic device and generate handshake movements. They are asked to follow the instructions that appear on the screen (e.g., :"press Page Up for the first handshake"), for handshake forces to be applied. In all of the following methods, each trial consists of 2 handshakes, and the subjects are required to compare between them. The computer is a simulated handshake model which generates a force signal as a function of time and the one dimensional hand position and its derivatives. 
(1)Fmodel(t) = &Phi;[x(t),t] 0 &le; t &le; T 
F[x,t] stands for any causal operator, e.g., non-linear time-varying mechanical model of the one dimensional stylus movement, and T is the duration of the handshake. In the current study T=5 seconds.</li>
</ul>
<ol>
<li>Conducting the "pure" test and calculating the model human likeness grade MHLGp 

The experiment starts with 12 practice trials in which all the handshakes (n=24) are human, such that the subjects simply shake hands with each other through the telerobotic system. The purpose of these practice trials is to enable the participants to be acquainted with a human handshake in the system. 
In the experiment, we compare four computer models. Each experimental block consists of 4 trials in which we compare the 4 tested models to a human handshake. One of the handshakes in each trial is an interaction with a force generated from one of the four models (a computer), and the other is with a human (the second subject). Therefore, the subjects function as both humans and interrogators. The order of the trials within each block is random and predetermined. Each experiment consists of 10 blocks, such that each computer handshake is repeated 10 times. An initial unanalyzed block is added for general acquaintance with the system and the task. 
For each model, the proportion of handshakes in which the subject chooses the model handshake over the human handshake as more human-like is calculated, to provide a value which is 0.5 when the model is indistinguishable from a human. We multiply this value by two in order to obtain the MHLGp, such that MHLG=0 is clearly non-human like and MHLG=1 means that the tested handshake is indistinguishable from the human handshake.</li>
<li>Conducting the "weighted human-model" test and calculating the model human likeness grade MHLGw 

In this protocol, there is only one interrogator subject. The other subject functions as the human entity in the handshakes. 
The experiment starts with 30 practice trials in which the interrogator experiences one human handshake and one computer handshake in each trial. In the end of the trial they are asked to choose which of the 2 handshakes was the human handshake. If they succeed, the screen displays "Correct!", and if they didn't choose the right handshake, a "Wrong!" message appears. 
After the practice block the experiment is conducted as follows: A trial consists of two handshakes. In one of the handshakes- the stimulus - the interrogator interacts with a combination of forces that comes from the human and a computer handshake model. 
(2) F = &alpha;stimulus&#8226; Fhuman + (1-&alpha;stimulus)&#8226;FstimulusModel 
&alpha;stimulus is equally distributed from 0 to 1, e.g: 
&alpha;stimulus= {0, 0.142, 0.284, 0.426, 0.568, 0.710, 0.852, 1} 
The other handshake - the reference - is a fixed combination of forces generated from the human and a reference model: 
(3) F = &alpha;reference&#8226; Fhuman + (1-&alpha;reference)&#8226;FreferenceModel ; &alpha;reference=0.5 

 At the end of each trial the interrogator is requested to choose the handshake that felt more human-like. 
In each experiment we compare two test models and one base model. 
Each experimental block consists of 24 trials comprising each of the linear combinations of the stimulus and the human (eq. 2) for each of the 3 model combinations: 
<img src="/files/ftp_upload/2492/2492model1.jpg" alt="Model 1" /> 
The order of the trials within each block is random and predetermined. Each experiment consists of 10 blocks, such that each combination is repeated 10 times. An initial unanalyzed block is added for general acquaintance with the system and the task. 
We fit a logistic psychometric function1 to the answers of the interrogator using the psignifit toolbox version 2.5.6 for Matlab, available at <a href="http://www.bootstrap-software.org/psignifit/">http://www.bootstrap-software.org/psignifit/</a> , with a constrained maximum likelihood method for estimation of the parameters, and find confidence intervals by the bias-corrected and accelerated (BCa) bootstrap method. The curve displays the probability of the interrogator to answer that a stimulus handshake is more human-like, as a function of &alpha;stimulus - &alpha;reference. The point of subjective equality (PSE) is extracted from the 0.5 threshold level of the psychometric curve, indicating the difference between the &alpha;stimulus and &alpha;reference for which the handshakes are perceived to be equally human like. The PSE is used for calculating the MHLGw according to: 
(4) MHLGw =0.5-PSE 
A model which is perceived to be as human-like as the reference model yields the MHLGw value 0.5. The models that are perceived as the least or the most human-like possible, yield MHLGw values of 0 or 1, respectively.</li>

