#computationalneuroscience — Public Fediverse posts

Gave a talk at the Crick institute on "Does heterogeneity help the brain and how could we know if it did?" part of a series we organised with @marcusghosh. It was fun to present work aimed at generating discussion.

https://neural-reckoning.org/talk_2026_crick_heterogeneity.html

In particular, it was a new experience to present work not primarily to get people excited about the ideas in the work, but to raise questions like: can these ideas be tested? Does this approach to doing computational neuroscience make sense? etc.

We should perhaps do more of this!

#neuroscience #ComputationalNeuroscience

Gave a talk at the Crick institute on "Does heterogeneity help the brain and how could we know if it did?" part of a series we organised with @marcusghosh. It was fun to present work aimed at generating discussion.

https://neural-reckoning.org/talk_2026_crick_heterogeneity.html

In particular, it was a new experience to present work not primarily to get people excited about the ideas in the work, but to raise questions like: can these ideas be tested? Does this approach to doing computational neuroscience make sense? etc.

We should perhaps do more of this!

#neuroscience #ComputationalNeuroscience

Gave a talk at the Crick institute on "Does heterogeneity help the brain and how could we know if it did?" part of a series we organised with @marcusghosh. It was fun to present work aimed at generating discussion.

https://neural-reckoning.org/talk_2026_crick_heterogeneity.html

In particular, it was a new experience to present work not primarily to get people excited about the ideas in the work, but to raise questions like: can these ideas be tested? Does this approach to doing computational neuroscience make sense? etc.

We should perhaps do more of this!

#neuroscience #ComputationalNeuroscience

Gave a talk at the Crick institute on "Does heterogeneity help the brain and how could we know if it did?" part of a series we organised with @marcusghosh. It was fun to present work aimed at generating discussion.

https://neural-reckoning.org/talk_2026_crick_heterogeneity.html

In particular, it was a new experience to present work not primarily to get people excited about the ideas in the work, but to raise questions like: can these ideas be tested? Does this approach to doing computational neuroscience make sense? etc.

We should perhaps do more of this!

#neuroscience #ComputationalNeuroscience

Gave a talk at the Crick institute on "Does heterogeneity help the brain and how could we know if it did?" part of a series we organised with @marcusghosh. It was fun to present work aimed at generating discussion.

https://neural-reckoning.org/talk_2026_crick_heterogeneity.html

In particular, it was a new experience to present work not primarily to get people excited about the ideas in the work, but to raise questions like: can these ideas be tested? Does this approach to doing computational neuroscience make sense? etc.

We should perhaps do more of this!

#neuroscience #ComputationalNeuroscience

Progress on building an #AI assistant for #Neuroscience: scope, funding, ideas, and a name: #Klea

https://ankursinha.in/2026/09/08/neuroscience-ai-assistant-updates-scope-funding-ideas-and-a-name-klea.html

#Neuroscience #OpenScience #LLM #LangChain #LangGraph #MCP #Python #FastAPI #FastMCP #NeuroML #NWB #ComputationalNeuroscience #ComputationalModelling #DataAnalysis #Klea

TLDR: the #RAG is ready for you to play with. The coding agent is WIP.

📚 📈#Words continue to evolve: Why #language, #memes, and #brains follow the #logic of #evolution 🧬🧠

📽 https://youtu.be/M2qiVz95ZYk

📎 https://philosophies.de/index.php/2023/12/25/naturalistic-view/

#DanielDennett #PhilosophyOfMind #CognitiveScience #Memetics #CulturalEvolution #TheoryOfEvolution #Consciousness #ArtificialIntelligence #AGI #Neuroscience #ComputationalNeuroscience #Zoomposium #SelfAndIdentity #MindAndBrain #Naturalism #Functionalism #Embodiment #InformationTheory #ScienceCommunication

#NeuralDynamics is a central subfield of #ComputationalNeuroscience studying timedependent #NeuralActivity and its governing #mathematics. It examines how #NeuralStates evolve, how stable or unstable patterns arise, and how #learning reshapes them. Neural dynamics forms the backbone for how #neurons & #NeuralNetworks generate complex activity over time. This post gives a brief overview of the field & its historical milestones:

🌍https://www.fabriziomusacchio.com/blog/2026-02-04-neural_dynamics/

#CompNeuro #Neuroscience #DynamicalSystems

I was looking through the VSAonline website (https://sites.google.com/view/hdvsaonline/home) and saw that by the end of this year there will be 96 recorded webinars plus the recordings from the Midnight Sun 2023 workshop.

So, if you're interested in #VectorSymbolicArchitecture / #HyperdimensionalComputing or just #VSA / #HDC -curious, the VSAonline website is the place to go (or head over to https://www.hd-computing.com/home, where there's a wider range of resources, including publication lists).

