Jekyll2021-07-11T20:31:53-04:00https://cengique.github.io/cns2020-tutorials-website/feed.xmlCNS Tutorials and ShowcaseWeb materials for CNS tutorials by Dr Cengiz Gunay and Dr Anca Doloc-Mihu.
{"type"=>nil, "name"=>nil, "url"=>nil, "avatar"=>nil, "bio"=>nil, "email"=>nil, "facebook"=>nil, "twitter"=>nil, "weibo"=>nil, "googleplus"=>nil, "telegram"=>nil, "medium"=>nil, "zhihu"=>nil, "douban"=>nil, "linkedin"=>nil, "github"=>nil, "npm"=>nil}Satellite tutorial SA4: Methods from Data Science for Model Simulation, Analysis, and Visualization2021-06-30T00:00:00-04:002021-06-30T00:00:00-04:00https://cengique.github.io/cns2020-tutorials-website/2021/06/30/cns2021-tutorial-data-science-methods<h3 id="organizers">Organizers:</h3>
<p>Dr. Cengiz Gunay and Dr. Anca Doloc-Mihu <br />
School of Science and Technology, Georgia Gwinnett College, USA</p>
<h3 id="tutorial-time">Tutorial time:</h3>
<table>
<thead>
<tr>
<th>Time zone:</th>
<th>Los Angeles</th>
<th>New York</th>
<th>Berlin</th>
<th>Sydney</th>
</tr>
</thead>
<tbody>
<tr>
<td>June 30, 2021</td>
<td>5am - 8:30am</td>
<td>8am - 11:30am</td>
<td>14:00 - 17:30</td>
<td>22:00 - 00:30 (July 1)</td>
</tr>
</tbody>
</table>
<!--more-->
<h2 id="description-of-the-tutorial">Description of the tutorial</h2>
<p>Computational neuroscience projects often involve a large number of
simulations for parameter search of computer models, which generates a
large amount of data. With the advances in computer hardware, software
methods, and cloud computing opportunities making this task easier,
the amount of collected data has exploded, similar to what has been
happening in many fields. High-performance computing (HPC) methods
have been used in the computational neuroscience field for a
while. However, the use of novel data science and big data methods is
less frequent. In this tutorial, we will review tools already
established in the big data field and demonstrate their usefulness in
computational neuroscience workflows, focusing
on <a href="https://spark.apache.org/">Apache Spark</a>. Spark is a distributed
computing framework used either for model simulation or for
post-processing and analysis of the generated data. The tutorial will
also have a session focusing on creating interactive
visualizations. We will review novel web-based interactive notebook
technologies based on Javascript
(<a href="https://observablehq.com/">Observable</a>) and Python
(<a href="https://jupyter.org/">Jupyter</a>).</p>
<h3 id="software-tools">Software tools</h3>
<ul>
<li><a href="https://spark.apache.org/">Apache Spark</a></li>
<li><a href="https://observablehq.com/">Observable</a></li>
<li><a href="https://jupyter.org/">Jupyter</a></li>
</ul>
<h3 id="expected-knowledgematerials">Expected knowledge/materials</h3>
<ul>
<li>Some familiarity with Python, Javascript, HTML</li>
<li>Command-line usage for accessing remote servers</li>
<li>For the visualization session: Google Account suggested for being
able to use the online Jupyter notebook service
at <a href="https://colab.research.google.com/">Google Colab</a></li>
</ul>
<h3 id="draft-schedule">Draft schedule</h3>
<table>
<thead>
<tr>
<th>NY Time</th>
<th>Speaker</th>
<th>Schedule item</th>
</tr>
</thead>
<tbody>
<tr>
<td>8:00 am</td>
<td>Cengiz Gunay</td>
<td>From High Performance Computing to Hadoop and Spark (<a href="https://cengique.github.io/course-adv-data-analytics/cns-spark-tutorial-2021.html">slides</a>)</td>
</tr>
<tr>
