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#computationalneuroscience — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #computationalneuroscience, aggregated by home.social.

  1. Shoutout to Peter Ohue for representing Neuromatch at a CompNeuro Nigeria webinar!

    He shared our free, open source learning materials, along with information on our virtual course that runs each July.

    #ComputationalNeuroscience #CompNeuro #NeuroAI #DeepLearning #OpenScience #Africa

  2. 🙋If you or your community are curious about #ComputationalNeuroscience, there is likely a Nueromatch Ambassador near you, or one who speaks your language, who would be glad to give a talk or answer questions.

    ➡️ Connect with an Ambassador: neuromatch.io/ambassadors/

    #CompNeuro #Neuromatch

  3. The ANDA-NI Retreat has begun, marking the third component of this year's Advanced Neural Data Analysis and Neuroinformatics programme. Nicholas A. Del Grosso and I are excited to kick off this closing project week together with a highly international crowd of dedicated participants.

    Over the past weeks, participants worked through two online courses covering the mathematical foundations of neural data analysis (ANDA) and data management skills from neuroinformatics (NI). Now comes the part that matters just as much: applying that knowledge to real data, contributed by the participants themselves, and collaborating in small teams to tackle research questions.

    Mastering the equations behind a technique is only half the job. The other half is developing an eye for data, learning to read what an analysis is actually telling you, spotting when something doesn't look right, and knowing how to embed and combine analysis methods into workflows that are reproducible and shareable with others. That's the skillset we're aiming to build this week.

    Looking forward to an exciting and hands-on week ahead.

    andani.info

    #Neuroscience #Neuroinformatics #DataAnalysis #OpenScience #ReproducibleResearch #ComputationalNeuroscience #ScienceEducation

  4. Meet Apollo Yang, a #Neuromatch Ambassador!

    Whether you are part of the 2026 #NeuromatchAcademy cohort or just curious about taking a course in the future, reach out and connect with a Neuromatch Ambassador!

    ➡️ neuromatch.io/ambassadors/

    #ComputationalNeuroscience #CompNeuro #NeuroAI

  5. Building detailed neuronal models is hard---#NeuroML helps, but there’s still a steep learning curve. We're investigating how can be used to create natural language interfaces to improve accessibility:

    - for NeuroML: learn from and query curated information sources
    - iterative LLM assisted code/model generation: modelling, validation, and simulation

    More details in the post:

    ankursinha.in/2026/01/30/build

  6. Building detailed neuronal models is hard---#NeuroML helps, but there’s still a steep learning curve. We're investigating how #LLMs can be used to create natural language interfaces to improve accessibility:

    - #RAG for NeuroML: learn from and query curated information sources
    - iterative LLM assisted code/model generation: modelling, validation, and simulation

    More details in the post:

    ankursinha.in/2026/01/30/build

    #Neuroscience #ComputationalNeuroscience #AcademicChatter #LangChain

  7. Building detailed neuronal models is hard---#NeuroML helps, but there’s still a steep learning curve. We're investigating how #LLMs can be used to create natural language interfaces to improve accessibility:

    - #RAG for NeuroML: learn from and query curated information sources
    - iterative LLM assisted code/model generation: modelling, validation, and simulation

    More details in the post:

    ankursinha.in/2026/01/30/build

    #Neuroscience #ComputationalNeuroscience #AcademicChatter #LangChain

  8. Building detailed neuronal models is hard---#NeuroML helps, but there’s still a steep learning curve. We're investigating how #LLMs can be used to create natural language interfaces to improve accessibility:

    - #RAG for NeuroML: learn from and query curated information sources
    - iterative LLM assisted code/model generation: modelling, validation, and simulation

    More details in the post:

    ankursinha.in/2026/01/30/build

    #Neuroscience #ComputationalNeuroscience #AcademicChatter #LangChain

  9. A lot of steps for starting new computational modelling projects in are repeatable and can be automated. So, I created a template to quickstart these projects. It includes boilerplate code and implements lots of other recommended/best practices in model and software development. Feedback/suggestions welcome!