#CognitiveScience #CogSci #ComputationalCognitiveScience #CompCogSci #AI #ArtificialIntelligence #ML #MachineLearning #neuromorphic #ComputationalNeuroscience

Rethinking the #hippocampal #cognitivemap as a #metalearning computational module – New publication by Luca Ambrogioni &
H. Freyja Ólafsdóttir (2023)

📔 https://doi.org/10.1016/j.tics.2023.05.011

#computationalneuroscience #learning

In the same vein as my #arxivfeed thing, here's a paper that I've been reading and really enjoying. I decided to spend more time on it than I usually do when reading papers because I wanted to search for gaps in my knowledge, and I really don't regret that decision! I'm only at the 4th section at the moment and I find it very well written, especially in the framing of things. So far it's a great overview!

"Neural Field Models: A mathematical overview and unifying framework"
https://arxiv.org/abs/2103.10554v4

#Neuroscience #ComputationalNeuroscience #MathematicalNeuroscience #NeuralFieldModelling #Biophysical #DynamicalSystems

In the same vein as my #arxivfeed thing, here's a paper that I've been reading and really enjoying. I decided to spend more time on it than I usually do when reading papers because I wanted to search for gaps in my knowledge, and I really don't regret that decision! I'm only at the 4th section at the moment and I find it very well written, especially in the framing of things. So far it's a great overview!

"Neural Field Models: A mathematical overview and unifying framework"
https://arxiv.org/abs/2103.10554v4

#Neuroscience #ComputationalNeuroscience #MathematicalNeuroscience #NeuralFieldModelling #Biophysical #DynamicalSystems

In the same vein as my #arxivfeed thing, here's a paper that I've been reading and really enjoying. I decided to spend more time on it than I usually do when reading papers because I wanted to search for gaps in my knowledge, and I really don't regret that decision! I'm only at the 4th section at the moment and I find it very well written, especially in the framing of things. So far it's a great overview!

"Neural Field Models: A mathematical overview and unifying framework"
https://arxiv.org/abs/2103.10554v4

#Neuroscience #ComputationalNeuroscience #MathematicalNeuroscience #NeuralFieldModelling #Biophysical #DynamicalSystems

In the same vein as my #arxivfeed thing, here's a paper that I've been reading and really enjoying. I decided to spend more time on it than I usually do when reading papers because I wanted to search for gaps in my knowledge, and I really don't regret that decision! I'm only at the 4th section at the moment and I find it very well written, especially in the framing of things. So far it's a great overview!

"Neural Field Models: A mathematical overview and unifying framework"
https://arxiv.org/abs/2103.10554v4

#Neuroscience #ComputationalNeuroscience #MathematicalNeuroscience #NeuralFieldModelling #Biophysical #DynamicalSystems

In the same vein as my #arxivfeed thing, here's a paper that I've been reading and really enjoying. I decided to spend more time on it than I usually do when reading papers because I wanted to search for gaps in my knowledge, and I really don't regret that decision! I'm only at the 4th section at the moment and I find it very well written, especially in the framing of things. So far it's a great overview!

"Neural Field Models: A mathematical overview and unifying framework"
https://arxiv.org/abs/2103.10554v4

#Neuroscience #ComputationalNeuroscience #MathematicalNeuroscience #NeuralFieldModelling #Biophysical #DynamicalSystems

#arxivfeed

"JGAT: a joint spatio-temporal graph attention model for brain decoding"
https://arxiv.org/abs/2306.05286

#Neuroscience #Neuroimaging #ComputationalNeuroscience #Connectivity #MachineLearning #ML

#arxivfeed

"JGAT: a joint spatio-temporal graph attention model for brain decoding"
https://arxiv.org/abs/2306.05286

#Neuroscience #Neuroimaging #ComputationalNeuroscience #Connectivity #MachineLearning #ML

#arxivfeed

"JGAT: a joint spatio-temporal graph attention model for brain decoding"
https://arxiv.org/abs/2306.05286

#Neuroscience #Neuroimaging #ComputationalNeuroscience #Connectivity #MachineLearning #ML

#arxivfeed

"JGAT: a joint spatio-temporal graph attention model for brain decoding"
https://arxiv.org/abs/2306.05286

#Neuroscience #Neuroimaging #ComputationalNeuroscience #Connectivity #MachineLearning #ML

#arxivfeed

"JGAT: a joint spatio-temporal graph attention model for brain decoding"
https://arxiv.org/abs/2306.05286

#Neuroscience #Neuroimaging #ComputationalNeuroscience #Connectivity #MachineLearning #ML

#arxivfeed

"Learning low-dimensional dynamics from whole-brain data improves task capture"
https://arxiv.org/abs/2305.14369

#Neuroscience #ComputationalNeuroscience #MachineLearning #Neuroimaging #NeuralODE #DynamicalSystems #Cognition

#arxivfeed

"Learning low-dimensional dynamics from whole-brain data improves task capture"
https://arxiv.org/abs/2305.14369

#Neuroscience #ComputationalNeuroscience #MachineLearning #Neuroimaging #NeuralODE #DynamicalSystems #Cognition

#arxivfeed

"Learning low-dimensional dynamics from whole-brain data improves task capture"
https://arxiv.org/abs/2305.14369

#Neuroscience #ComputationalNeuroscience #MachineLearning #Neuroimaging #NeuralODE #DynamicalSystems #Cognition