<td>9:00 am</td>
<td> </td>
<td>Practice & discussion</td>
</tr>
<tr>
<td>9:30 am</td>
<td> </td>
<td>Break</td>
</tr>
<tr>
<td>9:45 am</td>
<td>Anca Doloc-Mihu</td>
<td>High-dimensional data visualizations</td>
</tr>
<tr>
<td>11:00 am</td>
<td> </td>
<td>Practice & discussion</td>
</tr>
<tr>
<td>11:30 am</td>
<td> </td>
<td>End of tutorial</td>
</tr>
</tbody>
</table>
<p><a href="https://www.cnsorg.org/cns-2021-tutorials" target="_blank">See all CNS*2021 Tutorials here</a></p>{"type"=>nil, "name"=>nil, "url"=>nil, "avatar"=>nil, "bio"=>nil, "email"=>nil, "facebook"=>nil, "twitter"=>nil, "weibo"=>nil, "googleplus"=>nil, "telegram"=>nil, "medium"=>nil, "zhihu"=>nil, "douban"=>nil, "linkedin"=>nil, "github"=>nil, "npm"=>nil}Organizers: Dr. Cengiz Gunay and Dr. Anca Doloc-Mihu School of Science and Technology, Georgia Gwinnett College, USA Tutorial time: Time zone: Los Angeles New York Berlin Sydney June 30, 2021 5am - 8:30am 8am - 11:30am 14:00 - 17:30 22:00 - 00:30 (July 1)Satellite tutorial SA3: Signal processing and data analysis in Matlab2021-06-30T00:00:00-04:002021-06-30T00:00:00-04:00https://cengique.github.io/cns2020-tutorials-website/2021/06/30/cns2021-tutorial-pandora<h3 id="organizers">Organizers:</h3>
<p>Dr. Cengiz Gunay<br />
School of Science and Technology, Georgia Gwinnett College, USA</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/Q7Gc-Dq48Yw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
<h3 id="tutorial-time">Tutorial time:</h3>
<table>
<thead>
<tr>
<th>Time zone:</th>
<th>Los Angeles</th>
<th>New York</th>
<th>Berlin</th>
<th>Sydney</th>
</tr>
</thead>
<tbody>
<tr>
<td>June 30, 2021</td>
<td>noon - 3 pm</td>
<td>3 - 6 pm</td>
<td>21:00 - 24:00</td>
<td>05:00 am - 08:00 am (July 1)</td>
</tr>
</tbody>
</table>
<h3 id="draft-schedule">Draft schedule</h3>
<table>
<thead>
<tr>
<th>NY Time</th>
<th>Sessions with 5 minute break between</th>
</tr>
</thead>
<tbody>
<tr>
<td>3:00 pm</td>
<td>Introduction & installation</td>
</tr>
<tr>
<td>3:45 pm</td>
<td>Practice: Signal processing</td>
</tr>
<tr>
<td>4:30 pm</td>
<td>Practice: Tabular analysis</td>
</tr>
<tr>
<td>5:15 pm</td>
<td>Practice: Plotting</td>
</tr>
<tr>
<td>6:00 pm</td>
<td>End of tutorial</td>
</tr>
</tbody>
</table>
<h3 id="download-materials">Download materials</h3>
<ul>
<li><a href="/cns2020-tutorials-website/assets/present-pandora-tutorial-cns2021.pdf">Slides</a></li>
</ul>
<!--more-->
<h2 id="description">Description</h2>
<p>Matlab (Mathworks, Natick, MA) is a popular computing environment that
offers an alternative to more advanced environments with its
simplicity, especially for those less computationally inclined or for
collaborating with experimentalists. In this tutorial, we will focus
on the following tasks in Matlab:</p>
<ol>
<li>Signal processing of recorded or simulated traces (e.g., filtering
noise, spike and burst finding in single-unit intracellular
electrophysiology data in current-clamp, and extracting numerical
characteristics);</li>
<li>analyzing tabular data (e.g. obtained from Excel or from the result
of other analyses);</li>
<li>plotting and visualization.</li>
</ol>
<p>For all of these, we will take advantage of the PANDORA toolbox, which
is an open-source project that has been proposed for analysis and
visualization ( RRID: SCR_001831, [1]). PANDORA was initially
developed for managing and analyzing brute-force neuronal parameter
search databases. However, it has proven useful for various other
