C3N
@c3neuro.bsky.social
Led by PI @stefanieliebe.bsky.social : We use #AI tools to analyze neural activity and behavior in humans, bridging basic research on cognition and clinical applications, with a focus on #epilepsy. Based in Tübingen, Germany.
https://liebelab.github.io
https://liebelab.github.io
✨ Come meet us at the poster sessions — we’re thrilled to exchange ideas, spark new collaborations, and celebrate science together! ✨
September 30, 2025 at 2:21 PM
✨ Come meet us at the poster sessions — we’re thrilled to exchange ideas, spark new collaborations, and celebrate science together! ✨
📅 Wednesday 01.10.2025 | 12:30 – 14:00
🔹 PIII-9: @raeesmk.bsky.social presents
“Modeling Spatial Hearing with Cochlear Implants Using Deep Neural Networks”
🤝 Joint work with @jakhmack.bsky.social
🔹 PIII-9: @raeesmk.bsky.social presents
“Modeling Spatial Hearing with Cochlear Implants Using Deep Neural Networks”
🤝 Joint work with @jakhmack.bsky.social
September 30, 2025 at 2:21 PM
📅 Wednesday 01.10.2025 | 12:30 – 14:00
🔹 PIII-9: @raeesmk.bsky.social presents
“Modeling Spatial Hearing with Cochlear Implants Using Deep Neural Networks”
🤝 Joint work with @jakhmack.bsky.social
🔹 PIII-9: @raeesmk.bsky.social presents
“Modeling Spatial Hearing with Cochlear Implants Using Deep Neural Networks”
🤝 Joint work with @jakhmack.bsky.social
📅 Wednesday 01.10.2025 | 12:30 – 14:00
🔹 PIII-7: @meghaldani.bsky.social presents
“SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy” 🧩🤖
🔹 PIII-7: @meghaldani.bsky.social presents
“SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy” 🧩🤖
September 30, 2025 at 2:20 PM
📅 Wednesday 01.10.2025 | 12:30 – 14:00
🔹 PIII-7: @meghaldani.bsky.social presents
“SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy” 🧩🤖
🔹 PIII-7: @meghaldani.bsky.social presents
“SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy” 🧩🤖
📅 Tuesday 30.09.2025 | 18:00 – 19:30
🔹 PII-14: @muthujeyanthi.bsky.social presents
“Gamma–Theta–Spike Interactions Coordinate Sequence Representation in Human MTL”
🤝 In collaboration with Florian Mormann @unibonn.bsky.social
🔹 PII-14: @muthujeyanthi.bsky.social presents
“Gamma–Theta–Spike Interactions Coordinate Sequence Representation in Human MTL”
🤝 In collaboration with Florian Mormann @unibonn.bsky.social
September 30, 2025 at 2:20 PM
📅 Tuesday 30.09.2025 | 18:00 – 19:30
🔹 PII-14: @muthujeyanthi.bsky.social presents
“Gamma–Theta–Spike Interactions Coordinate Sequence Representation in Human MTL”
🤝 In collaboration with Florian Mormann @unibonn.bsky.social
🔹 PII-14: @muthujeyanthi.bsky.social presents
“Gamma–Theta–Spike Interactions Coordinate Sequence Representation in Human MTL”
🤝 In collaboration with Florian Mormann @unibonn.bsky.social
(6/6)
We thank @dfg.de @ml4science.bsky.social @brainloops.bsky.social @tuebingen-ai.bsky.social for the support!
We thank @dfg.de @ml4science.bsky.social @brainloops.bsky.social @tuebingen-ai.bsky.social for the support!
March 24, 2025 at 1:07 PM
(6/6)
We thank @dfg.de @ml4science.bsky.social @brainloops.bsky.social @tuebingen-ai.bsky.social for the support!
We thank @dfg.de @ml4science.bsky.social @brainloops.bsky.social @tuebingen-ai.bsky.social for the support!
