Preparation for JOSS submission

@gramm/stat_summary.m

@@ -21,6 +21,7 @@
 %       percentiles
 %       - 'fitnormalci'
 %       - 'fitpoissonci'
+%       - 'fitbinomialci'
 %       - 'fit95percentile'
 %       - @function : provide a the handle to a custom function that takes 
 %       y values (as an n_repetitions x n_data_points matrix) and returns 

examples.m

@@ -1382,5 +1382,50 @@
 g17.draw();
 
 
+%% Raw matlab code equivalent to the first figure (in paper.md)
+
+figure('Position',[100 100 800 400],'Color',[1 1 1]);
+
+% Define groups
+cyl = [4 6 8]; % Manually
+orig = unique(cars.Origin_Region); % Based on data
+
+% Loop over groups
+for oi = 1:length(orig) % External loop on the axes
+
+    % Axes creation
+    ax = subplot(1,length(orig),oi);
+    hold on
+
+    for ci = 1:length(cyl) %Internal loop on the colors
+
+        % Data selection
+        sel = strcmp(cars.Origin_Region,orig{oi}) & ...
+            cars.Cylinders==cyl(ci) & ...
+            ~isnan(cars.Model_Year) & ~isnan(cars.MPG);
+
+        % Plotting of raw data
+        plot(cars.Model_Year(sel),cars.MPG(sel),'.', ...
+            'MarkerSize',15);
+
+        % Keep the same color for the statistics
+        ax.ColorOrderIndex = ax.ColorOrderIndex - 1;
+
+        % Statistics (linear fit and plotting)
+        b = [ones(sum(sel),1) cars.Model_Year(sel)] \ ...
+			cars.MPG(sel);
+        x_fit = [min(cars.Model_Year(sel)) ...
+			max(cars.Model_Year(sel))];
+        plot(x_fit, x_fit * b(2) + b(1),'LineWidth',1.5);
+    end
+
+    % Axes legends
+    title(['Origin: ' orig{oi}]);
+    xlabel('Year');
+    ylabel('Fuel Economy (MPG)');
+end
+% Ugly color legend
+l = legend('4','','6','','8','','Location','southeast');
+title(l,'#Cyl');
 
 

paper/codemeta.json

@@ -0,0 +1,23 @@
+{
+  "@context": "https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld",
+  "@type": "Code",
+  "author": [
+    {
+      "@id": "https://orcid.org/0000-0003-0984-7016",
+      "@type": "Person",
+      "email": "[email protected]",
+      "name": "Pierre Morel",
+      "affiliation": "German Primate Center"
+    }
+  ],
+  "identifier": "https://doi.org/10.5281/zenodo.594625",
+  "codeRepository": "https://github.com/piermorel/gramm",
+  "datePublished": "2018-01-31",
+  "dateModified": "2018-01-31",
+  "dateCreated": "2018-01-31",
+  "description": "Gramm is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.",
+  "keywords": "data visualization, grouped data, visual analytics, plotting, matlab, statistical graphics",
+  "license": "MIT",
+  "title": "gramm",
+  "version": "v2.23"
+}

