add TrainSpikingNet

.github/actions/validTagsList.json

@@ -57,6 +57,7 @@
     "Expansion microscopy (ExM)",
     "Image registration",
     "Lightsheet fluorescence microscopy (LFSM)",
+    "Modelling",
     "Neural recording",
     "Segmentation",
     "Sequence analysis",
@@ -76,6 +77,7 @@
   "software ecosystem": [
     "BigDataViewer",
     "Blender",
+    "CUDA",
     "Fiji",
     "ImgLib2",
     "Janelia Workstation",

src/content/projects/_project-template.md

@@ -39,86 +39,91 @@ youtube params: "Optional. Supports any YouTube params: https://developers.googl
 #Optional "tag" fields. Select tags from the provided options - delete the options that are not applicable. If you feel another option is required to describe your project, add it and then note this in your pull request.
 development team:
   [
-    Branson Lab,
-    Dudman Lab,
-    Pachitariu Lab,
-    Preibisch Lab,
-    Saalfeld Lab,
-    Stringer Lab,
-    Turaga Lab,
-    CellMap,
-    FlyEM,
-    FlyLight,
-    MouseLight,
-    MultiFISH,
-    Scientific Computing Software,
+    "Ahrens Lab",
+    "Branson Lab",
+    "Dudman Lab",
+    "Pachitariu Lab",
+    "Preibisch Lab",
+    "Saalfeld Lab",
+    "Stringer Lab",
+    "Turaga Lab",
+    "CellMap",
+    "FlyEM",
+    "FlyLight",
+    "MouseLight",
+    "MultiFISH",
+    "Scientific Computing Software",
   ]
 programming language:
   [
-    C++,
-    Go,
-    Java,
-    Julia,
-    Kotlin,
-    MATLAB,
-    Nextflow,
-    Python,
-    Javascript,
-    Typescript,
+    "C++",
+    "Go",
+    "Java",
+    "Julia",
+    "Kotlin",
+    "MATLAB",
+    "Nextflow",
+    "Python",
+    "Javascript",
+    "Typescript",
   ]
 open source license:
-  [BSD-3 Clause, BSD-2 Clause, CC-by-0, GPL-2.0, GPL-3.0, MIT]
+  ["BSD-3 Clause", "BSD-2 Clause", "CC-by-0", "GPL-2.0", "GPL-3.0", "MIT"]
 software type:
   [
-    Command line application,
-    Fiji plugin,
-    Framework,
-    GUI application,
-    Native application,
-    Package,
-    Service,
-    Web application,
-    Website,
+    "Command line application",
+    "Fiji plugin",
+    "Framework",
+    "GUI application",
+    "Native application",
+    "Package",
+    "Service",
+    "Web application",
+    "Website",
   ]
 use case:
   [
-    Annotation,
-    Calcium imaging,
-    Confocal light microscopy (LM),
-    Containerization,
-    Correlative light EM (CLEM),
-    Electron microscopy (EM),
-    Electrophysiology,
-    Expansion microscopy (ExM),
-    Image registration,
-    Lightsheet fluorescence microscopy (LFSM),
-    Neural recording,
-    Segmentation,
-    Sequence analysis,
-    Single-molecule localization microscopy (SMLM),
-    Spatial transcriptomics,
-    Tool packaging/distribution,
+    "Annotation",
+    "Calcium imaging",
+    "Confocal light microscopy (LM)",
+    "Containerization",
+    "Correlative light EM (CLEM)",
+    "Electron microscopy (EM)",
+    "Electrophysiology",
+    "Expansion microscopy (ExM)",
+    "Image registration",
+    "Lightsheet fluorescence microscopy (LFSM)",
+    "Modelling",
+    "Neural recording",
+    "Segmentation",
+    "Sequence analysis",
+    "Single-molecule localization microscopy (SMLM)",
+    "Spatial transcriptomics",
+    "Tool packaging/distribution",
+    "Visualization",
   ]
 usage environment:
   [
-    Cloud,
-    Google Colab,
-    HPC cluster,
-    Jupyter notebook,
-    Local installation,
-    Web browser,
+    "Cloud",
+    "Google Colab",
+    "HPC cluster",
+    "Jupyter notebook",
+    "Local installation",
+    "Web browser",
   ]
 software ecosystem:
   [
-    BigDataViewer,
-    Fiji,
-    ImgLib2,
-    Janelia Workstation,
-    Java Virtual Machine,
-    Napari,
-    PyTorch,
-    Spark,
-    Tensorflow,
+    "BigDataViewer",
+    "Blender",
+    "CUDA",
+    "Fiji",
+    "ImgLib2",
+    "Janelia Workstation",
+    "Java Virtual Machine",
+    "Napari",
+    "PyTorch",
+    "Spark",
+    "Tensorflow",
   ]
 supported file types:
   [

src/content/projects/trainspikingnet.md

@@ -0,0 +1,29 @@
+---
+title: TrainSpikingNet
+tagline: "train a spiking recurrent neural network"
+maintainer: ben arthur
+#project type - Required. Pick one of the three options - delete the other two.
+project type: [Other]
+github link: https://github.com/SpikingNetwork/TrainSpikingNet.jl
+documentation link: https://github.com/SpikingNetwork/TrainSpikingNet.jl?tab=readme-ov-file
+installation instructions link: https://github.com/SpikingNetwork/TrainSpikingNet.jl?tab=readme-ov-file#installation
+preferred contact method: mailto:[email protected]
+how to cite text: "Arthur, Kim, Chen, Preibisch, and Darshan (2023), A scalable implementation of the recursive least-squares algorithm for training spiking neural networks"
+how to cite link: "https://doi.org/10.3389/fninf.2023.1099510"
+image file: ./trainspikingnet.png
+image caption: a recurrent neural network
+youtube url: https://www.youtube.com/watch?v=VWISCzC2kOs
+youtube caption: TrainSpikingNet talk at JuliaCon 2022
+#Optional "tag" fields. Select tags from the provided options - delete the options that are not applicable. If you feel another option is required to describe your project, add it and then note this in your pull request.
+development team: [Scientific Computing Software]
+programming language: [Julia]
+open source license: [BSD-3 Clause]
+software type: [Command line application]
+use case: [Calcium imaging, Electrophysiology, Modelling, Neural recording]
+usage environment: [HPC cluster, Local installation]
+software ecosystem: [CUDA]
+supported file types: []
+---
+
+TrainSpikingNet uses recursive least squares to train fluctation-driven spiking
+recurrent neural networks to recapitulate arbitrary temporal activity patterns.