Kv4.2-Positive Domains on Dendrites in the Mouse Medial Geniculate Body Receive Ascending Excitatory and Inhibitory Inputs Preferentially From the Inferior Colliculus

The medial geniculate body (MGB) is the thalamic center of the auditory lemniscal pathway. The ventral division of MGB (MGV) receives excitatory and inhibitory inputs from the inferior colliculus (IC). MGV is involved in auditory attention by processing descending excitatory and inhibitory inputs from the auditory cortex (AC) and reticular thalamic nucleus (RTN), respectively. However, detailed mechanisms of the integration of different inputs in a single MGV neuron remain unclear. Kv4.2 is one of the isoforms of the Shal-related subfamily of potassium voltage-gated channels that are expressed in MGB. Since potassium channel is important for shaping synaptic current and spike waveforms, subcellular distribution of Kv4.2 is likely important for integration of various inputs. Here, we aimed to examine the detailed distribution of Kv4.2, in MGV neurons to understand its specific role in auditory attention. We found that Kv4.2 mRNA was expressed in most MGV neurons. At the protein level, Kv4.2-immunopositive patches were sparsely distributed in both the dendrites and the soma of neurons. The postsynaptic distribution of Kv4.2 protein was confirmed using electron microscopy (EM). The frequency of contact with Kv4.2-immunopositive puncta was higher in vesicular glutamate transporter 2 (VGluT2)-positive excitatory axon terminals, which are supposed to be extending from the IC, than in VGluT1-immunopositive terminals, which are expected to be originating from the AC. VGluT2-immunopositive terminals were significantly larger than VGluT1-immunopositive terminals. Furthermore, EM showed that the terminals forming asymmetric synapses with Kv4.2-immunopositive MGV dendritic domains were significantly larger than those forming synapses with Kv4.2-negative MGV dendritic domains. In inhibitory axons either from the IC or from the RTN, the frequency of terminals that were in contact with Kv4.2-positive puncta was higher in IC than in RTN. In summary, our study demonstrated that the Kv4.2-immunopositive domains of the MGV dendrites received excitatory and inhibitory ascending auditory inputs preferentially from the IC, and not from the RTN or cortex. Our findings imply that time course of synaptic current and spike waveforms elicited by IC inputs is modified in the Kv4.2 domains.

Discrimination between Inhibitory and Excitatory Neurons of Mouse Hippocampus Based on the Shape of Extracellular Spike Waveforms

Purpose: Inhibitory and excitatory neurons play an essential role in brain function, and we aim to introduce an automatic method to discriminate these two populations based on features of the shape of their spikes. Consequently, we will explain the spike extraction from raw data of a single shank electrode and determine the best features of spike waveforms for the classification of neurons. It is noteworthy that, to the best of our knowledge, classification of inhibitory and excitatory neurons using the shape features extracted from their spike waveforms has not been done before. Materials and Methods: In this paper, we use a dataset of mouse hippocampus neurons in which the neuron types (inhibitory or excitatory) have been verified optogenetically. For the classification of mouse hippocampus neurons, we extracted eight shape features of their spike waveforms in addition to their firing rates and used three types of classifiers: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to analyze the discriminatory power of features based on the accuracy of the classifications. Results: We showed that Spike asymmetry, Peak-to-trough ratio, Recovery slope, and Duration between peaks were four shape features of spike waveforms participated in the optimum feature subsets that resulted in maximum classification accuracy. Moreover, the SVM classifier with RBF kernel resulted in maximum accuracy of %96.91 ± %13.03 and was identified as the best classifier. Conclusion: In this study, we found that shape features of spike waveforms can accurately classify inhibitory and excitatory neurons of mouse hippocampus. Also, we found an optimum subset of shape features of spike waveforms that resulted in better classification performance than previously proposed subsets of features used for clustering of neurons. Our findings open a promising way toward a functional classification of neurons automatically.

Modeling of high frequency high voltage of waveforms on life of enamel insulation

<p>In recent years it has been observed that insulation failure in electrical motors is caused by adjustable speed drives fed by power electronic converters. These converters produce impulse waveforms having a high slew rate generated by the high switching frequency of IGBTs. This paper focuses on high switching frequency stress in low voltage electrical motors for adjustable speeds. To examine the motor winding insulation under such stress twisted-pair samples were developed from enameled wires. A single-coated polyester of enamel with a thickness of 40 microns is used for this work. High-frequencies, high voltages of Square, and Square-rising, Square-spike waveforms of 10-30 kHz are used here. The test results show that the insulation fails earlier for the Square waveform compared to the Square-spike and Square-rising waveforms. In a nutshell, there is an analysis of PD formation in the insulation system at a higher switching frequency is analyzed. Electric field distributions between twisted pairs with various air gaps of the insulation system stressed by the Square and Square-rising waveforms up to 30 kHz are modeled using COMSOL software.</p>

Dataset of cortical activity recorded with high spatial resolution from anesthetized rats

Publicly available neural recordings obtained with high spatial resolution are scarce. Here, we present an electrophysiological dataset recorded from the neocortex of twenty rats anesthetized with ketamine/xylazine. The wideband, spontaneous recordings were acquired with a single-shank silicon-based probe having 128 densely-packed recording sites arranged in a 32 × 4 array. The dataset contains the activity of a total of 7126 sorted single units extracted from all layers of the cortex. Here, we share raw neural recordings, as well as spike times, extracellular spike waveforms and several properties of units packaged in a standardized electrophysiological data format. For technical validation of our dataset, we provide the distributions of derived single unit properties along with various spike sorting quality metrics. This large collection of in vivo data enables the investigation of the high-resolution electrical footprint of cortical neurons which in turn may aid their electrophysiology-based classification. Furthermore, the dataset might be used to study the laminar-specific neuronal activity during slow oscillation, a brain rhythm strongly involved in neural mechanisms underlying memory consolidation and sleep.