    ankursinha.in/2025/09/23/a-coo

  10. A lot of steps for starting new computational modelling projects in #NeuroML are repeatable and can be automated. So, I created a #Cookiecutter template to quickstart these projects. It includes boilerplate code and implements lots of other recommended/best practices in model and software development. Feedback/suggestions welcome!

    ankursinha.in/2025/09/23/a-coo

    #Neuroscience #ComputationalNeuroscience #Python #Git #AcademicChatter #FAIR #OpenScience #FOSS #SoftwareDevelopment

  11. A lot of steps for starting new computational modelling projects in #NeuroML are repeatable and can be automated. So, I created a #Cookiecutter template to quickstart these projects. It includes boilerplate code and implements lots of other recommended/best practices in model and software development. Feedback/suggestions welcome!

    ankursinha.in/2025/09/23/a-coo

    #Neuroscience #ComputationalNeuroscience #Python #Git #AcademicChatter #FAIR #OpenScience #FOSS #SoftwareDevelopment

  12. A new version of the Open Source Brain (#OpenSourceBrain) model validation framework was just released. Please update your installations:

    ```
    pip install -U OSBModelValidation
    ```

    github.com/OpenSourceBrain/osb

    is a package that allows you to validate your models against different simulation engines---to ensure that you get the same behaviours on all these engines. Examples:

    github.com/OpenSourceBrain/.gi

  13. A new version of the Open Source Brain (#OpenSourceBrain) model validation framework was just released. Please update your installations:

    ```
    pip install -U OSBModelValidation
    ```

    github.com/OpenSourceBrain/osb

    #OMV is a #Python package that allows you to validate your #NeuroML models against different simulation engines---to ensure that you get the same behaviours on all these engines. Examples:

    github.com/OpenSourceBrain/.gi

    #FAIR #ComputationalNeuroscience #Neuroscience #ModelValidation

  14. A new version of the Open Source Brain (#OpenSourceBrain) model validation framework was just released. Please update your installations:

    ```
    pip install -U OSBModelValidation
    ```

    github.com/OpenSourceBrain/osb

    #OMV is a #Python package that allows you to validate your #NeuroML models against different simulation engines---to ensure that you get the same behaviours on all these engines. Examples:

    github.com/OpenSourceBrain/.gi

    #FAIR #ComputationalNeuroscience #Neuroscience #ModelValidation

  15. A new release of is available. Please update to get the latest fixes and features.

    ```
    pip install --upgrade pyneuroml
    ```

  16. A new release of #PyNeuroML is available. Please update to get the latest fixes and features. #NeuroML #ComputationalModelling #ComputationalNeuroscience

    ```
    pip install --upgrade pyneuroml
    ```

  17. A new release of #PyNeuroML is available. Please update to get the latest fixes and features. #NeuroML #ComputationalModelling #ComputationalNeuroscience

    ```
    pip install --upgrade pyneuroml
    ```

  18. In the next version, 0.6.6, all component classes in the API gain a quick method to list the parameters of their model objects. So, if you load a file/model, you can easily print the parameters of the different model entities. See the test in the pull request for a quick example:

    github.com/NeuralEnsemble/libN

  19. In the next version, 0.6.6, all component classes in the #libNeuroML #Python API gain a quick method to list the parameters of their model objects. So, if you load a #NeuroML file/model, you can easily print the parameters of the different model entities. See the test in the pull request for a quick example:

    github.com/NeuralEnsemble/libN

    #ComputationalNeuroscience #AcademicChatter #Neuroscience #FAIR

  20. In the next version, 0.6.6, all component classes in the #libNeuroML #Python API gain a quick method to list the parameters of their model objects. So, if you load a #NeuroML file/model, you can easily print the parameters of the different model entities. See the test in the pull request for a quick example:

    github.com/NeuralEnsemble/libN

    #ComputationalNeuroscience #AcademicChatter #Neuroscience #FAIR

  21. I keep going back to this question about #TemporalCreditAssignment and #HippocampalReplay:
    As an "agent" you want to learn the value of places and which places are likely to lead to reward;

    -1) if a place leads to higher than expected reward, you'll want to propagate back the reward info from the reward throughout the places that led to the reward. If replay does that you should see an increase of replay at a new reward site and the replay sequences should start at the reward and reflect what you just did to reach it. Right?

