Topic: long-term synaptic plasticity

Hierarchical working memory refers to the human ability to group information on-the-fly into larger chunks, thereby overcoming the limited capacity of Working Memory (WM), as first suggested by George A. Miller in his 1956 paper. Although chunking has been explored experimentally in numerous studies since then, its neuronal underpinnings remain unknown to this day. In our work, we address this issue by proposing a novel Recurrent Neural Network (RNN) model that enables the spontaneous organization of memory into chunks within WM. Our model leverages specialized “chunking neurons” and long-term

Alzheimer’s disease (AD) is a chronic incurable neurodegenerative disorder characterized by progressive memory loss and cognitive dysfunctions. Brain function is governed by dynamic interactions between neurons and glial cells. Over the last decades it has been shown that calcium dynamics in astrocytes represent a fundamental signal that through gliotransmitter release regulates synaptic plasticity and behaviour. Here, by using cutting-edge techniques including 2-photon Ca2+ imaging, electrophysiology and behavioural memory tests, we present a longitudinal study in the PS2APP mouse model of AD

To comprehend the brain’s dynamic physiology, we must understand the mechanisms underlying neuronal excitability, crucial for neurons’ electrical activation during synaptic communication. Of particular interest is controlled potassium current leakage via two-pore domain potassium channels which finely modulate the intrinsic excitability of neurons. TRESK, the latest discovered channel of the K2P family, has a well-characterized pivotal role in nociception. Intriguingly, it also exhibits widespread expression throughout central nervous system, yet its function within the brain has remained unex