Neuronal Spike Shape

The neuronal spike sorting problem can be defined as follows see Fig. 3 a. Detection. Identifying the presence of spikes in the signal. b. The spike features mentioned above are sensitive to noise and intrinsic variations in spike shapes. To circumvent this issue, one can use alternative feature extraction methods. For example,

In this study, we introduce the Neuronal Spike Shapes NSS, a straightforward approach for analyzing the electrophysiological profiles of cells based on their Action Potential AP waveforms. The

To evaluate single-neuron spiking reliability, we analyzed patch-clamp recordings of neurons which had their synaptic inputs blocked bathed with 1 mM kynurenic acid and 0.1 mM picrotoxin and

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes NSS, a straightforward approach for the exploration of excitability states of neurons based

Spiking signal characterization the function t follows the spike when it overcomes the zero threshold. The shaded area represents the integral area of the spike that, analogous to the function, equals 1 a, with a being the sensitivity of the neuron.. This contribution is motivated by the actual intense scientific activity related to spiking signals and spiking neural networks

This repository contains the code of the Neuronal Spike Shapes NSS, a simple approach for analyzing the electrophysiological profiles of cells based on their Action Potential AP waveforms. The NSS method explores the heterogeneity of cell types and states by summarizing the AP waveform into a triangular representation complemented by a set of derived electrophysiological EP features.

Many investigators who make extracellular recordings from populations of cortical neurons are now using spike shape parameters, and particularly spike duration, as a means of classifying different neuronal sub-types. If a projecting neuron fires a spontaneous spike shortly before the stimulus is delivered there is collision in the axon

In particular, Neuronal Spike Shapes NSS captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium K channels as specific markers of the identified NSS partitions.

This paper introduces Neuronal Spike Shapes NSS, which represents an innovative model-based approach crafted to explore electrophysiological data to study cell states within neurons. To bridge the existing gap in the investigation of excitability states, NSS introduces a simple model that requires a limited amount of electrophysiological EP

channels. Moreover, spike shapes may overlap, thus making it difficult to select optimal windows to separate them. Another simple spike-sorting approach is to select a characteristic spike shape for each neuron and assign the rest of the spikes via template matching. But again, this requires the intervention of a user for selecting the