types of simulation or experimental data analysis [2-7]. PANDORA’s
original motivation was to offer an object-oriented program for
analyzing neuronal data inside the Matlab environment, in particular
with a database table-like object, similar to the “dataframe” object
offered in the R ecosystem and the pandas Python module. PANDORA
offers a similarly convenient syntax for a powerful database querying
system. A typical workflow would constitute of generating parameter
sets for simulations, and then analyze the resulting simulation output
and other recorded data, to find spikes and to measure additional
characteristics to construct databases, and finally analyze and
visualize these database contents. PANDORA provides objects for
loading datasets, controlling simulations, importing/exporting data,
and visualization. In this tutorial, we use the toolbox’s standard
features and show how to customize them for a given project.</p>
<h3 id="software-tools">Software tools:</h3>
<ul>
<li>Matlab</li>
<li>PANDORA - <a href="https://github.com/cengique/pandora-matlab">Github</a> and <a href="https://www.mathworks.com/matlabcentral/fileexchange/60237-cengique-pandora-matlab">MathWorks File Exchange</a> pages</li>
</ul>
<h3 id="references">References:</h3>
<ul>
<li>Günay et al. 2009 Neuroinformatics, 7(2):93-111. doi: 10.1007/s12021-009-9048-z</li>
<li>Doloc-Mihu et al. 2011 Journal of biological physics, 37(3), 263–283. doi:10.1007/s10867-011-9215-y</li>
<li>Lin et al. 2012 J Neurosci 32(21): 7267–77</li>
<li>Wolfram et al. 2014 J Neurosci, 34(7): 2538–2543; doi: 10.1523/JNEUROSCI.4511-13.2014</li>
<li>Günay et al. 2015 PLoS Comp Bio. doi: 10.1371/journal.pcbi.1004189</li>
<li>Wenning et al. 2018 eLife 2018;7:e31123 doi: 10.7554/eLife.31123</li>
<li>Günay et al. 2019 eNeuro, 6(4), ENEURO.0417-18.2019. doi:10.1523/ENEURO.0417-18.2019</li>
</ul>
<p><a href="https://www.cnsorg.org/cns-2021-tutorials" target="_blank">See all CNS*2021 Tutorials here</a></p>
<p>TBA</p>{"type"=>nil, "name"=>nil, "url"=>nil, "avatar"=>nil, "bio"=>nil, "email"=>nil, "facebook"=>nil, "twitter"=>nil, "weibo"=>nil, "googleplus"=>nil, "telegram"=>nil, "medium"=>nil, "zhihu"=>nil, "douban"=>nil, "linkedin"=>nil, "github"=>nil, "npm"=>nil}Organizers: Dr. Cengiz Gunay School of Science and Technology, Georgia Gwinnett College, USA Tutorial time: Time zone: Los Angeles New York Berlin Sydney June 30, 2021 noon - 3 pm 3 - 6 pm 21:00 - 24:00 05:00 am - 08:00 am (July 1) Draft schedule NY Time Sessions with 5 minute break between 3:00 pm Introduction & installation 3:45 pm Practice: Signal processing 4:30 pm Practice: Tabular analysis 5:15 pm Practice: Plotting 6:00 pm End of tutorial Download materials SlidesShowcase #3: Advances in the PANDORA Matlab Toolbox for intracellular electrophysiology data2020-07-18T00:00:00-04:002020-07-18T00:00:00-04:00https://cengique.github.io/cns2020-tutorials-website/2020/07/18/cns2020-showcase<p><strong>Presenter:</strong> Dr. Cengiz Gunay <br />
School of Science and Technology, Georgia Gwinnett College, USA</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/ctN7wPl_eAE?start=3889" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
<p><strong>Showcase time:</strong></p>
<table>
<thead>
<tr>
<th>Time zone:</th>
<th>Los Angeles</th>
<th>New York</th>
<th>Berlin</th>
<th>Sydney</th>
</tr>
</thead>
<tbody>
<tr>
<td>July 18, 2020</td>
<td>3pm</td>
<td>6pm</td>