<li>Conducting the "added noise" test and calculating the model human likeness grade MHLGn 

Similarly to the weighted model-human test, there is only one interrogator subject. The other subject functions as the human entity in the handshakes. The practice block is also the same as in the previous method. 
After the practice, in one of the 2 handshakes - the stimulus - the interrogator interacts with a computer handshake model. 
The other handshake - the reference - is a force generated from a combination of the human and white noise with a frequency range filtered according to the frequencies that appear in the human handshake. 
(5) F = &alpha; &#8226; Fhuman + (1-&alpha;)&#8226;Fnoise; 
&alpha; is equally distributed from 0 to 1, e.g: 
&alpha; = {0, 0.142, 0.284, 0.426, 0.568, 0.710, 0.852, 1} 

 At the end of each trial the interrogator is requested to choose the handshake that felt more human-like. 
The experiment is built in the same way the weighted model-human test mentioned above is built. However, while the calibration in the weighted human-model test is performed by comparing different combinations of a base model to a fix combination of itself, in this experiment the base model is substitute by the noise. 
The PSE is extracted from the psychometric curve and defines the MHLGn 

(6) MHLGn = 1-PSE 
The models that are perceived as the least or the most human-like possible, yield MHLGn values of 0 or 1, respectively.</li>
</ol>
3. Representative Results: 
Figure 2 demonstrates the results of one subject for each of the 3 methods. The tested models in all three experiments are 2 viscoelastic models- KB1: spring K=50 N/m, damper B=2 Ns/m; KB2: spring K=20 N/m, damper B=1.3 Ns/m. In the weighted model-human test, the MHLGw is evaluated by comparing each of the tested models to the elastic base model K=50 N/m.
<img src="/files/ftp_upload/2492/2492fig2.jpg" alt="Figure 2" /> 
Figure 2. The MHLG values of two viscoelastic models according to the "pure" test protocol (a), the "weighted model-human protocol" (b) and the "added noise" protocol (c). The error bars in (b) and (c) represent the psychometric curves' confidence intervals. The black bars represent the MHLG grades for the models, and the gray bars represent those of the base model in (b) and the noise in (c).
The results demonstrate that the viscoelastic model KB2 is perceived as more human like than the other viscoelastic model KB1 using all three evaluation methods.

