feat: blog: Add author bios and contributor info

Author: neural-loop

content/blog/efficient-compression-event-based-data-neuromorphic-applications/index.md

@@ -10,6 +10,7 @@ author:
 - Alexandre Marcireau
 image: file_read_benchmark.png
 tags: ["file encoding", "events", "event camera", "compression"]
+show_author_bios: true
 ---
 
 ## Datasets grow larger in size
@@ -40,10 +41,5 @@ The authors of this post have released [Expelliarmus](/neuromorphic-computing/so
 ## Summary
 When training spiking neural networks on event-based data, we want to be able to feed new data to the network as fast as possible. But given the high data rate of an event camera, the amount of data quickly becomes an issue itself, especially for more complex tasks. So we want to choose a good trade-off between a dataset size that's manageable and reading speed. We hope that this article will help future groups that record large-scale datasets to pick a good encoding format. 
 
-## Authors
-* [Gregor Lenz](https://lenzgregor.com) is a research engineer at SynSense, where he works on machine learning pipelines that can train and deploy robust models on neuromorphic hardware. He holds a PhD in neuromorphic engineering from Sorbonne University in Paris, France.
-* [Fabrizio Ottati](https://fabrizio-ottati.dev) is a Ph.D. student in the HLS Laboratory of the Department of Electronics and Communications, Politecnico di Torino. His main interests are event-based cameras, digital hardware design and neuromorphic computing. He is one of the maintainers of two open source projects in the field of neuromorphic computing, [Tonic](/neuromorphic-computing/software/data-tools/tonic/) and [Expelliarmus](/neuromorphic-computing/software/data-tools/expelliarmus/), and one of the founders of [Open Neuromorphic](https://open-neuromorphic.org).
-* [Alexandre Marcireau](https://github.com/aMarcireau).
-
 ## Comments
 The aedat4 file contains IMU events as well as change detection events, which increases the file size artificially in contrast to the other benchmarked formats.

content/blog/truenorth-deep-dive-ibm-neuromorphic-chip-design/index.md

@@ -9,6 +9,7 @@ author:
 tags: ["research", "hardware", "digital", "neuromorphic"]
 showTableOfContents: true
 math: true
+show_author_bios: true
 ---
 
 ## Why do we want to emulate the brain?
@@ -236,10 +237,6 @@ What we have discussed until now is the model of a TrueNorth core: in this, 256
 
 *Stay tuned for episode II, in which we will dive in the theory behind the asynchronous design of TrueNorth :)*
 
-## Authors
-
-- [Fabrizio Ottati](https://fabrizio-ottati.dev) is a Ph.D. student in the HLS Laboratory of the Department of Electronics and Communications, Politecnico di Torino. His main interests are event-based cameras, digital hardware design and neuromorphic computing. He is one of the maintainers of two open source projects in the field of neuromorphic computing, [Tonic](https://tonic.readthedocs.io) and [Expelliarmus](https://expelliarmus.readthedocs.io), and one of the founders of [Open Neuromorphic](https://open-neuromorphic.org).
-
 ## Bibliography 
 * [*TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip*](https://redwood.berkeley.edu/wp-content/uploads/2021/08/Akopyan2015.pdf), Filipp Akopyan et al.
 * [*Foundations of neuroscience - The neuron*](https://openbooks.lib.msu.edu/neuroscience/chapter/the-neuron/), Casey Henley, Michigan State University.

content/contributors/alexandre-marcireau/index.md

@@ -1,7 +1,10 @@
 ---
 title: "Alexandre Marcireau"
 description: "Software developer in neuromorphic computing, recognized for his contributions to the Faery event processing library for event-based camera data."
-social: []
+social:
+  - icon: "fa-brands fa-github"
+    link: "https://github.com/aMarcireau"
+    title: "github"
 draft: false
 ---
 Alexandre Marcireau is a software developer and contributor in the neuromorphic computing space, known for his work on the Faery event processing library.