MovSNN 0.0.3

.NET 7.0 This package targets .NET 7.0. The package is compatible with this framework or higher. 
dotnet add package MovSNN --version 0.0.3                

                    
                

            
NuGet\Install-Package MovSNN -Version 0.0.3
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package. 
<PackageReference Include="MovSNN" Version="0.0.3" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
paket add MovSNN --version 0.0.3                

                    
                

            
#r "nuget: MovSNN, 0.0.3"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package. 
// Install MovSNN as a Cake Addin
#addin nuget:?package=MovSNN&version=0.0.3

// Install MovSNN as a Cake Tool
#tool nuget:?package=MovSNN&version=0.0.3

A custom Spiking Neural Network implementation with a bayesian decoding scheme for supervised interpretation

Product Compatible and additional computed target framework versions.
.NET net7.0 is compatible.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed.  net8.0 was computed.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (1)

Showing the top 1 NuGet packages that depend on MovSNN:

Package Downloads
MovSNN.View

A library for displaying your neural networks using MovSNN

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Version Downloads Last updated
0.0.3 274 10/30/2023
0.0.1 143 10/14/2023

Now with vector decoders, unsupervised vector clustering, more generics and modularity, fixing some bugs in the neurons setup and also a basic approximation of reinforcement learning with the action table.