<ol>
	<li>Wichmann, F., Hill, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+psychometric+function:+I.+Fitting,+sampling,+and+goodness+of+fit.">The psychometric function: I. Fitting, sampling, and goodness of fit.</a> Perception &#38; Psychophysics. 63, 1293-1293 (2001).</li><li>Avraham, G., Levy-Tzedek, S., Karniel, 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> , (2009).</li><li>Avraham, G., Levy-Tzedek, S., Karniel, 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> , (2010).</li><li>Avraham, G., Levy-Tzedek, S., Peles, B. C., Bar-Haim, S., Karniel, 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> , (2010).</li><li>Karniel, A., Nisky, I., Avraham, G., Peles, B. C., Levy-Tzedek, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> , (2010).</li><li>Jindai, M., Watanabe, T., Shibata, S., Yamamoto, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> , (2006).</li><li>Kasuga, T., Hashimoto, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> , (2005).</li><li>Ouchi, K., Hashimoto, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Handshake+Telephone+System+to+Communicate+with+Voice+and+Force.">Handshake Telephone System to Communicate with Voice and Force.</a> , 466-471 (1998).</li><li>Bailenson, J., Yee, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virtual+interpersonal+touch+and+digital+chameleons.">Virtual interpersonal touch and digital chameleons.</a> Journal of Nonverbal Behavior. 31, 225-242 (2007).</li><li>Miyashita, T., Ishiguro, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human-like+natural+behavior+generation+based+on+involuntary+motions+for+humanoid+robots.">Human-like natural behavior generation based on involuntary motions for humanoid robots.</a> Robotics and Autonomous Systems. 48, 203-212 (2004).</li><li>Wang, Z., Lu, J., Peer, A., Buss, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> , (2010).</li><li>IiZuka, H., Ando, H., Maeda, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> , (2010).</li><li>Chaplin, W., Phillips, J., Brown, J., Clanton, N., Stein, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Handshaking,+gender,+personality,+and+first+impressions.">Handshaking, gender, personality, and first impressions.</a> Journal of Personality and Social Psychology. 79, 110-117 (2000).</li><li>Stewart, G., Dustin, S., Barrick, M., Darnold, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+the+handshake+in+employment+interviews.">Exploring the handshake in employment interviews.</a> Journal of Applied Psychology. 93, 1139-1146 (2008).</li><li>Van Der Heide, J., Fock, J., Otten, B., Stremmelaar, E., Hadders-Algra, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinematic+characteristics+of+reaching+movements+in+preterm+children+with+cerebral+palsy.">Kinematic characteristics of reaching movements in preterm children with cerebral palsy.</a> Pediatric research. 57, 883 (2005).</li><li>Ronnqvist, L., Rosblad, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinematic+analysis+of+unimanual+reaching+and+grasping+movements+in+children+with+hemiplegic+cerebral+palsy.">Kinematic analysis of unimanual reaching and grasping movements in children with hemiplegic cerebral palsy.</a> Clinical Biomechanics-Kidlington. 22, 165-175 (2007).</li><li>Goodale, M., Milner, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Separate+visual+pathways+for+perception+and+action.">Separate visual pathways for perception and action.</a> Trends in neurosciences. 15, 20-25 (1992).</li><li>Ganel, T., Goodale, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+control+of+action+but+not+perception+requires+analytical+processing+of+object+shape.">Visual control of action but not perception requires analytical processing of object shape.</a> Nature. 426, 664-667 (2003).</li><li>Pressman, A., Nisky, I., Karniel, A., Mussa-Ivaldi, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Probing+Virtual+Boundaries+and+the+Perception+of+Delayed+Stiffness.">Probing Virtual Boundaries and the Perception of Delayed Stiffness.</a> Advanced Robotics. 22, 119-140 (2008).</li><li>Nisky, I., Pressman, A., Pugh, C. 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No conflicts of interest declared.

