Topic: glia cells

Recurrent Neural Networks (RNNs) are a popular computational tool in neuroscience. Their architecture is inspired by biology, namely the abundance of lateral connections in cortex. Because they are universal approximators, they can be trained to match behavior in common neuroscience tasks, and their hidden state can be compared to neural recordings. Despite the biological inspiration, many features of real neurons are abstracted away (e.g. morphology, neuromodulators, excitability dynamics, short term plasticity). While many of these properties were included as add-ons to standard RNN models,

The brain extracellular matrix (ECM) is a lattice-like structure that occupies the intercellular space in the central nervous system (CNS). The ECM constituents are extremely long-lived and are widely believed to stabilize neurons and synapses in the brain. They are thought to be renewed only rarely, dependent on neuronal activity, by the activation of matrix metalloproteinases (MMPs) that cleave the existing ECM components, followed by de novo ECM protein synthesis. However, various studies have demonstrated that synapses change their structural organization on minutes to hours time scale, ca