Stimulus type shapes the topology of cellular functional networks in mouse visual cortex.

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex.

Stimulus-dependent functional network topology in mouse visual cortex.

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns.

Multi-element imaging of urinary stones by LA-ICP-MS with a homogeneous co-precipitation CaCO-matrix calibration standard.

Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) studies on trace element concentration and their spatial distribution in CaCO-matrix urinary stones are important but powerfully rely on matrix-matched external calibration. In this work, CaCO precipitate CaOx-1 which was doped with Mg, Cr, Mn, Fe, Co, Cu, Zn, and Sr was prepared by the homogeneous co-precipitation method. It had a homogeneous distribution of major (RSD of 0.

Predictability of real temporal networks.

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random.