We have presented a new protocol for a forced-choice Turing-like handshake test administered via a simple telerobotic system. This protocol is a platform for comparing artificial handshake models, rather than a platform for determining absolute human likeness. This protocol was presented in a few conferences2-5
We have shown here that this test is helpful in finding the parameters of the passive characteristics of motion that provide the most human-like feeling. It can be used in further studies in order to develop a model for a handshake which will be as human-like as possible. We will employ this platform in the first Turing-like handshake tournament which will take place in summer 2011 [[<a href="http://www.bgu.ac.il/~akarniel/HANDSHAKE/index.html">http://www.bgu.ac.il/~akarniel/HANDSHAKE/index.html</a>]], where competing models will be graded for their human likeness. The ultimate model should probably consider the nonlinearities and time-varying nature of human impedance21, mutual adaptation with the interrogator and many other aspects of a natural human handshake which should be tested and ranked using this forced-choice Turing-like handshake test.
The proposed test is one dimensional and performed via a telerobotic interface, and therefore is limited: it hides many aspects of the handshake such as tactile information, temperature, moisture, and grasping forces. Nevertheless, in several studies a telerobotic interface was used for exploring handshakes6-11and other forms of human-human interaction12. In addition, in this version of the test, we didn't consider the duration of the handshake, the initiation and release times, its multi-dimensional nature and the hand trajectories before and after the physical contact. There are also many types of handshakes depending on gender and culture of the person13-14 and therefore, one cannot expect to generate a single optimal human-like handshake model. Nevertheless, we believe that the simplicity of the proposed test is an advantage, at least at this preliminary stage of the study. Once the key features of such one dimensional handshake are properly characterized we can move on to consider these limitations and extend the test accordingly.
It should be noted that a Turing-like handshake test could be reversed, with the computer instead of the person being asked about the identity of the other party. In this framework, we consider the following reverse handshake hypothesis: the purpose of a handshake is to probe the shaken hand; according to the reverse handshake hypothesis, the optimal handshake algorithm - in the sense that it will be indistinguishable from a human handshake - will best facilitate the discrimination between people and machines. In other words, the model will yield the best handshake such that an appropriately tuned classifier can distinguish between human and machine handshakes.
If the reverse handshake hypothesis is indeed correct it yields a clinical application for our test: identifying motor impairments in people suffering from various neurological motor-related disorders, such as Cerebral Palsy (CP). Previous studies have shown differences in the kinematic parameters between CP patients and healthy subjects when performing reaching movements15-16. We recently showed that the characteristics of movements differ between healthy individuals and individuals with CP when shaking hands through a telerobotic system4. These findings strengthen our claim that people with motor impairments can be distinguished from healthy people by examining and exploring the handshake movement of each individual. One should also note that the test discussed herein is a perceptual test and recent studies distinguish between perception and action17-20. Future studies should explore three versions of the test in order to accurately assess the nature of the human-like handshake: (1) a psychometric test of the perceived similarity; (2) a motor behavior test (motormetric test) that will explore the motor reaction of the interrogator which may differ from his/her cognitively perceived similarity; (3) an ultimate optimal discriminator which attempts to distinguish between human and machine handshakes based on the force and position trajectories. 
In general terms, we assert that understanding the motor control system is a necessary condition for understanding the brain function, and that such understanding could be demonstrated by building a humanoid robot indistinguishable from a human. The current study focuses on handshakes via a telerobotic system. We assert that by ranking the prevailing scientific hypotheses about the nature of human hand movement control using the proposed Turing-like handshake test, we should be able to extract salient properties of human motor control or at least the salient properties required to build an artificial appendage that is indistinguishable from a human arm.

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<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">	
		
		<div>
		<table border="1">
		<thead>
		<tr>
		<th>Material Name</th>
		<th>Type</th>
		<th>Company</th>
		<th>Catalogue Number</th>
		<th>Comment</th>
		</tr>
		</thead>
		<tbody>
		
<tr>
<td>Two PHANTOM desktop robots</td>
<td>&nbsp;</td>
<td>SensAble Technologies</td>
<td>&nbsp;</td>
<td>2 Parallel cards Minimum system requirements: Intel or AMD-based PCs; Windows 2000/XP, 250 MB of disc space</td>
</tr>
<tr>
<td>SensAble technologies Drivers</td>
<td>&nbsp;</td>
<td>SensAble technologies</td>
<td>&nbsp;</td>
<td><a href="http://www.sensable.com">http://www.sensable.com</a></td>
</tr>
<tr>
<td>H3DAPI source code</td>
<td>&nbsp;</td>
<td>H3DAPI</td>
<td>&nbsp;</td>
<td><a href="http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation">http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation</a></td>
</tr>
<tr>
<td>Python 2.5</td>
<td>&nbsp;</td>
<td>Python</td>
<td>&nbsp;</td>
<td><a href="http://www.python.org/download/releases/2.5.5/">http://www.python.org/download/releases/2.5.5/</a></td>
</tr>
<tr>
<td>x3d codes</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>psignifit toolbox version 2.5.6</td>
<td>&nbsp;</td>
<td>Matlab</td>
<td>&nbsp;</td>
<td><a href="http://www.bootstrap-software.org/psignifit/">http://www.bootstrap-software.org/psignifit/</a></td>
</tr>		</tbody>
		</table>
		</div>