<td>midnight</td>
<td>8am (July 19)</td>
</tr>
</tbody>
</table>
<!--more-->
<p><a href="https://www.cnsorg.org/cns-2020-tutorials">All CNS*2020 Tutorials</a></p>
<h3 id="description-of-the-software-showcase">Description of the software showcase</h3>
<p><a href="https://github.com/cengique/pandora-matlab">PANDORA</a> is
an
<a href="https://www.mathworks.com/matlabcentral/fileexchange/60237-cengique-pandora-matlab">open-source toolbox for Matlab</a> (Mathworks,
Natick, MA), which has been originally developed for analysis and
visualization of single-unit intracellular electrophysiology data
(<a href="https://scicrunch.org/resources/about/registry/SCR_001831">RRID: SCR_001831</a>,
Günay et al. 2009 Neuroinformatics,
7(2):93-111. <a href="https://link.springer.com/article/10.1007/s12021-009-9048-z">doi: 10.1007/s12021-009-9048-z</a>). Even
though there are more modern and popular environments, such as the
Python and Anaconda ecosystem, Matlab still offers an advantage in its
simplicity, especially towards those less computationally inclined,
for instance for collaboration with experimentalists. PANDORA was
originally intended for managing and analyzing brute-force neuronal
parameter search databases (Günay et al. 2008 J Neurosci. 28(30):
7476-7491; Günay et al. 2010 J Neurosci. 30: 1686–98). However, it has
been proven useful for other types of simulation or experimental data
analysis
(<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3101324/">Doloc-Mihu et al. 2011 Journal of biological physics, 37(3), 263–283. doi:10.1007/s10867-011-9215-y</a>;
Lin et al. 2012 J Neurosci 32(21): 7267–77; <a href="http://www.jneurosci.org/content/34/7/2538.short">Wolfram et al. 2014 J Neurosci, 34(7): 2538–2543; doi: 10.1523/JNEUROSCI.4511-13.2014</a>;
<a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004189">Günay et al. 2015 PLoS Comp Bio. doi: 10.1371/journal.pcbi.1004189</a>;
<a href="https://elifesciences.org/articles/31123">Wenning et al. 2018 eLife 2018;7:e31123 doi: 10.7554/eLife.31123</a>;
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6709225/">Günay et al. 2019 eNeuro, 6(4), ENEURO.0417-18.2019. doi:10.1523/ENEURO.0417-18.2019</a>). PANDORA’s
original motivation was to offer object-oriented analysis specific to
neuronal data inside the Matlab environment, in particular with a
database table-like object, similar to R and the Python PANDAS
toolbox’s “dataframe” object, and a new syntax for a powerful database
querying system. The typical workflow would constitute of generating
parameter sets for simulations, and then in the resulting output data,
finding spikes and additional characteristics to construct databases,
and finally analyze and visualize these database contents. PANDORA
provides objects for loading datasets, controlling simulations,
importing/exporting data, and visualization. Since it’s inception, it
has grown with added functionality. In this showcase, we review the
toolbox’s standard features and show how to customize them for a given
project, and then introduce some of the new and experimental features,
such as ion channel fitting, evolutionary/genetic
algorithms. Furthermore, we will give a developers’ perspective for
those who may be interested in adding modules to this toolbox.</p>
<h3 id="software-tools">Software tools</h3>
<ul>
<li>PANDORA - <a href="https://github.com/cengique/pandora-matlab">Github</a> and <a href="https://www.mathworks.com/matlabcentral/fileexchange/60237-cengique-pandora-matlab">MathWorks File Exchange</a> pages</li>
