Tannishtha Das
@tannishthad.bsky.social
Reposted by Tannishtha Das
Fully neural mechanisms for structured generalization and rapid learning identified using a meta-learning (“learning-to-learn”) approach to model relational learning (e.g. if A > B and B > C, then A > C) 🧪🧠
www.nature.com/articles/s41...
www.nature.com/articles/s41...
Neural mechanisms of relational learning and fast knowledge reassembly in plastic neural networks - Nature Neuroscience
Using a metalearning approach to model relational learning, Miconi and Kay show neural mechanisms for structured generalization and rapid learning.
www.nature.com
January 15, 2025 at 2:20 PM
Fully neural mechanisms for structured generalization and rapid learning identified using a meta-learning (“learning-to-learn”) approach to model relational learning (e.g. if A > B and B > C, then A > C) 🧪🧠
www.nature.com/articles/s41...
www.nature.com/articles/s41...
Reposted by Tannishtha Das
Those who have used biophysically realistic conductance-based neuronal models may have faced difficulties in getting their model’s membrane potential baseline during action potential trains to match experimental observations. Our computational study addresses this problem: 🧵1/n
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Mechanisms and implications of high depolarization baseline offsets in conductance-based neuronal models
Somatic step-current injection is commonly used to characterize the electrophysiological properties of neurons. Many neuronal types show a large depolarization baseline offset (DBLO), which is defined...
www.biorxiv.org
November 24, 2024 at 1:42 PM
Those who have used biophysically realistic conductance-based neuronal models may have faced difficulties in getting their model’s membrane potential baseline during action potential trains to match experimental observations. Our computational study addresses this problem: 🧵1/n
🧠💻
🧠💻