feat: Fix UHEI/BrainScaleS-2

Changes:
* we use BrainScaleS-2 with an hyphen
* there are 3 top-level APIs to use it
* the number of synapses per ASIC was wrong at some places
*

Author: muffgaga

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

@@ -1,49 +1,49 @@
 ---
 active_product: true
-description: "Learn about Universität Heidelberg's neuromorphic hardware: BrainScaleS 2"
+description: "Learn about Heidelberg University's neuromorphic hardware: BrainScaleS-2"
 type: neuromorphic-hardware
 image: brainscales-2.jpg
 organization:
   group_name: null
   org_logo: heidelberg.jpg
-  org_name: Universität Heidelberg
+  org_name: Heidelberg University
   org_website: null
   product_page_link: https://wiki.ebrains.eu/bin/view/Collabs/neuromorphic/BrainScaleS/
   social_media_links:
     linkedin: https://www.linkedin.com/company/ebrains-eu/
     twitter: https://twitter.com/ebrains_eu
-    wikipedia: https://wiki.ebrains.eu/bin/view/Collabs/neuromorphic/BrainScaleS/
+    wikipedia: null
 product:
   announced_date: 2020-03-26
   applications: Edge processing, robotics
   chip_type: Mixed-signal
   neurons: 512
-  synapses: 130000
-  weight_bits: null
+  synapses: 131072
+  weight_bits: 6 bits (+ 6 bit mask for structural plasticity)
   activation_bits: null
   on_chip_learning: true
   power: ~1 W
   release_year: 2022
   release_date: 2022-02-24
-  software: hxtorch
+  software: hxtorch, jaxsnn, PyNN.brainscales2, BrainScaleS-2 OS
   status:
     announced: true
     released: true
     retired: false
-product_name: BrainScaleS 2
+product_name: BrainScaleS-2
 summary: The BrainScaleS-2 is an accelerated spiking neuromorphic system-on-chip integrating
-  512 adaptive integrate-and-fire neurons, 212k plastic synapses, embedded processors,
+  512 adaptive integrate-and-fire neurons, 131k plastic synapses, embedded processors,
   and event routing. It enables fast emulation of complex neural dynamics and exploration
   of synaptic plasticity rules. The architecture supports training of deep spiking
   and non-spiking neural networks using hybrid techniques like surrogate gradients.
-title: BrainScaleS 2 - Universität Heidelberg
+title: BrainScaleS-2 — Heidelberg University
 type: neuromorphic-hardware
 ---
 
 The BrainScaleS-2 accelerated neuromorphic system is an integrated circuit architecture for emulating biologically-inspired spiking neural networks. It was developed by researchers at the Heidelberg University and collaborators. Key features of the BrainScaleS-2 system include:
 
 ## System Architecture
-- Single-chip ASIC integrating a custom analog core with 512 neuron circuits, 212k plastic synapses, analog parameter storage, embedded processors for digital control and plasticity, and an event routing network
+- Single-chip ASIC integrating a custom analog core with 512 neuron circuits, 131k plastic synapses, analog parameter storage, embedded processors for digital control and plasticity, and an event routing network
 - Processor cores run a software stack with a C++ compiler and support hybrid spiking and non-spiking neural network execution
 - Capable as a unit of scale for larger multi-chip or wafer-scale systems
 
@@ -57,9 +57,9 @@ The BrainScaleS-2 accelerated neuromorphic system is an integrated circuit archi
 - Massively parallel readout of analog observables enables gradient-based and surrogate gradient optimization approaches
 
 ## Applications and Experiments
-- Accelerated (1,000-fold compared to biological real time) emulation of complex spiking neuron dynamics, multi-compartment models, and path integration circuits
+- Accelerated (1,000-fold compared to biological real time) emulation of complex spiking neural network dynamics, including configurable multi-compartmental cell morphologies
 - Exploration of synaptic plasticity models and critical network dynamics at biological timescales
-- Training of deep spiking neural networks using surrogate gradient techniques
+- Training of deep spiking neural networks using surrogate and exact gradient techniques
 - Non-spiking neural network execution leveraging synaptic crossbar for analog matrix multiplication
 
 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.
@@ -68,4 +68,8 @@ The accelerated operation and flexible architecture facilitate applications in c
 
 | Date | Title | Authors  | Venue/Source |
 |------|-------|----------|------------- |
-| January 2022 | [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://arxiv.org/abs/2201.11063) | Christian Pehle, Sebastian Billaudelle, Benjamin Cramer, Jakob Kaiser, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Aron Leibfried, Eric Müller, Johannes Schemmel | arXiv |
+| April 2024 | [jaxsnn: Event-driven gradient estimation for analog neuromorphic hardware](https://doi.org/10.1109/NICE61972.2024.10548709) | Eric Müller, Moritz Althaus, Elias Arnold, Philipp Spilger, Christian Pehle, Johannes Schemmel | 2024 Neuro-Inspired Computational Elements Conference (NICE) |
+| April 2023 | [hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2](https://doi.org/10.1145/3584954.3584993) | Philipp Spilger, Elias Arnold, Luca Blessing, Christian Mauch, Christian Pehle, Eric Müller, Johannes Schemmel | 2023 Neuro-Inspired Computational Elements Conference (NICE) |
+| May 2022 | [A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware](https://doi.org/10.3389/fnins.2022.884128) | Eric Müller, Elias Arnold, Oliver Breitwieser, Milena Czierlinski, Arne Emmel, Jakob Kaiser, Christian Mauch, Sebastian Schmitt, Philipp Spilger, Raphael Stock, Yannik Stradmann, Johannes Weis, Andreas Baumbach, Sebastian Billaudelle, Benjamin Cramer, Falk Ebert, Julian Göltz, Joscha Ilmberger, Vitali Karasenko, Mitja Kleider, Aron Leibfried, Christian Pehle, Johannes Schemmel | Frontiers in Neuroscience (Neuromorphic Engineering) |
+| February 2022 | [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://doi.org/10.3389/fnins.2022.795876) | Christian Pehle, Sebastian Billaudelle, Benjamin Cramer, Jakob Kaiser, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Aron Leibfried, Eric Müller, Johannes Schemmel | Frontiers in Neuroscience (Neuromorphic Engineering) |
+| January 2021 | [hxtorch: PyTorch for BrainScaleS-2 — Perceptrons on Analog Neuromorphic Hardware](https://doi.org/10.1007/978-3-030-66770-2_14) | Philipp Spilger, Eric Müller, Arne Emmel, Aron Leibfried, Christian Mauch, Christian Pehle, Johannes Weis, Oliver Breitwieser, Sebastian Billaudelle, Sebastian Schmitt, Timo C. Wunderlich, Yannik Stradmann, Johannes Schemmel | 2020 International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM) |