(5/6)
In summary: Phase of firing reflects temporal order of items through a revised mechanism linking oscillatory phase, stimulus timing, and memory - and not through direct, order-preserving encoding
In summary: Phase of firing reflects temporal order of items through a revised mechanism linking oscillatory phase, stimulus timing, and memory - and not through direct, order-preserving encoding
March 24, 2025 at 1:05 PM
(5/6)
In summary: Phase of firing reflects temporal order of items through a revised mechanism linking oscillatory phase, stimulus timing, and memory - and not through direct, order-preserving encoding
In summary: Phase of firing reflects temporal order of items through a revised mechanism linking oscillatory phase, stimulus timing, and memory - and not through direct, order-preserving encoding
(4/6)
Analysis of the RNN nd comparison with human recordings suggests a more general idea:
Phase of firing is shaped by an interaction of the dynamics of stimulus presentation and oscillation frequency.
Analysis of the RNN nd comparison with human recordings suggests a more general idea:
Phase of firing is shaped by an interaction of the dynamics of stimulus presentation and oscillation frequency.
March 24, 2025 at 1:05 PM
(4/6)
Analysis of the RNN nd comparison with human recordings suggests a more general idea:
Phase of firing is shaped by an interaction of the dynamics of stimulus presentation and oscillation frequency.
Analysis of the RNN nd comparison with human recordings suggests a more general idea:
Phase of firing is shaped by an interaction of the dynamics of stimulus presentation and oscillation frequency.
(3/6)
We asked: How would a recurrent neural network solve the task?
In a similar way: it also showed phase-structured firing that didn’t match item order, just like the human brain data.
We asked: How would a recurrent neural network solve the task?
In a similar way: it also showed phase-structured firing that didn’t match item order, just like the human brain data.
March 24, 2025 at 1:05 PM
(3/6)
We asked: How would a recurrent neural network solve the task?
In a similar way: it also showed phase-structured firing that didn’t match item order, just like the human brain data.
We asked: How would a recurrent neural network solve the task?
In a similar way: it also showed phase-structured firing that didn’t match item order, just like the human brain data.
(2/6)
Despite observing strong theta oscillations and phase-specific firing for items, we found no evidence that neurons fire in the same sequence as items were presented. (A direct contradiction to a classical prior theory)
Despite observing strong theta oscillations and phase-specific firing for items, we found no evidence that neurons fire in the same sequence as items were presented. (A direct contradiction to a classical prior theory)
March 24, 2025 at 1:04 PM
(2/6)
Despite observing strong theta oscillations and phase-specific firing for items, we found no evidence that neurons fire in the same sequence as items were presented. (A direct contradiction to a classical prior theory)
Despite observing strong theta oscillations and phase-specific firing for items, we found no evidence that neurons fire in the same sequence as items were presented. (A direct contradiction to a classical prior theory)
(1/6)
With @stefanieliebe.bsky.social, J. Niediek, @matthijspals.bsky.social , @humansingleneuron.bsky.social & @mackelab.bsky.social.
We recorded from 1,420 neurons and 921 LFP channels in human MTL in epilepsy patients during sequential working memory.
With @stefanieliebe.bsky.social, J. Niediek, @matthijspals.bsky.social , @humansingleneuron.bsky.social & @mackelab.bsky.social.
We recorded from 1,420 neurons and 921 LFP channels in human MTL in epilepsy patients during sequential working memory.
March 24, 2025 at 1:04 PM
(1/6)
With @stefanieliebe.bsky.social, J. Niediek, @matthijspals.bsky.social , @humansingleneuron.bsky.social & @mackelab.bsky.social.
We recorded from 1,420 neurons and 921 LFP channels in human MTL in epilepsy patients during sequential working memory.
With @stefanieliebe.bsky.social, J. Niediek, @matthijspals.bsky.social , @humansingleneuron.bsky.social & @mackelab.bsky.social.
We recorded from 1,420 neurons and 921 LFP channels in human MTL in epilepsy patients during sequential working memory.