scholarly journals P-sort: an open-source software for cerebellar neurophysiology

2021 ◽  
Vol 126 (4) ◽  
pp. 1055-1075
Author(s):  
Ehsan Sedaghat-Nejad ◽  
Mohammad Amin Fakharian ◽  
Jay Pi ◽  
Paul Hage ◽  
Yoshiko Kojima ◽  
...  
Keyword(s):  
Purkinje Cell ◽  
Open Source ◽  
Open Source Software ◽  
Spike Sorting ◽  
Analysis Software ◽  
Complex Spike ◽  
Statistical Property ◽  
Neurophysiological Data ◽  
Spike Analysis ◽  
Complex Spikes

Algorithms that perform spike sorting depend on waveforms to cluster spikes. However, a cerebellar Purkinje-cell produces two types of spikes; simple and complex spikes. A complex spike coincides with the suppression of generating simple spikes. Here, we recorded neurophysiological data from three species and developed a spike analysis software named P-sort that relies on this statistical property to improve both the detection and the attribution of simple and complex spikes in the cerebellum.

Developmental Changes in Evoked Purkinje Cell Complex Spike Responses

2003 ◽  
Vol 90 (4) ◽  
pp. 2349-2357 ◽  
Author(s):  
Daniel A. Nicholson ◽  
John H. Freeman
Keyword(s):  
Motor Learning ◽  
Purkinje Cell ◽  
Inferior Olive ◽  
Cell Complex ◽  
Climbing Fibers ◽  
Somatosensory Stimulation ◽  
Complex Spike ◽  
Long Latency ◽  
Inhibitory Feedback ◽  
Complex Spikes

The development of synaptic interconnections between the cerebellum and inferior olive, the sole source of climbing fibers, could contribute to the ontogeny of certain forms of motor learning (e.g., eyeblink conditioning). Purkinje cell complex spikes are produced exclusively by climbing fibers and exhibit short- and long-latency activity in response to somatosensory stimulation. Previous studies have demonstrated that evoked short- and long-latency complex spikes generally occur on separate trials and that this response segregation is regulated by inhibitory feedback to the inferior olive. The present experiment tested the hypothesis that complex spikes evoked by periorbital stimulation are regulated by inhibitory feedback from the cerebellum and that this feedback develops between postnatal days (PND) 17 and 24. Recordings from individual Purkinje cell complex spikes in urethan-anesthetized rats indicated that the segregation of short- and long-latency evoked complex spike activity emerges between PND17 and PND24. In addition, infusion of picrotoxin, a GABAA-receptor antagonist, into the inferior olive abolished the response pattern segregation in PND24 rats, producing evoked complex spike response patterns similar to those characteristic of younger rats. These data support the view that cerebellar feedback to the inferior olive, which is exclusively inhibitory, undergoes substantial changes in the same developmental time window in which certain forms of motor learning emerge.

scholarly journals P-sort: an open-source software for cerebellar neurophysiology

2021 ◽  
Author(s):  
Ehsan Sedaghat-Nejad ◽  
Mohammad Amin Fakharian ◽  
Jay Pi ◽  
Paul Hage ◽  
Yoshiko Kojima ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Statistical Characteristics ◽  
Nonlinear Dimensionality Reduction ◽  
Statistical Relationship ◽  
Manual Curation ◽  
Automated Software ◽  
P Cells ◽  
Single Cluster ◽  
Complex Spikes

AbstractAnalysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents challenges for spike detection. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. Here, we analyzed data from over 300 P-cells in marmosets, macaques, and mice, using an open-source, semi-automated software called P-sort that addresses the spike identification and attribution problems. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge seemingly disparate clusters, or split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns.

scholarly journals Epistemic Network Analysis for Semi-Structured Interviews and Other Continuous Narratives: Challenges and Insights

2019 ◽  
Author(s):  
Szilvia Zörgő ◽  
Gjalt - Jorn Ygram Peters
Keyword(s):  
Decision Making ◽  
Network Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Open Science ◽  
Manual Segmentation ◽  
Analysis Software ◽  
Structured Interviews ◽  
Therapy Choice ◽  
Open Coding