</ul>
<h3 id="background-readings">Background readings</h3>
<ul>
<li><a href="https://link.springer.com/article/10.1007/s12021-009-9048-z">Günay et al. 2009 Neuroinformatics,
7(2):93-111. doi: 10.1007/s12021-009-9048-z</a></li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3101324/">Doloc-Mihu et al. 2011 Journal of Biological Physics, 37(3), 263–283. doi:10.1007/s10867-011-9215-y</a></li>
<li>Lin et al. 2012 J Neurosci 32(21): 7267–77</li>
<li><a href="http://www.jneurosci.org/content/34/7/2538.short">Wolfram et al. 2014 J Neurosci, 34(7): 2538–2543; doi: 10.1523/JNEUROSCI.4511-13.2014</a></li>
<li><a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004189">Günay et al. 2015 PLoS Comp Bio. doi: 10.1371/journal.pcbi.1004189</a></li>
<li><a href="https://elifesciences.org/articles/31123">Wenning et al. 2018 eLife 2018;7:e31123 doi: 10.7554/eLife.31123</a></li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6709225/">Günay et al. 2019 eNeuro, 6(4), ENEURO.0417-18.2019. doi:10.1523/ENEURO.0417-18.2019</a>)</li>
</ul>
<!--more-->
<h3 id="download-materials">Download materials</h3>
<ul>
<li><a href="/cns2020-tutorials-website/assets/present-pandora-showcase-cns2020.pdf">Slides</a></li>
</ul>
<h3 id="showcase-organization">Showcase organization</h3>
<ol>
<li>Pandora introduction and updates</li>
<li>Tutorials and demo examples</li>
<li>Q & A feedback</li>
</ol>{"type"=>nil, "name"=>nil, "url"=>nil, "avatar"=>nil, "bio"=>nil, "email"=>nil, "facebook"=>nil, "twitter"=>nil, "weibo"=>nil, "googleplus"=>nil, "telegram"=>nil, "medium"=>nil, "zhihu"=>nil, "douban"=>nil, "linkedin"=>nil, "github"=>nil, "npm"=>nil}Presenter: Dr. Cengiz Gunay School of Science and Technology, Georgia Gwinnett College, USA Showcase time: Time zone: Los Angeles New York Berlin Sydney July 18, 2020 3pm 6pm midnight 8am (July 19)Tutorial #6: Methods from Data Science for Model Simulation, Analysis, and Visualization2020-07-18T00:00:00-04:002020-07-18T00:00:00-04:00https://cengique.github.io/cns2020-tutorials-website/2020/07/18/cns2020-tutorial<p><strong>Organizers:</strong> Dr. Cengiz Gunay and Dr. Anca Doloc-Mihu <br />
School of Science and Technology, Georgia Gwinnett College, USA</p>
<p><strong>Tutorial time:</strong></p>
<table>
<thead>
<tr>
<th>Time zone:</th>
<th>Los Angeles</th>
<th>New York</th>
<th>Berlin</th>
<th>Sydney</th>
</tr>
</thead>
<tbody>
<tr>
<td>July 18, 2020</td>
<td>10am - 1pm</td>
<td>1 - 4pm</td>
<td>7 - 10pm</td>
<td>3 - 6am (July 19)</td>
</tr>
</tbody>
</table>
<!--more-->
<p><a href="https://www.cnsorg.org/cns-2020-tutorials">All CNS*2020 Tutorials</a></p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/-VeiNaAO5kg" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
<h3 id="description-of-the-tutorial">Description of the tutorial</h3>
<p>Computational neuroscience projects often involve large number of
simulations for parameter search of computer models, which generates
large amount of data. With the advances in computer hardware, software
methods, and cloud computing opportunities making this task easier,
the amount of collected data has exploded, similar to what has been
happening in many fields. High performance computing (HPC) methods
have been used in the computational neuroscience field for a
while. However, use of novel data science and big data methods are