paper/paper.bib

@@ -0,0 +1,149 @@
+  @Book{Wickham:2009,
+    author = {Hadley Wickham},
+    title = {ggplot2: Elegant Graphics for Data Analysis},
+    publisher = {Springer-Verlag New York},
+    year = {2009},
+    isbn = {978-0-387-98140-6},
+    url = {http://ggplot2.org},
+  }
+  @book{Wilkinson:1999,
+   author = {Leland Wilkinson},
+   title = {The Grammar of Graphics},
+   year = {1999},
+   isbn = {0-387-98774-6},
+   publisher = {Springer-Verlag New York, Inc.},
+   address = {New York, NY, USA},
+  } 
+  @misc{michael_waskom_2017_883859,
+    author       = {Michael Waskom and
+                    Olga Botvinnik and
+                    Drew O'Kane and
+                    Paul Hobson and
+                    Saulius Lukauskas and
+                    David C Gemperline and
+                    Tom Augspurger and
+                    Yaroslav Halchenko and
+                    John B. Cole and
+                    Jordi Warmenhoven and
+                    Julian de Ruiter and
+                    Cameron Pye and
+                    Stephan Hoyer and
+                    Jake Vanderplas and
+                    Santi Villalba and
+                    Gero Kunter and
+                    Eric Quintero and
+                    Pete Bachant and
+                    Marcel Martin and
+                    Kyle Meyer and
+                    Alistair Miles and
+                    Yoav Ram and
+                    Tal Yarkoni and
+                    Mike Lee Williams and
+                    Constantine Evans and
+                    Clark Fitzgerald and
+                    Brian and
+                    Chris Fonnesbeck and
+                    Antony Lee and
+                    Adel Qalieh},
+    title        = {mwaskom/seaborn: v0.8.1 (September 2017)},
+    month        = sep,
+    year         = 2017,
+    doi          = {10.5281/zenodo.883859},
+    url          = {https://doi.org/10.5281/zenodo.883859}
+  }
+  @article{JSSv040i01,
+     author = {Hadley Wickham},
+     title = {The Split-Apply-Combine Strategy for Data Analysis},
+     journal = {Journal of Statistical Software, Articles},
+     volume = {40},
+     number = {1},
+     year = {2011},
+     keywords = {},
+     abstract = {Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored.
+
+  The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements.},
+     issn = {1548-7660},
+     pages = {1--29},
+     doi = {10.18637/jss.v040.i01},
+     url = {https://www.jstatsoft.org/v040/i01}
+  }
+  @article{doi:10.1152/jn.00504.2017,
+  author = {Enrico Ferrea and Lalitta Suriya-Arunroj and Dirk Hoehl and Uwe Thomas and Alexander Gail},
+  title = {Implantable computer-controlled adaptive multi-electrode positioning system (AMEP)},
+  journal = {Journal of Neurophysiology},
+  volume = {Advance online publication},
+  year = {2017},
+  doi = {10.1152/jn.00504.2017},
+      note ={PMID: 29187552},
+
+  URL = { 
+          https://doi.org/10.1152/jn.00504.2017
+    
+  },
+  eprint = { 
+          https://doi.org/10.1152/jn.00504.2017
+    
+  }
+  ,
+      abstract = { Acute neuronal recordings performed with metal microelectrodes in non-human primates allow investigating the neural substrate of complex cognitive behaviors. Yet, the daily re-insertion and positioning of the electrodes prevents recording from many neurons simultaneously, limiting the suitability of these types of recordings for brain-computer-interface applications or for large-scale population statistical methods on a trial-by-trial basis. In contrast, chronically implanted multi-electrode arrays offer the opportunity to record from many neurons simultaneously, but immovable electrodes prevent optimization of the signal during and after implantation and cause the tissue response to progressively impair the transduced signal quality, thereby limiting the number of different neurons that can be recorded over the lifetime of the implant. Semi-chronically implanted matrices of electrodes, instead, allow individually movable electrodes in depth and achieve higher channel count compared to acute methods, hence partially overcome these limitations. Existing semi-chronic systems with higher channel count lack computerized control of electrode movements, leading to limited user-friendliness and uncertainty in depth-positioning. Here we demonstrate a chronically-implantable Adaptive Multi-Electrode Positioning (AMEP) system with detachable drive for computerized depth-adjustment of individual electrodes over several millimeters. This semi-chronic 16-channel system is designed to optimize the simultaneous yield of units in an extended period following implantation since the electrodes can be independently depth-adjusted with minimal effort and their signal quality continuously assessed. Importantly, the electrode array is designed to remain within a chronic recording chamber for a prolonged time, or can be used for acute recordings with high signal-to-noise ratio in the cerebral cortex of non-human primates. }
+  }
+  @article{doi:10.1152/jn.00614.2017,
+  author = {Michael Berger and Antonino Calapai and Valeska Stephan and Michael Niessing and Leonore Burchardt and Alexander Gail and Stefan Treue},
+  title = {Standardized automated training of rhesus monkeys for neuroscience research in their housing environment},
