Topic: gap junctions

The seminar will present the origin of the hypothesis of electrical coupling between proximal axons, physiological and immunostaining evidence for the presence of the requisite gap junctions and will explain how electrical coupling could account for very fast network oscillations at >80 hz.

Axons in the optic nerve are arranged in bundles and conducting action potential with resistance related to their membrane. Optic nerve axons do not form absolutely independent conductive channels. They are directly coupled by gap junctions formed in majority by neuronal Cx45. Coupling of axons, except known transpassing functions, allows to reduce axonal membrane resistance of optic nerve and accelerates transduction of visual signal. This novel finding have substantial implications for understanding of the pathogenesis of various optic neuropathies and identifies a new potential target for a

Interneurons, ubiquitous in the central nervous system, form networks connected through both inhibitory chemical synapses and gap junctions. These networks are essential for regulating neuronal activity notably by regulating temporally patterned dynamic states. We aim to understand the mechanisms that allow for synchronization to arise in networks of interneurons with both chemical synapses and electrical gap junctions (Panel 1). To this end, we use the exact mean-field reduction, often referred to as the “neural mass model” in the literature (Montbrio et al, PRX, 2015). We first analyse a sin

Advancements in machine learning have drastically improved our ability to fit biophysical models of neural systems. We present here a fully-differentiable, biophysical model of the mouse retina that is primed for gradient-based optimization. Our model is based on the Jaxley software library (Deistler et al. 2024, Bernstein Conference) and JAX machine learning framework. The model consists of a network of Hodgkin-Huxley style neurons, realistic synapse dynamics, an experimentally-informed organization of cells in 3D space, and spatially constrained connectivity. We further implemented a wide v

Reinstating details of previously seen stimuli given a recall cue is a common cognitive function. Studies suggest that such recollection involves hippocampus-mediated top-down reactivation of neural ensembles active during encoding, yet the neural mechanisms of such generative retrieval remain unknown. We formulate a biologically plausible hypothesis where transient cholinergic modulation, known to enhance backpropagated action potentials and trigger recall, initiates backward signals from sparse neurons discriminatively representing the cue to cortical areas encoding the trace. These signals