Applying Quantitative Ethnography (QE) techniques to continuous narratives in an inquiry where manual segmentation with a multitude of codes is preferred poses several challenges. In order to address these issues, we developed the Reproducible Open Coding Kit – convention, open source software, and interface – that eases manual coding, enables researchers to reproduce the coding process, compare results, and collaborate. The ROCK can also be employed to prepare data for Epistemic Network Analysis software. Our paper elaborates the challenges we encountered and the insights we gained while conducting a research project on decision-making regarding therapy choice among patients in Budapest, Hungary. Our aim is to broaden the usage of QE, while facilitating Open Science principles and transparency.

scholarly journals Using extracellular low frequency signals to improve the spike sorting of cerebellar complex spikes

2019 ◽  
Author(s):  
Gil Zur ◽  
Mati Joshua
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Frequency Component ◽  
Climbing Fibers ◽  
High Frequency Component ◽  
Spike Sorting ◽  
Low Frequencies ◽  
Complex Spike ◽  
Complex Spikes

AbstractThe challenge of spike sorting has been addressed by numerous electrophysiological studies. These methods tend to focus on the information conveyed by the high frequencies, but ignore the potentially informative signals at lower frequencies. Activation of Purkinje cells in the cerebellum by input from the climbing fibers results in a large amplitude dendritic spike concurrent with a high frequency burst known as a complex spike. Due to the variability in the high frequency component of complex spikes, previous methods have struggled to sort these complex spikes in an accurate and reliable way. However, complex spikes have a prominent extracellular low frequency signal generated by the input from the climbing fibers. We exploited this to improve complex spike sorting by applying Principal Component Analysis (PCA) on the low frequencies of the signal and show that the low frequency first PC achieves a better separation of the complex spikes from noise. The low frequency data are more effective in detecting events entering into the analysis, and therefore can be harnessed to analyze the data with a larger signal to noise ratio. These two advantages make our method more effective for complex spike sorting. Our characterization of the dendritic low frequency components of complex spikes can be applied in other studies to gain insights into processing in the cerebellum.

Increase in Purkinje cell gain associated with naturally activated climbing fiber input

1983 ◽  
Vol 50 (1) ◽  
pp. 205-219 ◽  
Author(s):  
T. J. Ebner ◽  
Q. X. Yu ◽  
J. R. Bloedel
Keyword(s):  
Purkinje Cell ◽  
Purkinje Cells ◽  
Mossy Fiber ◽  
Climbing Fiber ◽  
Peripheral Stimulus ◽  
Short Term ◽  
Spike Response ◽  
Complex Spike ◽  
Simple Spike ◽  
Complex Spikes

These experiments were designed to test the hypothesis that climbing fiber inputs evoked by a peripheral stimulus increase the responsiveness of Purkinje cells to mossy fiber inputs. This hypothesis was based on a previous series of observations demonstrating that spontaneous climbing fiber inputs are associated with an accentuation of the Purkinje cell responses to subsequent mossy fiber inputs (10, 12). Furthermore, short-term nonpersistent interactions between climbing and mossy fiber inputs have been an important aspect of many theories of cerebellar function (5, 7, 8, 12, 36). Extracellular unitary recordings were made from Purkinje cells in lobule V of decerebrate, unanesthetized cats. To activate mossy and climbing fiber inputs, the forepaw was passively flexed by a Ling vibrator system. A data analysis was developed to sort the simple spike trials into two groups, based on the presence or absence of complex spikes activated by the stimulus. In addition, during those trials in which complex spikes were activated, the simple spike train was aligned on the occurrence of the complex spike. For each simple spike response to the forepaw input, the average firing rate during the response was compared to background both in those trials in which complex spikes were activated and in those in which they were not. The ratio of the response amplitudes in the histograms constructed from these two groups of trials permitted a quantification of the change in responsiveness when climbing fiber inputs were activated. The results show that both excitatory and inhibitory simple spike responses are accentuated when associated with the activation of a complex spike. Using an arbitrary level of a gain change ratio of 120% as indicating a significant modification, 64% of the response components analyzed increased their amplitude when climbing fiber input was present. Simple spike response components occurring prior to complex spike activation were usually not accentuated, although in a few cells the amplitude of this component of the response increased. In addition, in a small number of cells the occurrence of complex spikes was associated with a new simple spike component. For excitatory responses, the magnitude of the gain change ratio was shown to be inversely related to the amplitude of the simple spike response evoked by the mossy fiber inputs. The data obtained is consistent with the hypothesis that the climbing fiber input is associated with an increase in the responsiveness of Purkinje cells to mossy fiber inputs. The increased responsiveness occurs whether the simple spike modulation evoked by the peripheral stimulus is excitatory or inhibitory. The change in responsiveness is short term and nonpersistent. It is argued that the activation of climbing fiber inputs to the cerebellar cortex is associated with an increase in the gain of Purkinje cells to mossy fiber inputs activated by natural peripheral stimuli.

