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. To address this question, we recorded spiking activity from >500 single GC neurons in male and female mice permitted to freely lick to receive four liquid gustatory stimuli and water. We developed a set of data analysis tools to determine the ability of GC neurons to discriminate gustatory information and then to quantify the degree to which this information exists in the spike rate versus the spike timing or phase relative to licks. These tools include machine learning algorithms for classification of spike trains and methods from geometric shape and functional data analysis. Our results show that while GC neurons primarily encode taste information using a rate code, the timing of spikes is also an important factor in taste discrimination. A further finding is that taste discrimination using spike timing is improved when the timing of licks is considered in the analysis. That is, the interlick phase of spiking provides more information than the absolute spike timing itself. Overall, our analysis demonstrates that the ability of GC neurons to distinguish among tastes is best when spike rate and timing is interpreted relative to the timing of licks.SIGNIFICANCE STATEMENT Neurons represent information from the outside world via changes in their number of action potentials (spikes) over time. This study examines how neurons in the mouse gustatory cortex (GC) encode taste information when gustatory stimuli are experienced through the active process of licking. We use electrophysiological recordings and data analysis tools to evaluate the ability of GC neurons to distinguish tastants and then to quantify the degree to which this information exists in the spike rate versus the spike timing relative to licks. We show that the neuron's ability to distinguish between tastes is higher when spike rate and timing are interpreted relative to the timing of licks, indicating that the lick cycle is a key factor for taste processing.

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. We develop a mathematical representation, elastic Riemannian metrics, and associated tools for shape graphs. Specifically, we derive tools for shape graph registration, geodesics, statistical summaries, shape modeling, and shape synthesis. Geodesics are convenient for visualizing optimal deformations, and PCA helps in dimension reduction and statistical modeling. One key challenge in comparing shape graphs with vastly different complexities (in number of nodes and edges). This paper introduces a novel multi-scale representation to handle this challenge. Using the notions of (1) "effective resistance" to cluster nodes and (2) elastic shape averaging of edge curves, it reduces graph complexity while retaining overall structures. This allows shape comparisons by bringing graphs to similar complexities. We demonstrate these ideas on 2D RBV networks from the STARE and DRIVE databases and 3D neurons from the NeuroMorpho database.

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. In the present study we sought to further elucidate the molecular mechanisms by which ultrasound regulates neuronal excitability and synaptic function. OBJECTIVES: To determine the effect of ultrasound stimulation on voltage-gated ion channel function and synaptic plasticity. METHODS: Primary rat cortical neurons were exposed to a 40 s, 200 kHz pulsed ultrasound stimulus or sham-stimulus. Whole-cell patch clamp electrophysiology, quantitative proteomics and high-resolution confocal microscopy were employed to determine the effects of ultrasound stimulation on molecular regulators of neuronal excitability and synaptic function. RESULTS: We find that ultrasound exposure elicits sustained but reversible increases in whole-cell potassium currents. In addition, we find that ultrasound exposure activates synaptic signalling cascades that result in marked increases in excitatory synaptic transmission. Finally, we demonstrate the requirement of ionotropic glutamate receptor (AMPAR/NMDAR) activation for ultrasound-induced modulation of neuronal potassium currents. CONCLUSION: These results suggest specific patterns of pulsed ultrasound can induce contemporaneous enhancement of both neuronal excitability and synaptic function, with implications for the application of FUS in experimental and therapeutic settings. Further study is now required to deduce the precise molecular mechanisms through which these changes occur.

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. They are particularly useful due to the existence of simplifying coordinate transformations for particular parameter values, such as the well-known square-root velocity transform. In this paper, we extend the transformations appearing in the existing literature to a family of isometries, which take any elastic metric to the flat L 2 metric. We also extend the transforms to treat piecewise linear curves and demonstrate the existence of optimal matchings over the diffeomorphism group in this setting. We conclude the paper with multiple examples of shape geodesics for open and closed curves. We also show the benefits of our approach in a simple classification experiment.