+  journal = {Journal of Neurophysiology},
+  volume = {Advance online publication},
+  year = {2017},
+  doi = {10.1152/jn.00614.2017},
+      note ={PMID: 29142094},
+
+  URL = { 
+          https://doi.org/10.1152/jn.00614.2017
+    
+  },
+  eprint = { 
+          https://doi.org/10.1152/jn.00614.2017
+    
+  }
+  ,
+      abstract = { Teaching non-human primates the complex cognitive behavioral tasks that are central to cognitive neuroscience research is an essential and challenging endeavor. It is crucial for the scientific success that the animals learn to interpret the often complex task rules, and reliably and enduringly act accordingly. To achieve consistent behavior and comparable learning histories across animals, it is desirable to standardize training protocols. Automatizing the training can significantly reduce the time invested by the person training the animal. And self-paced training schedules with individualized learning speeds based on automatic updating of task conditions could enhance the animals' motivation and welfare. We developed a training paradigm for across-task unsupervised training (AUT) of successively more complex cognitive tasks to be administered through a stand-alone housing-based system optimized for rhesus monkeys in neuroscience research settings (Calapai et al. 2016). The AUT revealed inter-individual differences in long-term learning progress between animals, helping to characterize learning personalities, and commonalities, helping to identify easier and more difficult learning steps in the training protocol. Our results demonstrate that (1) rhesus monkeys stay engaged with the AUT over months despite access to water and food outside the experimental sessions, but with lower numbers of interaction compared to conventional fluid-controlled training; (2) with unsupervised training across sessions and task levels, rhesus monkeys can learn tasks of sufficient complexity for state-of-the art cognitive neuroscience in their housing environment; (3) AUT learning progress is primarily determined by the number of interactions with the system rather than the mere exposure time. }
+  }
+  
+@article{1741-2552-13-1-016002,
+  author={Pierre Morel and Enrico Ferrea and Bahareh Taghizadeh-Sarshouri and Josep Marcel Cardona Audí and Roman
+Ruff and Klaus-Peter Hoffmann and Sören Lewis and Michael Russold and Hans Dietl and Lait Abu-Saleh and Dietmar
+Schroeder and Wolfgang Krautschneider and Thomas Meiners and Alexander Gail},
+  title={Long-term decoding of movement force and direction with a wireless myoelectric implant},
+  journal={Journal of Neural Engineering},
+  volume={13},
+  number={1},
+  pages={016002},
+  url={http://stacks.iop.org/1741-2552/13/i=1/a=016002},
+  doi = {10.1088/1741-2560/13/1/016002},
+  year={2016},
+  abstract={Objective. The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. Approach. Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. Main results. In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. Significance. These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).}
+}
+@article{10.1371/journal.pbio.2001323,
+    author = {Pierre Morel and Philipp Ulbrich and Alexander Gail},
+    journal = {PLOS Biology},
+    publisher = {Public Library of Science},
+    title = {What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control},
+    year = {2017},
+    month = {06},
+    volume = {15},
+    url = {https://doi.org/10.1371/journal.pbio.2001323},
+    pages = {1-23},
+    abstract = {Author summary Economic choice in humans and animals can be understood as a weighing of benefits (e.g., reward) against costs (e.g., effort, delay, risk), leading to a preference for the behavioral option with highest expected utility. The costs of the action associated with a choice can thereby affect its utility: for equivalent benefits, an action that requires less physical effort will be preferred to a more effortful one. Here, we characterized how human subjects assess physical effort when choosing between arm movements. We show that the effort cost of a movement increases with its duration and with the square of the force it is performed against but not with the distance covered. Therefore, the subjective cost that determines decisions does not reflect the objective energetic cost of the actions—i.e., the corresponding metabolic expenditure. Instead, the subjective cost has commonalities with the cost that our central nervous system is believed to minimize for controlling the motor execution of actions. Our findings thus argue in favor of action selection and action control sharing common underlying optimization principles.},
+    number = {6},
+    doi = {10.1371/journal.pbio.2001323}
+}
+@article {HBM:HBM23932,
+author = {Wan, Nick and Hancock, Allison S. and Moon, Todd K. and Gillam, Ronald B.},
+title = {A functional near-infrared spectroscopic investigation of speech production during reading},
+journal = {Human Brain Mapping},
+year = {2017},
+issn = {1097-0193},
+url = {http://dx.doi.org/10.1002/hbm.23932},
+doi = {10.1002/hbm.23932},
+volume = {Advance online publication},
+keywords = {fNIRS, functional connectivity, Granger causality, oral reading, silent reading},
+}