Spikesort 3d

Manufactured by Neuralynx

Sourced in United States, United Kingdom

SpikeSort 3D is a powerful data analysis software developed by Neuralynx. It provides sophisticated tools for sorting and analyzing neural spike data recorded from multi-channel electrophysiology systems. The software offers advanced three-dimensional visualization and clustering algorithms to aid in the identification and classification of individual neurons.

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Lab products found in correlation

Matlab, by MathWorks (4 mentions) Pcie 6351, by National Instruments (2 mentions) 200 m optic fibre, by Thorlabs (2 mentions) Digilynx recording system, by Neuralynx (2 mentions) Cheetah acquisition software, by NeuroNexus (2 mentions) Rhd2164 headstage, by Intan Technologies (2 mentions) Cheetah acquisition software, by Neuralynx (2 mentions) Digital lynx s, by Neuralynx (2 mentions) Cheetah data acquisition system, by Neuralynx (1 mentions) Matlab r2018a, by MathWorks (1 mentions) Digilynx, by Neuralynx (1 mentions) Digital lynx sx, by Neuralynx (1 mentions) Cheetah, by Neuralynx (1 mentions) Digital lynx system, by Neuralynx (1 mentions) Hs 18, by Neuralynx (1 mentions)

Close spelling variants of this product

SpikeSort 3D 2.5.1.0 (3 citations)

27 protocols using spikesort 3d

Spike Sorting and Artifact Exclusion

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Once rats had recovered from surgery, recording sessions were performed in a behavioral chamber outfitted with a 32 channel recording system (Neuralynx). Spiking data was acquired using a bandpass filter between 600 and 6000 Hz and a spike detection threshold of 30 μV. Clusters were manually cut (Spikesort 3D, Neuralynx), and both single- and multi-units were considered. All manually cut units were used for analysis. We observed that a small fraction of trials showed apparent artifacts in which some units appeared to have extremely high firing rates, which we suspect was due to motion of the implant or tether. We therefore excluded units from analysis on trials where the Median Absolute Deviation (Leys et al., 2013 (link)) of their spike count exceeded a conservative threshold of three.

Miller K.J., Botvinick M.M, & Brody C.D. (2022). Value representations in the rodent orbitofrontal cortex drive learning, not choice. eLife, 11, e64575.

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Single-Unit Recording Protocol

Cited 1 time

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The impedance of electrode tips was matched to 100–300 kΩ measured at 1 kHz through gold plating. After the postoperative recovery period, electrodes were gradually advanced (≤160 µm per day) until reached the target regions. Unit isolation and cluster cutting procedures have been described before (Kim et al., 2007 (link)). Briefly, unit signals were amplified (10,000×), filtered (600 Hz to 6 kHz), and digitized (32 kHz) by using the Cheetah data acquisition system (Neuralynx). Unit isolation was performed by using an automatic spike-sorting program (SpikeSort 3D; Neuralynx) and additional manual cutting as described in previous studies (Kim et al., 2018 (link); Kim et al., 2015 (link)). Raster plots and peristimulus time histograms were generated by NeuroExplorer (Nex Technologies). For all units, we ruled out any chance of recording the same cells across multiple sessions by comparing the shape of the waveform, autocorrelogram, and interspike interval histogram between recording days.

Kong M.S., Kim E.J., Park S., Zweifel L.S., Huh Y., Cho J, & Kim J.J. (2021). ‘Fearful-place’ coding in the amygdala-hippocampal network. eLife, 10, e72040.

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Electrophysiological Characterization of VTA Neurons

Cited 2 times

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Extracellular recordings were made from VTA using a data acquisition system (DigiLynx, Neuralynx). VTA recording sites were verified histologically. The identity of dopaminergic cells was confirmed by recording the electrophysiological responses of cells to a brief blue light pulse train, which stimulates only DAT-expressing cells. Spikes were sorted using Spike-Sort3D (Neuralynx) or MClust-3.5 (A.D. Redish). Putative GABAergic neurons in the VTA were identified by clustering of firing patterns as described previously^{34 (link),37 (link)}. All confidence intervals are standard error unless otherwise noted.
Data analyses were performed using NumPy 1.15 and MATLAB R2018a (Mathworks). Spike times were collected in 1 ms bins to create peri-stimulus time histograms. These histograms were then smoothed by convolving with the function

(1 - e^{- t}) \cdot e^{- t / T}

where T was a time constant, set to 20 ms as in Eshel et al.^{34 (link)}. For single-cell traces, we set T to 200 ms for display purposes.
After smoothing, the data were baseline-corrected by subtracting from each trial and each neuron independently the mean over that trial’s activity from −1000 to 0 ms relative to stimulus onset (or relative to reward onset in the unexpected reward condition).

