<div dir="ltr"><div><div><div>Dear colleagues,<span style="color:rgb(0,0,0)"><br></span></div><span style="color:rgb(0,0,0)"><br>You are invited to attend our shortly upcoming full-day tutorial session at CNS*2014 in Quebec City.<br>
<br></span></div><span style="color:rgb(0,0,0)"><b>Where:</b> </span>Organization for Computational Neurosciences (OCNS) Conference, Quebec City Conference Center, Room 2105<br></div><b>When:</b> Saturday July 26, 2014 between 9am - 4:30pm<br>
<div><div><span style="color:rgb(0,0,0)"></span><div><div><span style="color:rgb(0,0,0)"><br></span><strong><span style="color:rgb(0,0,0)"><br>T9: Neuronal Model Parameter Search Techniques</span></strong>
<p><br><span style="color:rgb(0,0,0)">Parameter tuning of model neurons to mimic biologically realistic activity is a non‐trivial task. </span><span style="color:rgb(0,0,0)">Multiple models may exhibit similar dynamics that match experimental data – i.e., there is no single </span><span style="color:rgb(0,0,0)">“correct” model. To address this issue, the ensemble modeling technique proposes to represent </span><span style="color:rgb(0,0,0)">properties of living neurons with a set of neuronal models. Several approaches to ensemble </span><span style="color:rgb(0,0,0)">modeling have been proposed over the years, but the two most prevalent parameter tuning </span><span style="color:rgb(0,0,0)">methods are systematic “brute‐force” searches [1, 2] and various evolutionary algorithms‐based </span><span style="color:rgb(0,0,0)">techniques [3, 4, 5, 6]. Both approaches relay on traversing a very large parameter space (with </span><span style="color:rgb(0,0,0)">thousands to millions of model instances), but utilize diametrically different ways to accomplish that. </span><span style="color:rgb(0,0,0)">In both cases, however, entire collections of biologically realistic models are generated, whose </span><span style="color:rgb(0,0,0)">neural activity characteristics can then be cataloged and studied using a database [1, 2]. </span><br>
<br><span style="color:rgb(0,0,0)">The tutorial covers “tips and tricks,” as well as various pitfalls in all stages of model construction, </span><span style="color:rgb(0,0,0)">large‐scale simulations on high performance computing clusters [S2], database construction and </span><span style="color:rgb(0,0,0)">analysis of neural data, along with a discussion about the strengths and weaknesses of the two </span><span style="color:rgb(0,0,0)">parameter search techniques. We will review software implementations for each technique: </span><span style="color:rgb(0,0,0)">PANDORA Matlab Toolbox [7][S1] for the brute force method and NeRvolver (i.e., evolver of nerve </span><span style="color:rgb(0,0,0)">cells) for evolutionary algorithms. PANDORA was used in recent projects for tuning models of rat </span><span style="color:rgb(0,0,0)">globus pallidus neurons [2][M1], lobster pyloric network calcium sensors [8][M2], leech heart </span><span style="color:rgb(0,0,0)">interneurons [9][M3,S3] and hippocampal O‐LM interneurons (Skinner Lab, TWRI/UHN and Univ. </span><span style="color:rgb(0,0,0)">Toronto). NeRvolver is a prototype of a computational intelligence‐based system for automated </span><span style="color:rgb(0,0,0)">construction, tuning, and analysis of neuronal models, which is currently under development in the </span><span style="color:rgb(0,0,0)">Computational Intelligence and Bio (logical) informatics Laboratory at Delaware State University [10]. </span><span style="color:rgb(0,0,0)">Through the utilization of computational intelligence methods (i.e., Multi‐Objective Evolutionary </span><span style="color:rgb(0,0,0)">Algorithms and Fuzzy Logic), the NeRvolver system generates classification rules describing biological </span><span style="color:rgb(0,0,0)">phenomena discovered during the process of model creation or tuning. Thus in addition to </span><span style="color:rgb(0,0,0)">producing neuronal models, NeRvolver provides–via such rules–insights into the functioning of the </span><span style="color:rgb(0,0,0)">biological neurons being modeled. In the tutorial, we will present basic functionalities of the system </span><span style="color:rgb(0,0,0)">and demonstrate how to analyze the results returned by the software. </span><br>