less frequent. In this tutorial, we will review established HPC
methods and introduce novel data science tools to be used in
computational neuroscience workflows, starting from the industry
standard of <a href="https://hadoop.apache.org/">Apache Hadoop</a> to newer
tools, such as <a href="https://spark.apache.org/">Apache Spark</a>. These tools
can be used for either model simulation or post-processing and
analysis of the generated data. To visualize the data, we will review
novel web-based interactive dashboard technologies mostly based on
Javascript and Python.</p>
<h3 id="software-tools">Software tools</h3>
<ul>
<li><a href="https://hadoop.apache.org/">Apache Hadoop</a></li>
<li><a href="https://spark.apache.org/">Apache Spark</a></li>
</ul>
<h3 id="expected-knowledgematerials">Expected knowledge/materials</h3>
<ul>
<li>Some familiarity with Python, Javascript, HTML</li>
<li>For the visualization session: Google Account suggested to use the online Jupyter notebook service at <a href="colab.research.google.com/">Colab</a></li>
</ul>
<!--more-->
<h3 id="draft-schedule">Draft schedule</h3>
<table>
<thead>
<tr>
<th>Time from start</th>
<th>Speaker</th>
<th>Schedule item</th>
</tr>
</thead>
<tbody>
<tr>
<td>00:00</td>
<td>Cengiz Gunay</td>
<td>From High Performance Computing to Hadoop and Spark (<a href="https://cengique.github.io/course-adv-data-analytics/cns-spark-tutorial.html">slides</a>)</td>
</tr>
<tr>
<td>00:50</td>
<td> </td>
<td>Break</td>
</tr>
<tr>
<td>01:00</td>
<td>Anca Doloc-Mihu</td>
<td>High-dimensional data visualizations (<a href="/cns2020-tutorials-website/assets/TutorialDataViz-cns2020.zip">slides and materials</a>)</td>
</tr>
<tr>
<td>01:50</td>
<td> </td>
<td>Break</td>
</tr>
<tr>
<td>02:00</td>
<td>Hieu Dinh, Joshua Walton, Anthony Morariu</td>
<td>Analysim.tech: A data sharing site for crowdsourcing analysis of parameter-search datasets (<a href="https://ggcedu-my.sharepoint.com/:p:/g/personal/hdinh3_ggc_edu/ETRC3EF0VCtPoqVxkJaV680BK1c2Ahfcfeg1V79ZBXR3Mg?e=NZCFX8">slides</a> and <a href="https://www.analysim.tech/">demo site</a>)</td>
</tr>
<tr>
<td>02:50</td>
<td> </td>
<td>Break before next session</td>
</tr>
</tbody>
</table>{"type"=>nil, "name"=>nil, "url"=>nil, "avatar"=>nil, "bio"=>nil, "email"=>nil, "facebook"=>nil, "twitter"=>nil, "weibo"=>nil, "googleplus"=>nil, "telegram"=>nil, "medium"=>nil, "zhihu"=>nil, "douban"=>nil, "linkedin"=>nil, "github"=>nil, "npm"=>nil}Organizers: Dr. Cengiz Gunay and Dr. Anca Doloc-Mihu School of Science and Technology, Georgia Gwinnett College, USA Tutorial time: Time zone: Los Angeles New York Berlin Sydney July 18, 2020 10am - 1pm 1 - 4pm 7 - 10pm 3 - 6am (July 19)Welcome2020-06-26T00:00:00-04:002020-06-26T00:00:00-04:00https://cengique.github.io/cns2020-tutorials-website/2020/06/26/welcome-to-jekyll<p>Welcome to our CNS*2020 tutorial and showcase materials website! Click
on <a href="/cns2020-tutorials-website/tutorial.html">Tutorial</a>
or <a href="/cns2020-tutorials-website/showcase.html">Showcase</a> above to
select. Enjoy! :ghost: :ghost: :ghost:</p>{"type"=>nil, "name"=>nil, "url"=>nil, "avatar"=>nil, "bio"=>nil, "email"=>nil, "facebook"=>nil, "twitter"=>nil, "weibo"=>nil, "googleplus"=>nil, "telegram"=>nil, "medium"=>nil, "zhihu"=>nil, "douban"=>nil, "linkedin"=>nil, "github"=>nil, "npm"=>nil}Welcome to our CNS*2020 tutorial and showcase materials website! Click on Tutorial or Showcase above to select. Enjoy! :ghost: :ghost: :ghost: