Cortical Coding of Gustatory and Thermal Signals in Active Licking Mice.

Eating behaviors are influenced by the integration of gustatory, olfactory, and somatosensory signals, which all contribute to the perception of flavor. Although extensive research has explored the neural correlates of taste in the gustatory cortex (GC), less is known about its role in encoding thermal information. This study investigates the encoding of oral thermal and chemosensory signals by GC neurons compared to the oral somatosensory cortex.

Statistical Analysis of Complex Shape Graphs.

This paper provides developments in statistical shape analysis of shape graphs, and demonstrates them using such complex objects as Retinal Blood Vessel (RBV) networks and neurons. The shape graphs are represented by sets of nodes and edges (articulated curves) connecting some nodes. The goals are to utilize nodes (locations, connectivity) and edges (edge weights and shapes) to: (1) characterize shapes, (2) quantify shape differences, and (3) model statistical variability.

Ultrasound modulates neuronal potassium currents via ionotropic glutamate receptors.

Background: Focused ultrasound stimulation (FUS) has the potential to provide non-invasive neuromodulation of deep brain regions with unparalleled spatial precision. However, the cellular and molecular consequences of ultrasound stimulation on neurons remains poorly understood. We previously reported that ultrasound stimulation induces increases in neuronal excitability that persist for hours following stimulation in vitro.

Active Licking Shapes Cortical Taste Coding.

Neurons in the gustatory cortex (GC) represent taste through time-varying changes in their spiking activity. The predominant view is that the neural firing rate represents the sole unit of taste information. It is currently not known whether the phase of spikes relative to lick timing is used by GC neurons for taste encoding.

Simplifying Transforms for General Elastic Metrics on the Space of Plane Curves.

In the shape analysis approach to computer vision problems, one treats shapes as points in an infinite-dimensional Riemannian manifold, thereby facilitating algorithms for statistical calculations such as geodesic distance between shapes and averaging of a collection of shapes. The performance of these algorithms depends heavily on the choice of the Riemannian metric. In the setting of plane curve shapes, attention has largely been focused on a two-parameter family of first order Sobolev metrics, referred to as elastic metrics.