Online spike sorting via deep contractive autoencoder

Spike sorting – the process of separating spikes from different neurons – is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron’s activity from background electrical noise based on the shapes of the waveforms (WFs) obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (DCAE) architecture. DCAEs are deep neural networks that can learn a latent state representation of their inputs. The main advantage of DCAE approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our DCAE-based online spike sorting algorithm achieves over 90% accuracy at sorting previously-unseen spike waveforms. Moreover, our approach produces superior results compared to several state-of-the-art offline spike-sorting procedures.

Interpretation of dust impact signals detected by Cassini at Saturn

&lt;p&gt;The Cassini spacecraft spent more than 13 years in the dusty environment of Saturn. During this long period of investigations of the Saturn magnetosphere, the RPWS (Radio Plasma Wave Science) instrument recorded more than half a million spiky signatures. However, not all of them can be interpreted as dust impact signals because plasma structures like solitary waves can result in similar pulses.&lt;/p&gt;&lt;p&gt;We select the registered spike waveforms recorded by both dipole and monopole configurations of electric field antennas operated in 10 kHz or 80 kHz sampling rates at the distance of 0.2 Rs around the rings mid-plane. These waveforms were corrected using Cassini WBR (Wide Band Receiver) transfer function to obtain the correct shape of the signal. The signal polarity, amplitude, and timescales of different parts of the waveforms were quantitatively inspected according to the spacecraft potential, the density of the ambient plasma, the intensity of the Saturn&amp;#8217;s magnetic field, and its orientation with respect to the spacecraft. The magnetic field orientation was also used for distinguishing between signals resulting from dust impacts and signals produced by solitary waves misinterpreted as dust impact signals.&lt;/p&gt;&lt;p&gt;The preliminary results of our study indicate similarities with previous laboratory studies of dust impact waveforms on the reduced model of Cassini bombarded with submicron-sized iron grains in external magnetic fields at the LASP facility of the University of Colorado. The polarity of the signals changes in accordance with a polarity of the spacecraft potential and pre-spike signals are also observed. The core of the paper is devoted to the relation between characteristics of dust impact signals and local plasma parameters and magnetic field intensity at the radial distance from 2 Rs to 60 Rs from Saturn surface.&lt;/p&gt;

Wireless logging of extracellular neuronal activity in the telencephalon of free-swimming salmonids

Background Salmonids return to the river where they were born in a phenomenon known as mother-river migration. The underpinning of migration has been extensively examined, particularly regarding the behavioral correlations of external environmental cues such as the scent of the mother-river and geomagnetic compass. However, neuronal underpinning remains elusive, as there have been no biologging techniques suited to monitor neuronal activity in the brain of large free-swimming fish. In this study, we developed a wireless biologging system to record extracellular neuronal activity in the brains of free-swimming salmonids. Results Using this system, we recorded multiple neuronal activities from the telencephalon of trout swimming in a rectangular water tank. As proof of principle, we examined the activity statistics for extracellular spike waveforms and timing. We found cells firing maximally in response to a specific head direction, similar to the head direction cells found in the rodent brain. The results of our study suggest that the recorded signals originate from neurons. Conclusions We anticipate that our biologging system will facilitate a more detailed investigation into the neural underpinning of fish movement using internally generated information, including responses to external cues.

Distinct Spiking Patterns of Excitatory and Inhibitory Neurons and LFP Oscillations in Prefrontal Cortex During Sensory Discrimination

Prefrontal cortex (PFC) are broadly linked to various aspects of behavior. During sensory discrimination, PFC neurons can encode a range of task related information, including the identity of sensory stimuli and related behavioral outcome. However, it remains largely unclear how different neuron subtypes and local field potential (LFP) oscillation features in the mouse PFC are modulated during sensory discrimination. To understand how excitatory and inhibitory PFC neurons are selectively engaged during sensory discrimination and how their activity relates to LFP oscillations, we used tetrode recordings to probe well-isolated individual neurons, and LFP oscillations, in mice performing a three-choice auditory discrimination task. We found that a majority of PFC neurons, 78% of the 711 recorded individual neurons, exhibited sensory discrimination related responses that are context and task dependent. Using spike waveforms, we classified these responsive neurons into putative excitatory neurons with broad waveforms or putative inhibitory neurons with narrow waveforms, and found that both neuron subtypes were transiently modulated, with individual neurons’ responses peaking throughout the entire duration of the trial. While the number of responsive excitatory neurons remain largely constant throughout the trial, an increasing fraction of inhibitory neurons were gradually recruited as the trial progressed. Further examination of the coherence between individual neurons and LFPs revealed that inhibitory neurons exhibit higher spike-field coherence with LFP oscillations than excitatory neurons during all aspects of the trial and across multiple frequency bands. Together, our results demonstrate that PFC excitatory neurons are continuously engaged during sensory discrimination, whereas PFC inhibitory neurons are increasingly recruited as the trial progresses and preferentially coordinated with LFP oscillations. These results demonstrate increasing involvement of inhibitory neurons in shaping the overall PFC dynamics toward the completion of the sensory discrimination task.