Spike sorting with Kilosort4

Pachitariu, Marius; Sridhar, Shashwat; Pennington, Jacob; Stringer, Carsen

doi:10.1038/s41592-024-02232-7

Cited by 53 publications

(5 citation statements)

References 49 publications

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“…This dataset contains neural recordings (two 96-electrode Utah arrays) from motor cortex (M1 and PMd) while a monkey performed a cycling task to navigate a virtual environment. Offline sorting of spike waveforms (using Kilosort [113]) yielded single-neuron and high-quality multi-neuron isolations. The dataset also includes threshold crossings that were detected online.…”

Section: Mc_cyclementioning

confidence: 99%

An emerging view of neural geometry in motor cortex supports high-performance decoding

Perkins

Cunningham

Wang

et al. 2023

Preprint

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Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. We document how low tangling – a typical property of motor-cortex neural trajectories – yields unusual neural geometries. We designed a decoder, MINT, to embrace appropriate statistical constraints for these geometries. MINT takes a trajectory-centric approach: a library of neural trajectories (rather than a set of neural dimensions) provides a scaffold approximating the neural manifold. Each neural trajectory has a corresponding behavioral trajectory, allowing straightforward but highly nonlinear decoding. MINT outperformed other interpretable methods, and outperformed expressive machine learning methods in 37 of 42 comparisons. Yet unlike such methods MINT's constraints are known, not the implicit result of optimizing decoder output. MINT performed well across tasks, suggesting its assumptions are generally well-matched to the statistics of neural data. Despite embracing highly nonlinear relationships between behavior and potentially complex neural trajectories, MINT's computations are simple, scalable, and provide interpretable quantities such as data likelihoods. MINT's performance and simplicity suggest it may be an excellent candidate for clinical BCI applications.

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Section: Mc_cyclementioning

confidence: 99%

An emerging view of neural geometry in motor cortex supports high-performance decoding

Perkins

Cunningham

Wang

et al. 2023

Preprint

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“…Future works can refine our estimates of derivation accuracy by addressing issues that would arise from extracellular recordings such as noise 18,54 and spike sorting [55][56][57] . In addition, including a larger portion of silent neurons as suggested experimentally 17,[33][34][35] and shared inputs among the neurons in the microcircuits 31,32 will help refine the estimated accuracy and the necessary stimulation strategies.…”

Section: Discussionmentioning

confidence: 99%

Deriving connectivity from spiking activity in biophysical cortical microcircuits

Moghbel,

Hassan,

Guet-McCreight

et al. 2024

Preprint

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Inferring detailed cortical microcircuit connectivity is essential for uncovering how information is processed in the brain. A common methodin vivouses short-lag spike cross-correlations to derive putative monosynaptic connections between pairs of neurons, but previous studies did not address confounds of physiological large-scale networks such as correlated firing and inactive neurons. We tested connectivity derivation methods on ground-truth spiking data from detailed models of human cortical microcircuits in different layers and between key neuron types. We showed that physiological oscillations in the large-scale microcircuits hindered derivation accuracy, which was improved using a shorter cross-correlogram analysis window. We then showed that connection derivation was poor in cortical layer 2/3 microcircuits compared to layer 5, due to low firing rates and inactive neurons. General stimulation strategies for layer 2/3 microcircuits led to only a moderate improvement in derivation performance, due to a trade-off between the proportions of inactive neurons and overactive neurons, indicating the need for more refined strategies. Lastly, we showed that derivation of inhibitory connections from somatostatin interneurons targeting distal dendrites required a longer timescale of cross-correlation lags. Our results identify key physiological challenges and methods to improve accuracy in deriving connections from spiking activity in large-scale neuronal microcircuits.

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“…Recording channels acquired electrical signals from the most dorsal region of the vibrissal primary somatosensory cortex (vS1) down to the most ventral region of the basolateal amygdala (BLA) using the deepest of the 960 electrode sites. Electrophysiological signals were processed with Kilosort2.5 (https://github.com/MouseLand/Kilosort) using default parameters for spike sorting and then manually curated with Phy2 (https://github.com/cortex-lab/phy) 63 . Only well isolated single units were used for electrophysiological data analysis.…”

Section: Methodsmentioning

confidence: 99%

A failure to discriminate social from non-social touch at the circuit level may underlie social avoidance in autism

Chari,

Hernandez,

Couto

et al. 2024

Preprint

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Social touch is critical for communication and to impart emotions and intentions. However, certain autistic individuals experience aversion to social touch, especially when it is unwanted. We used a novel social touch assay and Neuropixels probes to compare neural responses to social vs. non-social interactions in three relevant brain regions: vibrissal somatosensory cortex, tail of striatum, and basolateral amygdala. We find that wild type (WT) mice showed aversion to repeated presentations of an inanimate object but not of another mouse. Cortical neurons cared most about touch context (social vs. object) and showed a preference for social interactions, while striatal neurons changed their preference depending on whether mice could choose or not to interact. Amygdalar and striatal neurons were preferentially modulated by forced object touch, which was the most aversive. In contrast, theFmr1knockout (KO) model of autism found social and non-social interactions equally aversive and displayed more aversive facial expressions to social touch when it invaded their personal space. Importantly, whenFmr1KO mice could choose to interact, neurons in all three regions did not discriminate social valence. Thus, a failure to differentially encode social from non-social stimuli at the circuit level may underlie social avoidance in autism.

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Spike sorting with Kilosort4

Cited by 53 publications

References 49 publications

An emerging view of neural geometry in motor cortex supports high-performance decoding

An emerging view of neural geometry in motor cortex supports high-performance decoding

Deriving connectivity from spiking activity in biophysical cortical microcircuits

A failure to discriminate social from non-social touch at the circuit level may underlie social avoidance in autism

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