one dimensional turing-like handshake test for motor intelligence

In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, and the movement of the human hand is a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human or artificial). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a two-alternative forced choice method and ask which of two systems is more human-like. We extract a quantitative grade for each model according to its resemblance to the human handshake motion and name it "Model Human-Likeness Grade" (MHLG). We present three methods to estimate the MHLG. (i) By calculating the proportion of subjects' answers that the model is more human-like than the human; (ii) By comparing two weighted sums of human and model handshakes we fit a psychometric curve and extract the point of subjective equality (PSE); (iii) By comparing a given model with a weighted sum of human and random signal, we fit a psychometric curve to the answers of the interrogator and extract the PSE for the weight of the human in the weighted sum. Altogether, we provide a protocol to test computational models of the human handshake. We believe that building a model is a necessary step in understanding any phenomenon and, in this case, in understanding the neural mechanisms responsible for the generation of the human handshake.

Watch this Scientific Journal Video about One Dimensional Turing-Like Handshake Test for Motor Intelligence at JoVE.com

One Dimensional Turing-Like Handshake Test for Motor Intelligence

We present a Turing-like Handshake test administered through a telerobotic system in which the interrogator is holding a robotic stylus and interacting with another party (human or artificial). We use a forced choice method, and extract a measure for the similarity of the artificial model to a human handshake.

In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human.  ...

one-dimensional-turing-like-handshake-test-for-motor-intelligence

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26.6K Views.  Ben-Gurion University. We present a Turing-like Handshake test administered through a telerobotic system in which the interrogator is holding a robotic stylus and interacting with another party (human or artificial). We use a forced choice method, and extract a measure for the similarity of the artificial model to a human handshake.

Video: One Dimensional Turing-Like Handshake Test for Motor Intelligence

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.
This extracellular technique has advantages. First, extracellular electrodes can stimulate and record neurons through the sheath5, so it does not need to be removed. Thus, neurons will be healthier in extracellular experiments than in intracellular ones. Second, if ganglia are rotated by appropriate pinning of the sheath, extracellular electrodes can access neurons on both sides of the ganglion, which makes it easier and more efficient to identify multiple neurons in the same preparation. Third, extracellular electrodes do not need to penetrate cells, and thus can be easily moved back and forth among neurons, causing less damage to them. This is especially useful when one tries to record multiple neurons during repeating motor patterns that may only persist for minutes. Fourth, extracellular electrodes are more flexible than intracellular ones during muscle movements. Intracellular electrodes may pull out and damage neurons during muscle contractions. In contrast, since extracellular electrodes are gently pressed onto the sheath above neurons, they usually stay above the same neuron during muscle contractions, and thus can be used in more intact preparations.
To uniquely identify motor neurons for a motor pool (in particular, the I1/I3 muscle in Aplysia) using extracellular electrodes, one can use features that do not require intracellular measurements as criteria: soma size and location, axonal projection, and muscle innervation4,6,7. For the particular motor pool used to illustrate the technique, we recorded from buccal nerves 2 and 3 to measure axonal projections, and measured the contraction forces of the I1/I3 muscle to determine the pattern of muscle innervation for the individual motor neurons.
We demonstrate the complete process of first identifying motor neurons using muscle innervation, then characterizing their timing during motor patterns, creating a simplified diagnostic method for rapid identification. The simplified and more rapid diagnostic method is superior for more intact preparations, e.g. in the suspended buccal mass preparation8 or in vivo9. This process can also be applied in other motor pools10,11,12 in Aplysia or in other animal systems2,3,13,14.