#arxivfeed

"Learning low-dimensional dynamics from whole-brain data improves task capture"
https://arxiv.org/abs/2305.14369

#Neuroscience #ComputationalNeuroscience #MachineLearning #Neuroimaging #NeuralODE #DynamicalSystems #Cognition

#arxivfeed

"Learning low-dimensional dynamics from whole-brain data improves task capture"
https://arxiv.org/abs/2305.14369

#Neuroscience #ComputationalNeuroscience #MachineLearning #Neuroimaging #NeuralODE #DynamicalSystems #Cognition

After a long break, new #arxivfeed

"Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation"
https://arxiv.org/abs/2305.15208

#BayesianInference #MachineLearning #Modelling #SBI #ComputationalNeuroscience

After a long break, new #arxivfeed

"Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation"
https://arxiv.org/abs/2305.15208

#BayesianInference #MachineLearning #Modelling #SBI #ComputationalNeuroscience

After a long break, new #arxivfeed

"Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation"
https://arxiv.org/abs/2305.15208

#BayesianInference #MachineLearning #Modelling #SBI #ComputationalNeuroscience

After a long break, new #arxivfeed

"Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation"
https://arxiv.org/abs/2305.15208

#BayesianInference #MachineLearning #Modelling #SBI #ComputationalNeuroscience

After a long break, new #arxivfeed

"Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation"
https://arxiv.org/abs/2305.15208

#BayesianInference #MachineLearning #Modelling #SBI #ComputationalNeuroscience

Katharina Wilmes (https://katharinawilmes.com) will give a talk on "Uncertainty-modulated prediction errors in cortical microcircuits" today at #Venusberg #Bonn :

⏰ May 19, 2023, 12:00 pm
🌎 https://bonn-neuroscience.de/seminars/uncertainty-modulated-prediction-errors-in-cortical-microcircuits/

#compneuro #computationalneuroscience #plasticity

#arxivfeed

"Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias"
https://www.biorxiv.org/content/10.1101/2023.03.10.532087v1

#Neuroscience #ComputationalNeuroscience #DynamicalModels #DynamicalSystems #Neuroimaging #neurodegeneration #TheVirtualBrain

#arxivfeed

"Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias"
https://www.biorxiv.org/content/10.1101/2023.03.10.532087v1

#Neuroscience #ComputationalNeuroscience #DynamicalModels #DynamicalSystems #Neuroimaging #neurodegeneration #TheVirtualBrain

#arxivfeed

"Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias"
https://www.biorxiv.org/content/10.1101/2023.03.10.532087v1

#Neuroscience #ComputationalNeuroscience #DynamicalModels #DynamicalSystems #Neuroimaging #neurodegeneration #TheVirtualBrain

#arxivfeed

"Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias"
https://www.biorxiv.org/content/10.1101/2023.03.10.532087v1

#Neuroscience #ComputationalNeuroscience #DynamicalModels #DynamicalSystems #Neuroimaging #neurodegeneration #TheVirtualBrain

#arxivfeed

"Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias"
https://www.biorxiv.org/content/10.1101/2023.03.10.532087v1

#Neuroscience #ComputationalNeuroscience #DynamicalModels #DynamicalSystems #Neuroimaging #neurodegeneration #TheVirtualBrain

#Arxivfeed :

"Simulation-based inference for efficient identification of generative models in connectomics"
https://www.biorxiv.org/content/10.1101/2023.01.31.526269v1

#Neuroscience #ComputationalNeuroscience #MachineLearning #SimulationBasedInference #Inference #Connectome #Connectivity

#Arxivfeed :

"Simulation-based inference for efficient identification of generative models in connectomics"
https://www.biorxiv.org/content/10.1101/2023.01.31.526269v1

#Neuroscience #ComputationalNeuroscience #MachineLearning #SimulationBasedInference #Inference #Connectome #Connectivity

#Arxivfeed :

"Simulation-based inference for efficient identification of generative models in connectomics"
https://www.biorxiv.org/content/10.1101/2023.01.31.526269v1

#Neuroscience #ComputationalNeuroscience #MachineLearning #SimulationBasedInference #Inference #Connectome #Connectivity

#Arxivfeed :

"Simulation-based inference for efficient identification of generative models in connectomics"
https://www.biorxiv.org/content/10.1101/2023.01.31.526269v1

#Neuroscience #ComputationalNeuroscience #MachineLearning #SimulationBasedInference #Inference #Connectome #Connectivity

#Arxivfeed :

"Simulation-based inference for efficient identification of generative models in connectomics"
https://www.biorxiv.org/content/10.1101/2023.01.31.526269v1

#Neuroscience #ComputationalNeuroscience #MachineLearning #SimulationBasedInference #Inference #Connectome #Connectivity

#arxivfeed :

"Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks"
https://www.biorxiv.org/content/10.1101/2023.01.05.522909v1

#Neuroscience #Neuro #ComputationalNeuroscience #Vision #MachineLearning #DeepLearning #ConvolutionalNeuralNetworks