Neuroscience
Dive into the latest discoveries, foundational theories, and cutting-edge techniques that unravel the mysteries of the brain and nervous system.
New Insights into PTSD: Astrocytic GABA Dysregulation and a Novel Therapeutic Target
This research reveals astrocytic GABA dysregulation in the prefrontal cortex (PFC) as a key mechanism driving PTSD pathophysiology and proposes monoamine oxidase B (MAOB) inhibition as a promising new treatment strategy. Combining human clinical data, postmortem brain analyses, preclinical mouse models, and pharmacological testing, the study builds a strong translational case for the novel MAOB
Cypin Regulates Synaptic Content via K63-linked Polyubiquitination: A New Layer in Synaptic Plasticity
A new study reveals a novel function for the cytosolic protein Cypin in regulating synaptic composition and function via K63-linked polyubiquitination—an underexplored post-translational modification in neurons. The work answers key questions about how specific ubiquitin chains modulate synaptic targeting and turnover, offering insights with therapeutic potential. Proposed model: Cypin modulates synaptic signaling by regulating proteasome
RibbonFold: Generating Amyloid Polymorph Landscapes with AI
Amyloid fibrils are central to neurodegenerative diseases like Alzheimer’s and Parkinson’s. However, predicting their structures is notoriously difficult because, unlike most proteins that fold into a single stable form, amyloids exhibit extreme polymorphism: a single sequence can fold into multiple distinct, stable structures depending on conditions. Existing tools like AlphaFold2, designed for monomeric, soluble proteins,
Rethinking Decision-Making: Tiny Recurrent Neural Networks for Interpretable Cognitive Modeling
A recent breakthrough in computational neuroscience introduces a novel framework that uses tiny recurrent neural networks (RNNs) to model the cognitive processes behind biological decision-making. The study challenges traditional approaches like Bayesian inference and reinforcement learning (RL) by offering a more flexible yet interpretable alternative. Instead of relying on complex, high-dimensional models, this work demonstrates
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