Comparison of Different Spike Sorting Subtechniques Based on Rat Brain Basolateral Amygdala Neuronal Activity Developing electrophysiological recordings of brain neuronal activity and their analysis provide a basis for exploring the structure of brain function and nervous system investigation. The recorded signals are typically a combination of action potentials (spikes) and noise. High amounts of background noise and possibility of electric signaling recording from several neurons adjacent to the recording site have led scientists to develop neuronal signal processing tools such as spike sorting to facilitate brain data analysis. Spike sorting plays a pivotal role in understanding the electrophysiological activity of neuronal networks. This process prepares recorded data for meaningful interpretations of interactions among neurons, investigating the connectivity between different brain regions, and ultimately understanding the overall structure of brain functions. Spike sorting consists of three steps: spike detection, feature extraction, and spike clustering. There are several methods to implement each of spike sorting steps. This paper provides a systematic comparison of various spike sorting subtechniques applied to real extracellularly recorded data from a rat brain basolateral amygdala. An efficient sorted data resulted from careful choice of spike sorting submethods leads to better interpretation of the brain structures connectivity under different conditions, which is a very sensitive concept in diagnosis and treatment of neurological disorders. Here, spike detection is performed by appropriate choice of threshold level via three different approaches. Feature extraction is done through PCA and Kernel PCA methods, where Kernel PCA outperforms. We have applied four different algorithms for spike clustering including Kmeans, Fuzzy Cmeans, Bayesian and Fuzzy maximum likelihood estimation. As one requirement of most clustering algorithms, optimal number of clusters is achieved through validity indices for each method. Finally, the sorting results are evaluated using interspike interval histograms. Rat brain real data used in simulations was recorded in neuroscience research center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference Extracellular recordings using modern, dense probes provide detailed footprints of action potentials (spikes) from thousands of neurons simultaneously. Inferring the activity of single neurons from these recordings, however, is a complex blind source separation problem, complicated both by the high intrinsic data dimensionality and large data volume. Despite these complications, dense probes can allow for the estimation of a spike9s source location, a powerful feature for determining the firing neuron9s position and identity in the recording. Here we present a novel, generative model for inferring the source of individual spikes given observed electrical traces. To allow for scalable, efficient inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on biophysically realistic simulated datasets, showing that our method outperforms heuristic localization methods such as center of mass and can improve spike sorting performance significantly. We further apply our model to real data to show that it is an effective, interpretable tool for analyzing largescale extracellular recordings.