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.

In animals with large  identified neurons (e.g. mollusks), analysis of motor pools is done using  intracellular techniques1,2,3,4. Recently,  we developed ...

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction

Most studies on morphogenesis rely on qualitative descriptions of how anatomical traits are affected by the disruption of specific genes and genetic pathways. Quantitative descriptions are rarely performed, although genetic manipulations produce a range of phenotypic effects and variations are observed even among individuals within control groups.&#160;Emerging evidence shows that morphology, size and location of organelles play a previously underappreciated, yet fundamental role in cell function and survival. Here we provide step-by-step instructions for performing quantitative analyses of phenotypes at the Drosophila larval neuromuscular junction (NMJ). We use several reliable immuno-histochemical markers combined with bio-imaging techniques and morphometric analyses to examine the effects of genetic mutations on specific cellular processes. In particular, we focus on the quantitative analysis of phenotypes affecting morphology, size and position of nuclei within the striated muscles of Drosophila larvae. The Drosophila larval NMJ is a valuable experimental model to investigate the molecular mechanisms underlying the structure and the function of the neuromuscular system, both in health and disease. However, the methodologies we describe here can be extended to other systems as well.

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction

Morphology, size and location of intracellular organelles are evolutionarily conserved and appear to directly affect their function. Understanding the molecular mechanisms underlying these processes has become an important goal of modern biology. Here we show how these studies can be facilitated by the application of quantitative techniques.

Most studies on morphogenesis rely on qualitative descriptions of how anatomical traits are affected by the disruption of specific genes and genetic ...

External Excitation of Neurons Using Electric and Magnetic Fields in One- and Two-dimensional Cultures

A neuron will fire an action potential when its membrane potential exceeds a certain threshold. In typical activity of the brain, this occurs as a result of chemical inputs to its synapses. However, neurons can also be excited by an imposed electric field. In particular, recent clinical applications activate neurons by creating an electric field externally. It is therefore of interest to investigate how the neuron responds to the external field and what causes the action potential. Fortunately, precise and controlled application of an external electric field is possible for embryonic neuronal cells that are excised, dissociated and grown in cultures. This allows the investigation of these questions in a highly reproducible system.
In this paper some of the techniques used for controlled application of external electric field on neuronal cultures are reviewed. The networks can be either one dimensional, i.e. patterned in linear forms or allowed to grow on the whole plane of the substrate, and thus two dimensional. Furthermore, the excitation can be created by the direct application of electric field via electrodes immersed in the fluid (bath electrodes) or by inducing the electric field using the remote creation of magnetic pulses.

Neuronal cultures are a good model for studying emerging brain stimulation techniques via their effect on single neurons or a population of neurons. Presented here are different methods for stimulation of patterned neuronal cultures by an electric field produced directly by bath electrodes or induced by a time-varying magnetic field.

A neuron will fire an action potential when its membrane potential exceeds a certain threshold. In typical activity of the brain, this occurs as a result ...

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

The purpose of this study is to show the methodology for the examination of the white matter connections of the supplementary motor area (SMA) complex (pre-SMA and SMA proper) using a combination of fiber dissection techniques on cadaveric specimens and magnetic resonance (MR) tractography. The protocol will also describe the procedure for a white matter dissection of a human brain, diffusion tensor tractography imaging, and three-dimensional documentation. The fiber dissections on human brains and the 3D documentation were performed at the University of Minnesota, Microsurgery and Neuroanatomy Laboratory, Department of Neurosurgery. Five postmortem human brain specimens and two whole heads were prepared in accordance with Klingler's method. Brain hemispheres were dissected step by step from lateral to medial and medial to lateral under an operating microscope, and 3D images were captured at every stage. All dissection results were supported by diffusion tensor imaging. Investigations on the connections in line with Meynert's fiber tract classification, including association fibers (short, superior longitudinal fasciculus I and frontal aslant tracts), projection fibers (corticospinal, claustrocortical, cingulum, and frontostriatal tracts), and commissural fibers (callosal fibers) were also conducted.