Dabney W., Kurth-Nelson Z., Uchida N., Starkweather C.K., Hassabis D., Munos R, & Botvinick M. (2020). A distributional code for value in dopamine-based reinforcement learning. Nature, 577(7792), 671-675.

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Reaching Movement Electrophysiology Analysis

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Data was acquired at 30kHz using Cheetah acquisition software and Digital Lynx SX hardware (Neuralynx). TTL pulses generated by the master camera were channeled into the Digital Lynx to synchronize videos with the electrophysiology data (Figure 6B). After recording, single-unit activity was clustered manually using Spike Sort 3D (Neuralynx). Isolation distance and L-ratio were used to quantify cluster quality and noise contamination (Schmitzer-Torbert et al., 2005 (link)). Frames containing the maximum outward extent of a reach were identified using the CLARA curator (Supp. Figure 1). These frames were used to create reaching epochs in the recording data (±500ms from the reach maximum). Spike data during each reaching epoch was binned at 10 ms and trial-averaged. Firing rate was normalized to baseline activity (1000–500ms before Reach max) using a z-score. Units that displayed a significantly increased (z>2.56) firing rate for at least 100ms during the reach epoch (−500ms before Reach max to 500ms after) were classified as “Movement related”. All other units were classified as “non-movement related”. After all units were classified, they were normalized to generate a heatmap. All units were temporally shifted so that reach max was t=0 and plotted using custom software in MATLAB (MathWorks).

Bowles S., Williamson W.R., Nettles D., Hickman J, & Welle C.G. (2021). Closed-loop automated reaching apparatus (CLARA) for interrogating complex motor behaviors. Journal of neural engineering, 18(4), 10.1088/1741-2552/ac1ed1.

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Spike Sorting and Pyramidal Cell Identification

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Clusters of spikes were identified visually using SpikeSort 3D (Neuralynx), cut automatically using Klustakwik, and manually sorted/combined based on less than 25% visually identified overlap with noise, spike waveform height, and peak-amplitude. L-ratios were calculated for each cluster using David A. Redish’s MClust MATLAB package and only included if the value was less than 0.1 (Schmitzer-Torbert et al., 2005 (link)). Putative pyramidal cells were identified based on interspike-intervals and spike waveform shape (Ranck, 1973 (link); Figure 1C). Only clusters that were stable across the recording session were included for further analysis (Figure 1D). To assess cluster stability, for each cluster, we first used SpikeSort3D to visualize changes in waveform peaks across the recording session and excluded any clusters that showed a marked change in waveform peak across the session. To demonstrate the validity of this approach, for each of the remaining clusters, we selected the channel with the highest session-averaged peak amplitude and compared the mean peak amplitude (“waveform height”) for spikes recorded during the first 10 min to the mean peak amplitude for spikes recorded during the last 10 min using the Pearson correlation coefficient.

Stout J.J, & Griffin A.L. (2020). Representations of On-Going Behavior and Future Actions During a Spatial Working Memory Task by a High Firing-Rate Population of Medial Prefrontal Cortex Neurons. Frontiers in Behavioral Neuroscience, 14, 151.

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Neuronal Activity and Eye Dynamics

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Stimulus presentation, behavioral control and drug administration was regulated by Remote Cortex 5.95 (Laboratory of Neuropsychology, National Institute for Mental Health, Bethesda, MD). Raw data were collected using Remote Cortex 5.95 (1-kHz sampling rate) and by Cheetah data acquisition (32.7-kHz sampling rate, 24-bit sampling resolution) interlinked with Remote Cortex 5.95. Data were replayed offline, sampled with 16-bit resolution and band-pass filtered (0.6–9 kHz). Spikes were sorted manually using SpikeSort3D (Neuralynx). Eye position and pupil diameter were recorded using a ViewPoint eyetracker (Arrington research) at 220 Hz. Pupil diameter was recorded in 33 out of 47 recording sessions.

van Kempen J., Brandt C., Distler C., Bellgrove M.A, & Thiele A. (2022). Dopamine influences attentional rate modulation in Macaque posterior parietal cortex. Scientific Reports, 12, 6914.