    -2) if a place leads to lower than expected reward, you'll also want to propagate that lowered value, pretty much in the same way, so if replay does that you should see a similar replay rate and content for increased OR decreased reward sites. Right?

    -3) if a place has had unchanged reward for a while and you're just in exploitation mode (just going there again and again because you know that's the best place to go to in the environment) then you shouldn't need to update anything and replay rate should be quite low at that unchanged reward side. Right?

    That's not at all what replay is doing IRL, so does that mean replay is not used for temporal credit assignment? Or did I (very likely) miss something?

    #Neuroscience #ComputationalNeuroscience #DecisionMaking #Hippocampus

  22. is participating in again this year under @INCF . We're looking for people with some experience of to work on developing biophysically detailed computational models using and .

    Please spread the word, especially to students interested in modelling. We will help them learn the NeuroML ecosystem so they can use its standardised pipeline in their work.

    docs.neuroml.org/NeuroMLOrg/Ou

    CC

  23. #NeuroML is participating in #GSoC2025 again this year under @INCF . We're looking for people with some experience of #ComputationalNeuroscience to work on developing #standardised biophysically detailed computational models using #NeuroML #PyNN and #OpenSourceBrain.

    Please spread the word, especially to students interested in modelling. We will help them learn the NeuroML ecosystem so they can use its standardised pipeline in their work.

    docs.neuroml.org/NeuroMLOrg/Ou

    CC #AcademicChatter

  24. #NeuroML is participating in #GSoC2025 again this year under @INCF . We're looking for people with some experience of #ComputationalNeuroscience to work on developing #standardised biophysically detailed computational models using #NeuroML #PyNN and #OpenSourceBrain.

    Please spread the word, especially to students interested in modelling. We will help them learn the NeuroML ecosystem so they can use its standardised pipeline in their work.

    docs.neuroml.org/NeuroMLOrg/Ou

    CC #AcademicChatter

  25. We are very happy to provide a consolidated update on the ecosystem in our @eLife paper, “The NeuroML ecosystem for standardized multi-scale modeling in neuroscience”: doi.org/10.7554/eLife.95135.3

    is a standard and software ecosystem for data-driven biophysically detailed endorsed by the @INCF and CoMBINE, and includes a large community of users and software developers.

    1/x

  26. We are very happy to provide a consolidated update on the #NeuroML ecosystem in our @eLife paper, “The NeuroML ecosystem for standardized multi-scale modeling in neuroscience”: doi.org/10.7554/eLife.95135.3

    #NeuroML is a standard and software ecosystem for data-driven biophysically detailed #ComputationalModelling endorsed by the @INCF and CoMBINE, and includes a large community of users and software developers.

    #Neuroscience #ComputationalNeuroscience #ComputationalModelling 1/x

  27. We are very happy to provide a consolidated update on the #NeuroML ecosystem in our @eLife paper, “The NeuroML ecosystem for standardized multi-scale modeling in neuroscience”: doi.org/10.7554/eLife.95135.3

    #NeuroML is a standard and software ecosystem for data-driven biophysically detailed #ComputationalModelling endorsed by the @INCF and CoMBINE, and includes a large community of users and software developers.

    #Neuroscience #ComputationalNeuroscience #ComputationalModelling 1/x

  28. Are you interested in a masters or PhD in computational neuroscience? Don't miss @bccn_berlin's hybrid event!

    🧠 International graduate program(s) computational neuroscience
    📅 29-Jan-2025, 15-18 CET
    📍 BCCN Lecture hall & Zoom

    Learn more & register: bit.ly/4h0zj9y

    #neuroscience #neuroinformatics #ComputationalNeuroscience