The purpose of this study is to show each step of the fiber dissection technique on human cadaveric brains, the 3D documentation of these dissections, and the diffusion tensor imaging of the anatomically dissected fiber pathways.

The purpose of this study is to show the methodology for the examination of the white matter connections of the supplementary motor area (SMA) complex ...

In Vivo Single-Molecule Tracking at the Drosophila Presynaptic Motor Nerve Terminal

An increasing number of super-resolution microscopy techniques are helping to uncover the mechanisms that govern the nanoscale cellular world. Single-molecule imaging is gaining momentum as it provides exceptional access to the visualization of individual molecules in living cells. Here, we describe a technique that we developed to perform single-particle tracking photo-activated localization microscopy (sptPALM) in Drosophila larvae. Synaptic communication relies on key presynaptic proteins that act by docking, priming, and promoting the fusion of neurotransmitter-containing vesicles with the plasma membrane. A range of protein-protein and protein-lipid interactions tightly regulates these processes and the presynaptic proteins therefore exhibit changes in mobility associated with each of these key events. Investigating how mobility of these proteins correlates with their physiological function in an intact live animal is essential to understanding their precise mechanism of action. Extracting protein mobility with high resolution in vivo requires overcoming limitations such as optical transparency, accessibility, and penetration depth. We describe how photoconvertible fluorescent proteins tagged to the presynaptic protein Syntaxin-1A can be visualized via slight oblique illumination and tracked at the motor nerve terminal or along the motor neuron axon of the third instar Drosophila larva.

In Vivo Single-Molecule Tracking at the Drosophila Presynaptic Motor Nerve Terminal

Here we illustrate how single molecule photo-activated localization microscopy can be carried out on the motor nerve terminal of a live Drosophila melanogaster third instar larva.

An increasing number of super-resolution microscopy techniques are helping to uncover the mechanisms that govern the nanoscale cellular world. ...

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases.

We introduce a kinematic analysis method that uses a three-dimensional motion capture apparatus containing four cameras and data processing software for performing functional evaluations during fundamental research involving rodent models.

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of ...

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

High interindividual variability in the recovery of upper limb (UL) function after stroke means it is difficult to predict an individual&#39;s potential for recovery based on clinical assessments alone. The functional integrity of the corticospinal tract is an important prognostic biomarker for recovery of UL function, particularly for those with severe initial UL impairment. This article presents a protocol for evaluating corticospinal tract function within 1 week of stroke. This protocol can be used to select and stratify patients in trials of interventions designed to improve UL motor recovery and outcomes after stroke. The protocol also forms part of the PREP2 algorithm, which predicts UL function for individual patients 3 months poststroke. The algorithm sequentially combines a UL strength assessment, age, transcranial magnetic stimulation, and stroke severity, within a few days of the stroke. The benefits of using PREP2 in clinical practice are described elsewhere. This article focuses on the use of a UL strength assessment and transcranial magnetic stimulation to evaluate corticospinal tract function.

This protocol is for evaluating corticospinal tract function within 1 week of stroke. It can be used to select and stratify patients in trials of interventions designed to improve upper limb motor recovery and outcomes and in clinical practice for predicting upper limb functional outcomes 3 months after stroke.

High interindividual variability in the recovery of upper limb (UL) function after stroke means it is difficult to predict an individual&#39;s potential ...