<br><span style="color:rgb(0,0,0)">We will allocate enough time for Q&A and if participants bring a laptop pre‐loaded with Matlab, they </span><span style="color:rgb(0,0,0)">can follow some of our examples. </span></p>
<p><b>Lecturers/Organizers:</b></p><p><span style="color:rgb(0,0,0)">Cengiz Günay, </span><span style="color:rgb(0,0,0)">Anca Doloc-Mihu (Emory University, USA) </span></p><p><span style="color:rgb(0,0,0)"></span><span style="color:rgb(0,0,0)">Vladislav Sekulić (University of Toronto, Canada), <br>
</span></p><p><span style="color:rgb(0,0,0)"></span><span style="color:rgb(0,0,0)">Tomasz G. Smolinski (Delaware State University, USA) </span><br></p><p><br><br><strong><span style="color:rgb(0,0,0)">References </span></strong><br>
<span style="color:rgb(0,0,0)">[1] Astrid A. Prinz, Cyrus P. Billimoria, and Eve Marder. Alternative to hand‐tuning conductance‐</span><span style="color:rgb(0,0,0)">based models: Construction and analysis of databases of model neurons. J Neurophysiol, 90:3998–</span><span style="color:rgb(0,0,0)">4015, 2003. </span></p>
<p><span style="color:rgb(0,0,0)">[2] Cengiz Günay, Jeremy R. Edgerton, and Dieter Jaeger. Channel density distributions explain </span><span style="color:rgb(0,0,0)">spiking variability in the globus pallidus: A combined physiology and computer simulation database </span><span style="color:rgb(0,0,0)">approach. J. Neurosci., 28(30):7476–91, July 2008.</span></p>
<p><span style="color:rgb(0,0,0)">[3] Pablo Achard and Erik De Schutter. Complex parameter landscape for a complex neuron model. </span><span style="color:rgb(0,0,0)">PLoS Comput Biol, 2(7):794–804, Jul 2006. </span></p>
<p><span style="color:rgb(0,0,0)">[4] Tomasz G. Smolinski and Astrid A. Prinz. Computational intelligence in modeling of biological </span><span style="color:rgb(0,0,0)">neurons: A case study of an invertebrate pacemaker neuron. In Proceedings of the International </span><span style="color:rgb(0,0,0)">Joint Conference on Neural Networks, pages 2964–2970, Atlanta, GA, 2009. </span></p>
<p><span style="color:rgb(0,0,0)">[5] Tomasz G. Smolinski and Astrid A. Prinz. Multi‐objective evolutionary algorithms for model </span><span style="color:rgb(0,0,0)">neuron parameter value selection matching biological behavior under different simulation scenarios. </span><span style="color:rgb(0,0,0)">BMC Neuroscience, 10(Suppl 1):P260, 2009. </span></p>
<p><span style="color:rgb(0,0,0)">[6] Damon G. Lamb and Ronald L. Calabrese. Correlated conductance parameters in leech heart </span><span style="color:rgb(0,0,0)">motor neurons contribute to motor pattern formation. PLoS One, 8(11):e79267, 2013. </span></p>
<p><span style="color:rgb(0,0,0)">[7] Cengiz Günay, Jeremy R. Edgerton, Su Li, Thomas Sangrey, Astrid A. Prinz, and Dieter Jaeger. </span><span style="color:rgb(0,0,0)">Database analysis of simulated and recorded electrophysiological datasets with PANDORA’s Toolbox. </span><span style="color:rgb(0,0,0)">Neuroinformatics, 7(2):93–111, 2009. </span></p>
<p><span style="color:rgb(0,0,0)">[8] Cengiz Günay and Astrid A. Prinz. Model calcium sensors for network homeostasis: Sensor and </span><span style="color:rgb(0,0,0)">readout parameter analysis from a database of model neuronal networks. J Neurosci, 30:1686–1698, </span><br>
<span style="color:rgb(0,0,0)">Feb 2010. NIHMS176368,PMC2851246. </span></p>
<p><span style="color:rgb(0,0,0)">[9] Anca Doloc‐Mihu and Ronald L. Calabrese. A database of computational models of a half‐center </span><span style="color:rgb(0,0,0)">oscillator for analyzing how neuronal parameters influence network activity. J Biol Phys, 37(3):263–</span><span style="color:rgb(0,0,0)">283, Jun 2011. </span></p>
<p><span style="color:rgb(0,0,0)">[10] Emlyne Forren, Myles Johnson‐Gray, Parth Patel, and Tomasz G. Smolinski. Nervolver: a </span><span style="color:rgb(0,0,0)">computational intelligence‐based system for automated construction, tuning, and analysis of </span><span style="color:rgb(0,0,0)">neuronal models. BMC Neuroscience, 13(Suppl 1):P36, 2012. <br>
</span></p><div>-Cengiz<br><br>--<br>Cengiz Gunay<br>Postdoctoral Fellow, Dept. of Biology<br>Visiting Faculty, Dept. of Math & CS<br>Emory University<br><a href="mailto:cgunay@emory.edu">cgunay@emory.edu</a><br><a href="http://www.biology.emory.edu/research/Prinz/Cengiz/">http://www.biology.emory.edu/research/Prinz/Cengiz/</a><br>
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