Athena Akrami
@athenaakrami.bsky.social
1.2K followers 320 following 90 posts
Neuroscientist at The Sainsbury Wellcome Centre, UCL, in London. Leading the "Learning, Inference & Memory" laboratory. Accidental advocate of #longcovid https://www.lim.bio/
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Reposted by Athena Akrami
carlosbrody.bsky.social
What happens in your brain when you make up your mind?

Postdoc (soon faculty at U. of Utah) @thomas-zhihao-luo.bsky.social and ex-grad student (now Shanahan Fellow at Allen Institute) @timkimd.bsky.social have some answers in this new paper out in Nature!

www.nature.com/articles/s41...

🧵 1/6
athenaakrami.bsky.social
Thanks, Matteo! Will do.
athenaakrami.bsky.social
Heron is open source, and we'd love to get feedback from the wider Python community.

We'd also like to explore how Heron can be used on other data analysis pipelines (besides neuroscience), and hope to see a community forming around the use and update of the code.

12/
athenaakrami.bsky.social
Heron is for those who
- Demand full control of their setup

- Prefer not to have to choose bet hardware/software that run only on a specific chip/OS combo

- Appreciate the ease & speed that high-level languages (eg Python) & Graphical User Interfaces (GUIs) offer them.

11/
athenaakrami.bsky.social
Heron is designed from the ground up to allow every experiment to be implemented using the tools the developers are more familiar with & the hardware they happen to own in the lab.

10/
athenaakrami.bsky.social
Heron’s main principle is to allow researchers to design and implement the experimental flow as close as possible to their mental schemata of the experiment, in the form of a Knowledge Graph. Self documentation and ease of reconstruction/reconfiguration comes for free.

9/
Heron’s combination of graphical and text-based code development.
(A) Schematic of Heron’s two levels of operation. Left shows a Knowledge Graph made of Nodes with Names, named Parameters and named Input/Output points. Nodes are connected with links (Edges) that define the flow of information. Right shows a piece of textual code that fully defines both the Node’s Graphical User Interface (GUI) appearance and the processes run by the machine. (B) Heron’s implementation of A, where the Knowledge Graph is generated in Heron’s Node Editor window while the code that each Node needs to be defined and the code that each Node is running is written in a separate Integrated Development Environment (IDE, in this case PyCharm).
athenaakrami.bsky.social
These all motivated George to develope Heron, a Knowledge Graph editor, that allows for hybrid programming. i.e. it has a graphical interface, which behaves a lot like Bonsai, but users can write their own nodes in Python.

8/
athenaakrami.bsky.social
Another issue to consider is to fit one's mental image of the experimental pipeline into existing commercialised or customised platforms.

Don't you like to be able to design & code up your experiment, as close as possible to your "mental schema" of how it would unfold?

7/
athenaakrami.bsky.social
Bonsai, is a tool that is very easy to use and put together simple experiments. However, it gets more difficult as the complexity of experiments increases.

6/
A graph showing how 'difficulty of use' scales with 'experiment complexity'.
athenaakrami.bsky.social
You may think, but wait, don't we have already a few pretty good options, like Bonsai & ROS?

Yes, they're powerful and great tools, but the ROS is mainly built in C++ and so it is not an easy tool for many experimentalists to use.

5/
A graph showing how 'difficulty of use' scales with 'experiment complexity'.
athenaakrami.bsky.social
Some platforms make it very difficult to flexibly connect various hardware that may run on different operating system/chip combinations. Or, piecing codes from different programming languages.

4/
athenaakrami.bsky.social
Some are too low level, too complicated. Some are too black-boxy, too opaque.

Some will end up with a spaghetti code, you have no idea how to keep track of after few iterations, let alone other ppl who'd like to use & expand your code/design.

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athenaakrami.bsky.social
Those of you who've attempted to code up a new experiment, requiring various complex hardware and software, know very well how daunting it can be to pick the right system-design platform and development environment.

Some are too specific, too restricted.
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athenaakrami.bsky.social
🎉 Heron is finally out @elife.bsky.social! Led by George Dimitriadis, with Ella Svahn & @macaskillaf.bsky.social

🧪 🧠 🐭 🤖

If you wonder why yet another tool for experimental pipelines, read the 🧵 below:

#neuroscience #neuroskyence #OpenSource

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elifesciences.org/articles/91915
Reposted by Athena Akrami
elife.bsky.social
Archerfish make lightning-fast decisions to catch prey, but their aim isn’t hardwired. They can adapt to new physics and generalise rules across contexts, all in under 100 ms.
buff.ly/7Ogh3B1
athenaakrami.bsky.social
I fully agree -- RNNs do form attractor dynamics. That cannot be used as an argument against attractors ...
athenaakrami.bsky.social
2\ In rodents, many important works from Svoboda (in collaboration w Romani) lab on motor preparation in ALM/thalamus get into nitty gritty of proving attractor dynamics, with most of your check list ticked
athenaakrami.bsky.social
Besides the misleading title, I disagree with Konrad on the RNN case -- training an ugly RNN to show all the properties of attractor dynamics means that the RNN has formed attractors!
athenaakrami.bsky.social
1\ Reading the title (as well as opening w 'mathematicsl objects can not be') sounded as if you're suggesting attractors, conceptually & in essence, cannot be considered as mechanistic models. But what you're saying is that experimentally, things have been consistent w attractors, but not full proof