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Extracellular Recording Techniques in VTA

Cited 1 time

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Recording techniques were based on a previous study^{6 (link)}. Briefly, we recorded extracellularly from VTA using a custom-built, screw-driven microdrive containing six or eight tetrodes (Sandvik, Palm Coast, Florida) glued to a 200 µm optic fibre (ThorLabs). Tetrodes were affixed to the fibre so that their tips extended 300–600 µm from the end of the fibre. Neural and behavioural signals were recorded with a DigiLynx recording system (Neuralynx) or a custom-built system using a multi-channel amplifier chip (RHA2116, Intan Technologies LLC) and data acquisition device (PCIe-6351, National Instruments). Broadband signals from each wire were filtered between 0.1 and 9000 Hz and recorded continuously at 32 kHz. To extract spike timing, signals were band-pass-filtered between 300 and 6000 Hz and sorted offline using SpikeSort3D (Neuralynx) or MClust-3.5 (A. D. Redish). At the end of each session, the fibre and tetrodes were lowered by 40–80 µm to record new units the next day.
To be included in the dataset, a neuron had to be well-isolated (L-ratio³⁶ < 0.05) and recorded within 0.5 mm of a light-identified or putative dopamine neuron, to ensure that it was recorded in VTA. Recording sites were also verified histologically with electrolytic lesions using 10–15 s of 30 µA direct current.

Eshel N., Bukwich M., Rao V., Hemmelder V., Tian J, & Uchida N. (2015). Arithmetic and local circuitry underlying dopamine prediction errors. Nature, 525(7568), 243-246.

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Tracking Neural Signals and Behavior

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Thresholded waveforms from each tetrode were conducted to a 16-channel head stage containing an operational amplifier (Neuralynx). A flexible cable connected the head stage to a 32-ch Digital Lynx data acquisition system (Neuralynx), where electrical signals were acquired at 32 kHz. An overhead color video camera was used to monitor the animal’s position at 30 frames/sec by tracking the position of one red light-emitting diode (LED) attached to the animal’s head stage. The positions of the LED during adjacent 33.3 msec epochs were then interpolated to estimate the LED position at 16.67 msec intervals, to mimic 60 frames/sec temporal resolution.
Tetrode signals were analyzed offline with SpikeSort 3D (Neuralynx). Signals from each tetrode were then evaluated for event parameters that correspond to the activity of a single neuron, using a procedure known as “cluster cutting” [31 (link),32 (link)]. Timestamps associated with single-unit events were then matched to the associated 16.67 msec interval from the video record using custom analysis software.

Harvey R.E., Rutan S.A., Willey G.R., Siegel J.J., Clark B.J, & Yoder R.M. (2018). Linear Self-Motion Cues Support the Spatial Distribution and Stability of Hippocampal Place Cells. Current biology : CB, 28(11), 1803-1810.e5.

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Extracting LFP and Spike Signals

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To obtain LFP, we low-pass filtered the raw voltage traces (cut-off frequency: 200 Hz), and then downsampled it to 496.6 Hz (the two-bat experiments) or 520.8 Hz (the four-bat experiments).
We detected spikes from band-pass filtered (600–6000 Hz) voltage traces using threshold crossing. Spike sorting was done automatically using SNAP Sorter (Neuralynx), then manually checked using SpikeSort3D (Neuralynx). For each tetrode on each session, all spikes not assigned to single units were grouped into a multiunit. All units with firing rate below 2 Hz were excluded from further analysis.

Zhang W., Rose M.C, & Yartsev M.M. (2022). A unifying mechanism governing inter-brain neural relationship during social interactions. eLife, 11, e70493.

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Single Unit Isolation and Analysis

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Single units were isolated using commercial software (SpikeSort 3D, Neuralynx). Various waveform parameters, including peak amplitude, energy, and peak-to-trough latencies, were used to distinguish single units from the PER (n = 66) and the POR (n = 51). Only units with mean firing rates ≥0.5 Hz during the event period (from cue onset to response) were analyzed.

Lim H.Y., Ahn J.R, & Lee I. (2022). The Interaction of Cue Type and Its Associated Behavioral Response Dissociates the Neural Activity between the Perirhinal and Postrhinal Cortices. eNeuro, 9(2), ENEURO.0065-22.2022.

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