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75 | 75 |
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76 | 76 | Neuroshare is a standardized API for accessing neurophysiology
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77 | 77 | data stored in vendor-specific binary formats in a vendor-neutral
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78 |
| -way. The G-Node Neuroshare Tools provide libraries and utilities |
79 |
| -built on Neuroshare and Python to work with Neuroshare compatible |
80 |
| -files on various platforms. |
| 78 | +way. |
| 79 | +% The G-Node Neuroshare Tools provide libraries and utilities |
| 80 | +%built on Neuroshare and Python to work with Neuroshare compatible |
| 81 | +%files on various platforms. |
81 | 82 |
|
82 | 83 | \begin{itemize}[nolistsep,topsep=0em,leftmargin=1pc]
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83 | 84 | \item High-level Python library to access Neuroshare compatible data-files
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145 | 146 |
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146 | 147 | \vspace{0.5em}
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147 | 148 |
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148 |
| -G-Node provides a free cloud-based service neuroscientists can use for storage, management and sharing of data (\url{https://portal.g-node.org/data/}). An API for data access is provided (\url{http://g-node.github.com/g-node-portal/}), allowing developers to code their own clients. A web-based interface as well as a client for MATLAB$^\textrm{\textregistered}$ (\url{http://g-node.github.com/gnode-client-matlab/}) are already provided. A client for Python is currently under development. When completed it shall provide: |
| 149 | +G-Node provides a free cloud-based service neuroscientists can use for |
| 150 | +storage, management and sharing of data |
| 151 | +(\url{https://portal.g-node.org/data/}). An API for data access is |
| 152 | +provided (\url{http://g-node.github.com/g-node-portal/}), allowing |
| 153 | +developers to code their own clients. |
| 154 | +% A web-based interface as well as a client for |
| 155 | +% MATLAB$^\textrm{\textregistered}$ |
| 156 | +% (\url{http://g-node.github.com/gnode-client-matlab/}) are already |
| 157 | +% provided. |
| 158 | +A client for Python is currently under development: %. When completed it shall provide: |
149 | 159 |
|
150 | 160 | \begin{itemize}[nolistsep,topsep=0em,leftmargin=1pc]
|
151 |
| -\item Compatibility with NEO: use it 'out-of-the-box' with existing code written for NEO and gain additional services provided by the G-Node 'for free' |
| 161 | +\item Compatibility with NEO%: use it 'out-of-the-box' with existing code written for NEO and gain additional services provided by the G-Node 'for free' |
152 | 162 | \item Smart lazy loading and caching for frugal bandwidth and memory usage
|
153 | 163 | \item Possibility to work in a mixed workflow: work on the same data in Python and MATLAB$^\textrm{\textregistered}$
|
154 | 164 | \end{itemize}
|
155 | 165 |
|
156 |
| -Stay tuned for development updates! |
| 166 | +%Stay tuned for development updates! |
157 | 167 |
|
158 | 168 | %___________________________________________________________________________
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159 | 169 | \ndsection{Metadata Management}%
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|
184 | 194 | \vspace{0.5em} %some additional spacing
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185 | 195 | LFPy is a Python module for simulation of extracellular electrical
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186 | 196 | potentials evoked by activity of multi-compartment model neurons.
|
187 |
| -LFPy runs on top of the NEURON-simulator, using the included Python interface |
188 |
| -(\url{http://www.neuron.yale.edu}). |
| 197 | +%LFPy runs on top of the NEURON-simulator, using the included Python interface |
| 198 | +%(\url{http://www.neuron.yale.edu}). |
189 | 199 |
|
190 | 200 | LFPy provides:
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191 | 201 | \begin{itemize}[nolistsep, topsep=0em, leftmargin=1pc]
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192 | 202 | \item A forward modeling scheme for calculating extracellular
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193 | 203 | potentials from compartmental membrane currents in an infinite
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194 | 204 | homogeneous linear extracellular medium
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195 |
| -\item Python-classes for setting up cells, synapses and recording |
196 |
| - electrodes |
| 205 | +%\item Python-classes for setting up cells, synapses and recording |
| 206 | +% electrodes |
197 | 207 | \item Scripting capabilities thanks to NEURON and the
|
198 | 208 | Python programming environment
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199 | 209 | \item Simultaneous simulation of the model cell responses and
|
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231 | 241 | statistics, multivariate decoding
|
232 | 242 | \end{itemize}
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233 | 243 |
|
234 |
| -\vspace{1em} |
| 244 | +%\vspace{1em} |
235 | 245 | \includegraphics[width=\columnwidth]{mne_screenshot.pdf}
|
236 | 246 | % Selected set of citations, Here is an example:
|
237 | 247 | \ndcite{A. Gramfort, \emph{et.~al.}
|
|
283 | 293 | \item Use supplied plugins for common plots such as Raster Plot, PSTH, Correlogram and analog signals
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284 | 294 | \end{itemize}
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285 | 295 |
|
286 |
| -\vspace{1em} |
| 296 | +%\vspace{1em} |
287 | 297 | \includegraphics[width=\columnwidth]{spykeviewer_screenshot.png}
|
288 | 298 | % Selected set of citations, Here is an example:
|
289 | 299 | % No publication yet...
|
|
299 | 309 | formats and it is memory-efficient. Truely Open Source, BSD-licensed.
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300 | 310 |
|
301 | 311 | \begin{itemize}[nolistsep,topsep=0em,leftmargin=1pc]
|
302 |
| -\item User-friendly and customisable |
| 312 | +%\item User-friendly and customisable |
303 | 313 | \item Interactive command-line interface in Python
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304 | 314 | \item GUI and visualization widgets
|
305 |
| -\item Automatic and manual clustering |
| 315 | +%\item Automatic and manual clustering |
306 | 316 | \item Support for multi-channel data
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307 |
| -\item Based on: NumPy, PyTables, matplotlib, scikit-learn |
| 317 | +%\item Based on: NumPy, PyTables, matplotlib, scikit-learn |
308 | 318 | \end{itemize}
|
309 | 319 | %___________________________________________________________________________
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310 | 320 |
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