Three-Dimensional Motor Nerve Organoid Generation

A fascicle of axons is one of the major structural motifs observed in the nervous system. Disruption of axon fascicles could cause developmental and neurodegenerative diseases. Although numerous studies of axons have been conducted, our understanding of formation and dysfunction of axon fascicles is still limited due to the lack of robust three-dimensional in vitro models. Here, we describe a step-by-step protocol for the rapid generation of a motor nerve organoid (MNO) from human induced pluripotent stem (iPS) cells in a microfluidic-based tissue culture chip. First, fabrication of chips used for the method is described. From human iPS cells, a motor neuron spheroid (MNS) is formed. Next, the differentiated MNS is transferred into the chip. Thereafter, axons spontaneously grow out of the spheroid and assemble into a fascicle within a microchannel equipped in the chip, which generates an MNO tissue carrying a bundle of axons extended from the spheroid. For the downstream analysis, MNOs can be taken out of the chip to be fixed for morphological analyses or dissected for biochemical analyses, as well as calcium imaging and multi-electrode array recordings. MNOs generated with this protocol can facilitate drug testing and screening and can contribute to understanding of mechanisms underlying development and diseases of axon fascicles.

This protocol provides a comprehensive procedure to fabricate human iPS cell-derived motor nerve organoid through spontaneous assembly of a robust bundle of axons extended from a spheroid in a tissue culture chip.

A fascicle of axons is one of the major structural motifs observed in the nervous system. Disruption of axon fascicles could cause developmental and ...

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

Fine motor skills are essential in everyday life and can be compromised in several nervous system disorders. The acquisition and performance of these tasks require sensory-motor integration and involve precise control of bilateral brain circuits. Implementing unimanual behavioral paradigms in animal models will improve the understanding of the contribution of brain structures, like the striatum, to complex motor behavior as it allows manipulation and recording of neural activity of specific nuclei in control conditions and disease during the performance of the task. 
Since its creation, optogenetics has been a dominant tool for interrogating the brain by enabling selective and targeted activation or inhibition of neuronal populations. The combination of optogenetics with behavioral assays sheds light on the underlying mechanisms of specific brain functions. Wireless head-mounted systems with miniaturized light-emitting diodes (LEDs) allow remote optogenetic control in an entirely free-moving animal. This avoids the limitations of a wired system being less restrictive for animals' behavior without compromising light emission efficiency. The current protocol combines a wireless optogenetics approach with high-speed videography in a unimanual dexterity task to dissect the contribution of specific neuronal populations to fine motor behavior.

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

The present protocol describes how to use wireless optogenetics combined with high-speed videography in a single pellet reach-to-grasp task to characterize the neural circuits involved in the performance of skilled motor behavior in freely moving mice.

Fine motor skills are essential in everyday life and can be compromised in several nervous system disorders. The acquisition and performance of these ...

Expanding the Toolkit for In Vivo Imaging of Axonal Transport

Axonal transport maintains neuronal homeostasis by enabling the bidirectional trafficking of diverse organelles and cargoes. Disruptions in axonal transport have devastating consequences for individual neurons and their networks, and contribute to a plethora of neurological disorders. As many of these conditions involve both cell autonomous and non-autonomous mechanisms, and often display a spectrum of pathology across neuronal subtypes, methods to accurately identify and analyze neuronal subsets are imperative.
This paper details protocols to assess in vivo axonal transport of signaling endosomes and mitochondria in sciatic nerves of anesthetized mice. Stepwise instructions are provided to 1) distinguish motor from sensory neurons in vivo, in situ, and ex vivo by using mice that selectively express fluorescent proteins within cholinergic motor neurons; and 2) separately or concurrently assess in vivo axonal transport of signaling endosomes and mitochondria. These complementary intravital approaches facilitate the simultaneous imaging of different cargoes in distinct peripheral nerve axons to quantitatively monitor axonal transport in health and disease.

Expanding the Toolkit for In Vivo Imaging of Axonal Transport

Using transgenic fluorescent mice, detailed protocols are described to assess in vivo axonal transport of signaling endosomes and mitochondria within motor and sensory axons of the intact sciatic nerve in live animals.