feat(BSS2): Add jaxsnn logo and version information

Author: benkroehs

content/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/index.md

@@ -61,6 +61,10 @@ The BrainScaleS-2 accelerated neuromorphic system is an integrated circuit archi
 - Exploration of synaptic plasticity models and critical network dynamics at biological timescales
 - Training of deep spiking neural networks using surrogate and exact gradient techniques
 - Non-spiking neural network execution leveraging synaptic crossbar for analog matrix multiplication
+- Available via three different software frameworks:
+  - [jaxsnn](https://open-neuromorphic.org/neuromorphic-computing/software/snn-frameworks/jaxsnn/), a JAX-based framework for event-based numerical simulation of SNNs
+  - [hxtorch](https://open-neuromorphic.org/neuromorphic-computing/software/snn-frameworks/hxtorch/), a PyTorch-based deep learning Python library for SNNs
+  - [PyNN.brainscales2](https://open-neuromorphic.org/neuromorphic-computing/software/snn-frameworks/pynn-brainscales2), an implementation of the PyNN API
 
 The accelerated operation and flexible architecture facilitate applications in computational neuroscience research and novel machine learning approaches. The system design serves as a scalable basis for future large-scale neuromorphic computing platforms.
 

content/neuromorphic-computing/software/snn-frameworks/hxtorch/index.md

@@ -6,6 +6,9 @@ website: https://electronicvisions.github.io/documentation-brainscales2/latest/
 dependencies: PyTorch, BrainScaleS-2 OS
 field_of_application: Machine Learning, Neuromorphic Hardware, In-the-loop Training
 source_code: https://github.com/electronicvisions/hxtorch
+stars_widget_url: https://img.shields.io/github/stars/electronicvisions/hxtorch
+stars: 9
+version_widget_url: https://img.shields.io/github/v/tag/electronicvisions/releases-ebrains
 license: LGPL-2.0-or-later
 supports_hardware: True
 supports_NIR: True
@@ -16,7 +19,7 @@ maintainer: Electronic Visions Group
 
 ## Overview
 
-**hxtorch** is a deep learning Python library used for numerical simulation, neuromorphic emulation and training of spiking neural networks (SNNs). Built on top of PyTorch, it integrates the automatic differentiation and modular design of the PyTorch ecosystem with neuromorphic experiment execution, enabling hardware-in-the-loop training workflows on the neuromorphic hardware system BrainScaleS-2 .
+**hxtorch** is a deep learning Python library used for numerical simulation, neuromorphic emulation and training of spiking neural networks (SNNs). Built on top of PyTorch, it integrates the automatic differentiation and modular design of the PyTorch ecosystem with neuromorphic experiment execution, enabling hardware-in-the-loop training workflows on the neuromorphic hardware system [BrainScaleS-2](https://open-neuromorphic.org/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/).
 
 The library abstracts the hardware configuration and experiment execution, while allowing users to define networks using familiar PyTorch modules such as LIF and LI neuron layers and synaptic connections. By separating network definition from execution, hxtorch supports both software simulation and hardware emulation within a single, unified API.
 

content/neuromorphic-computing/software/snn-frameworks/jaxsnn/index.md

@@ -2,10 +2,14 @@
 title: "jaxsnn"
 type: neuromorphic-software
 description: Event-based training of spiking neural networks with support for BrainScaleS-2 hardware-in-the-loop based on JAX.
+logo: jaxsnn.png
 website: https://electronicvisions.github.io/documentation-brainscales2/latest/
 dependencies: JAX, BrainScaleS-2 OS
 field_of_application: Machine Learning, Neuromorphic Hardware, In-the-loop Training, Event-based Training
 source_code: https://github.com/electronicvisions/jaxsnn
+stars_widget_url: https://img.shields.io/github/stars/electronicvisions/jaxsnn
+stars: 20
+version_widget_url: https://img.shields.io/pypi/v/jaxsnn
 license: LGPL-2.0-or-later
 supports_hardware: True
 supports_NIR: True
@@ -16,7 +20,7 @@ maintainer: Electronic Visions Group
 
 ## Overview
 
-**jaxsnn** is a deep learning Python library used for event-based numerical simulation, neuromorphic emulation and training of spiking neural networks (SNNs) with BrainScaleS-2 neuromorphic hardware in-the-loop. It is maintained by the Electronic Visions group at Heidelberg University.
+**jaxsnn** is a deep learning Python library used for event-based numerical simulation, neuromorphic emulation and training of spiking neural networks (SNNs) with [BrainScaleS-2](https://open-neuromorphic.org/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/) neuromorphic hardware in-the-loop. It is maintained by the Electronic Visions group at Heidelberg University.
 
 Unlike conventional deep learning libraries, which rely on dense tensor representations and time-discretized updates, jaxsnn is designed for event-driven computation. It directly operates on asynchronous spike events and supports gradient-based learning using methods such as EventProp and “Fast & Deep” spike-time coding. The library leverages JAX’s automatic differentiation, just-in-time compilation (via XLA) and support for hardware acceleration to enable efficient and composable training of biologically inspired SNNs.
 

content/neuromorphic-computing/software/snn-frameworks/jaxsnn/jaxsnn.png

@@ -6,6 +6,9 @@ website: https://electronicvisions.github.io/documentation-brainscales2/latest/
 dependencies: PyNN, BrainScaleS-2 OS
 field_of_application: Neuromorphic Hardware, Computational Neuroscience, Spiking Neural Networks, Local Plasticity
 source_code: https://github.com/electronicvisions/pynn-brainscales
+stars_widget_url: https://img.shields.io/github/stars/electronicvisions/pynn-brainscales
+stars: 6
+version_widget_url: https://img.shields.io/github/v/tag/electronicvisions/releases-ebrains
 license: LGPL-2.0-or-later
 supports_hardware: True
 supports_NIR: False
@@ -16,7 +19,7 @@ maintainer: Electronic Visions Group
 
 ## Overview
 
-**PyNN.brainscales2** is an implementation of the backend-agnostic PyNN API for BrainScaleS-2.
+**PyNN.brainscales2** is an implementation of the backend-agnostic PyNN API for [BrainScaleS-2](https://open-neuromorphic.org/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/).
 It supports arbitrary topologies, and complex plasticity rules.
 
 Custom cell types are available allowing fine-grained access to the configuration of the available neuron circuits on hardware.