The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings. NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.

Experimental findings show the ubiquitous presence of graded responses and tuning curves in the neocortex, particularly in visual areas [1-15]. Among these, inferotemporal-cortex (IT) neurons respond to complex visual stimuli, but differences in the neurons' responses can be used to distinguish the stimuli eliciting the responses [8, 9, 16-18]. The IT projects directly to the medial temporal lobe (MTL) [19], where neurons respond selectively to different pictures of specific persons and even to their written and spoken names [20-22]. However, it is not clear whether this is done through a graded coding, as in the neocortex, or a truly invariant code, in which the response-eliciting stimuli cannot be distinguished from each other. To address this issue, we recorded single neurons during the repeated presentation of different stimuli (pictures and written and spoken names) corresponding to the same persons. Using statistical tests and a decoding approach, we found that only in a minority of cases can the different pictures of a given person be distinguished from the neurons' responses and that in a larger proportion of cases, the responses to the pictures were different to the ones to the written and spoken names. We argue that MTL neurons tend to lack a representation of sensory features (particularly within a sensory modality), which can be advantageous for the memory function attributed to this area [23-25], and that a full representation of memories is given by a combination of mostly invariant coding in the MTL with a representation of sensory features in the neocortex.

Microglia are central to neuroimmune responses and undergo dynamic structural and functional changes in models of stress and addiction, and in response to pharmacological treatments. While transcriptomic and proteomic assays provide insights into molecular profiles, morphological analysis remains a valuable proxy for assessing region-specific microglial response. However, morphological features alone often fail to capture the full complexity of microglial function, underscoring the need for standardized methods and complementary approaches. Here, we describe a standardized imaging pipeline for analyzing microglia in the nucleus accumbens core (NAcore), integrating unbiased confocal image acquisition with precise anatomical reference points. We compare two widely used image analysis platforms—IMARIS and CellSelect-3DMorph—highlighting their workflows, output metrics, and utility in quantifying microglial morphology following treatment with adenosine triphosphate (ATP). Both tools detect well described features of microglial dynamics, though they differ in automation level, analysis speed, and output types. Our findings demonstrate that both platforms provide reliable morphological data, with CellSelect-3DMorph offering a rapid, open-access alternative for high-throughput analysis. Additionally, using software-derived parameters in principal component analysis clustering has proven useful for identifying distinct subpopulations of microglia separated by their morphology. This work provides a practical framework for morphological analysis and promotes reproducibility in microglial studies under environmental and pharmacological interventions.

Exposure to chronic stress creates vulnerability to drug misuse and presents a barrier to sustained recovery for many individuals experiencing substance use disorders (SUDs). Preclinical literature demonstrates that stress modulates psychostimulant intake and seeking, yet there are wide gaps in our understanding of the specific mechanisms by which stress promotes brain changes that may govern addiction-related behaviors. Recent data suggest that microglia, innate immune cells in the central nervous system, are highly responsive to chronic stressors, and several mechanistic links have been explored highlighting the critical role microglia play in stress-related brain adaptation. Importantly, psychostimulants may engage similar microglial machinery, which opens the door for investigation into how microglia may be involved in shaping motivation for psychostimulants, especially in the context of stress exposure. The aims of this review are threefold: 1. Offer a brief overview of microglial biology in the adult brain. 2. Review current methods of interrogating microglial function with a focus on morphometric analyses. 3. Highlight preclinical research describing how microglia contribute to brain changes following chronic stress and/or psychostimulant exposure. Ultimately, this review serves to prime investigators studying the intersection of stress and SUDs to consider the relevant impacts of microglial actions.

Exposure to chronic stressors creates vulnerability to drug misuse and a barrier to sustained recovery for many individuals experiencing substance use disorders (SUDs). Preclinical literature demonstrates that stress modulates psychostimulant intake and seeking, yet there are wide gaps in our understanding of the specific mechanisms by which stress promotes brain changes that may govern addiction-related behaviors. Recent data suggest that microglia, innate immune cells in the central nervous system, are highly responsive to chronic stressors, and several mechanistic links have been explored highlighting the critical role microglia play in stress-related brain adaptation. Importantly, psychostimulants may engage similar microglial machinery, which opens the door for investigation into how microglia may be involved in shaping motivation for psychostimulants, especially in the context of stress exposure. The aims of this review are threefold: 1. Offer a brief overview of microglial biology in the adult brain; 2. Review current methods of interrogating microglial function with a focus on morphometric analyses; 3. Highlight preclinical research describing how microglia contribute to brain changes following chronic stress and/or psychostimulant exposure. Ultimately, this review serves to prime investigators studying the intersection of stress and SUDs to consider the relevant impacts of microglial actions.

The recent advances in the chronic implantation of electrodes have allowed the collection of extracellular activity from neurons over long periods of time. To fully take advantage of these recordings, it is necessary to track single neurons continuously, particularly when their associated waveform changes over time. Multiple spike sorting algorithms can track drifting neurons but they do not perform well in conditions like a temporary increase in the noise level and changes in the number of detectable neurons. In this work, we present Tracking_Graph, a general framework to track neurons under these conditions. Tracking_Graph can be implemented with different spike sorting algorithms, allowing the experimenter to use the algorithm best fitted for their setup. The main idea behind Tracking_Graph is the blockwise analysis of the recording and application of a classification algorithm to match spikes to templates in different segments, leading to a directional metric that can be used to link clusters across blocks. Moreover, the algorithm can detect and fix sorting errors (splits and merges) in isolated blocks. We compared an implementation of Tracking_Graph with other algorithms using long-term simulations and obtained superior performance in all the metrics.