#computationalneuroscience — Public Fediverse posts on home.social

Dan Goodman @[email protected] · 2026-09-11 · 15:15 UTC

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

#neuroscience #computationalneuroscience

Dan Goodman @[email protected] · 2026-09-11 · 15:15 UTC

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

#neuroscience #computationalneuroscience

Dan Goodman @[email protected] · 2026-09-11 · 15:15 UTC

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

#neuroscience #computationalneuroscience

Dan Goodman @[email protected] · 2026-09-11 · 15:15 UTC

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

#computationalneuroscience #neuroscience

Dan Goodman @[email protected] · 2026-09-11 · 15:15 UTC

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

#neuroscience #computationalneuroscience

Philo Sophies @[email protected] · 2026-06-26 · 13:22 UTC

🧠🤖 AIM for the #Brain – How #AI and #Neuroscience Come Together

For more information: https://indico.uni-muenster.de/event/4011/

or: https://philosophies.de/index.php/2026/05/18/aim-for-the-brain/

#AIMfortheBrain #ArtificialIntelligence #Neuroscience #MachineLearning #ComputationalNeuroscience #DataScience #DeepLearning #NeuralNetworks #Consciousness #EEG #MEG #Neuroscience #CDSC #UniversityofMünster #BrainResearch #CognitiveScience #Interdisciplinarity #AI #Research #PredictiveProcessing #Neurotechnology #Science

#brain #ai #neuroscience #aimforthebrain #artificialintelligence #machinelearning

Philo Sophies @[email protected] · 2026-06-07 · 08:42 UTC

🧠🤖 AIM for the #Brain – How #AI and #Neuroscience Come Together

For more information: https://indico.uni-muenster.de/event/4011/

or: https://philosophies.de/index.php/2026/05/18/aim-for-the-brain/

#AIMfortheBrain #ArtificialIntelligence #Neuroscience #MachineLearning #ComputationalNeuroscience #DataScience #DeepLearning #NeuralNetworks #Consciousness #EEG #MEG #Neuroscience #CDSC #UniversityofMünster #BrainResearch #CognitiveScience #Interdisciplinarity #AI #Research #PredictiveProcessing #Neurotechnology #Science

#brain #ai #neuroscience #aimforthebrain #artificialintelligence #machinelearning

Ranjith Jaganathan @[email protected] · 2025-11-20 · 06:18 UTC

I’m excited to share that our article has been published: “Brain Topology Disruption in Early-Onset Dementia: Review of Current Findings and the Need for Network Resilience-Focused Models” (http://dx.doi.org/10.1002/brb3.70903)

In this review, we highlight several important insights:

- A summary of how early‐onset forms of dementia (including Alzheimer’s disease, frontotemporal dementia (FTD), and behavioral variant FTD) show disruption in brain network topology (both structural and functional) rather than purely focal pathology.

- Evidence that brain networks lose their optimal organisational properties (e.g., balance of segregation and integration) in early‐onset dementia, reflecting decline in network resilience. For example, previous work has shown disrupted segregation/integration in large‐scale brain networks in Alzheimer’s/MCI.

- The concept of network resilience as a key lens: rather than only asking “where damage occurs”, the paper argues we should ask “how the network topology fails to compensate, reorganise or maintain function under pathology”. This shifts the view to resilience‐focused models.

- Review of methodological findings: how graph‐theoretic metrics (clustering coefficient, global/local efficiency, modularity, assortativity, small‐worldness) are being applied to neuroimaging and electrophysiology in early dementia.

- Gaps and opportunities: the need for models that integrate network resilience, longitudinal data, multimodal connectivity (structural + functional + electrophysiological) and early‐onset cohorts; and the translational potential for biomarkers and interventions that support network integrity rather than just reduce pathology.

I believe this work contributes to bridging neuroscience, network theory, and clinical neurology, and invites discussion on how we can design interventions that strengthen brain network resilience in dementia.

Thanks to my co-authors (Hema Nawani, Sredha Sunil) and reviewers, and a huge thank you to our professor Veeky Baths for his guidance and support throughout this work.

If you’re working in cognitive neuroscience, network approaches to brain disorders, early‐onset dementia, connectomics or translational neurology, let’s collaborate to make a real impact.

#Neuroscience #BrainNetworks #Dementia #EarlyOnsetDementia #Neurodegeneration #NetworkResilience #ClinicalNeuroscience #GraphTheory #NetworkNeuroscience #ComputationalNeuroscience

#neuroscience #brainnetworks #dementia #earlyonsetdementia #neurodegeneration #networkresilience

Alessandro Torcini @[email protected] · 2024-12-18 · 20:48 UTC

Neuronal dynamics can be #balanced
without the need of strong external currents, as usually assumed, here with A. Politi we explain how this can work thanks to #short term #depression (open access article 2024)

#neuroscience #computationalneuroscience #balance #neuralnetwork #oscillators

https://pubs.aip.org/aip/cha/article/34/4/041102/3283544/A-robust-balancing-mechanism-for-spiking-neural

#balanced #short #depression #neuroscience #computationalneuroscience #balance