scholarly journals A Purkinje cell model that simulates complex spikes

2020 ◽  
Author(s):  
Amelia Burroughs ◽  
Nadia L. Cerminara ◽  
Richard Apps ◽  
Conor Houghton
Keyword(s):  
Action Potential ◽  
Purkinje Cell ◽  
Spike Activity ◽  
Cell Model ◽  
Compartment Model ◽  
Complex Spike ◽  
Simple Spike ◽  
Lower Amplitude ◽  
Complex Spikes

AbstractPurkinje cells are the principal neurons of the cerebellar cortex. One of their distinguishing features is that they fire two distinct types of action potential, called simple and complex spikes, which interact with one another. Simple spikes are stereotypical action potentials that are elicited at high, but variable, rates (0 – 100 Hz) and have a consistent waveform. Complex spikes are composed of an initial action potential followed by a burst of lower amplitude spikelets. Complex spikes occur at comparatively low rates (~ 1 Hz) and have a variable waveform. Although they are critical to cerebellar operation a simple model that describes the complex spike waveform is lacking. Here, a novel single-compartment model of Purkinje cell electrodynamics is presented. The simpler version of this model, with two active conductances and a leak channel, can simulate the features typical of complex spikes recorded in vitro. If calcium dynamics are also included, the model can capture the pause in simple spike activity that occurs after complex spike events. Together, these models provide an insight into the mechanisms behind complex spike spikelet generation, waveform variability and their interactions with simple spike activity.

scholarly journals A spike-sorting method based on hierarchical clustering to discriminate extracellularly recorded simple spikes and complex spikes from cerebellar Purkinje cells

2021 ◽  
Author(s):  
Takayuki Michikawa ◽  
Keisuke Isobe ◽  
Shigeyoshi Itohara
Keyword(s):  
Purkinje Cell ◽  
Purkinje Cells ◽  
Quantitative Evaluation ◽  
Cerebellar Cortex ◽  
Single Unit ◽  
Spike Sorting ◽  
Sorting Method ◽  
Single Unit Recordings ◽  
Cerebellar Purkinje Cells ◽  
Complex Spikes

Background: In the cerebellar cortex, Purkinje cells are the only output neurons and exhibit two types of discharge. Most Purkinje cell discharges are simple spikes, which are commonly appearing action potentials exhibiting a rich variety of firing patterns with a rate of up to 400 Hz. More infrequent discharges are complex spikes, which consist of a short burst of impulses accompanied by a massive increase in dendritic Ca2+ with a firing rate of around 1 Hz. The discrimination of these spikes in extracellular single-unit recordings is not always straightforward, as their waveforms vary depending on recording conditions and intrinsic fluctuations. New Method: To discriminate complex spikes from simple spikes in the extracellular single-unit data, we developed a semiautomatic spike-sorting method based on divisive hierarchical clustering. Results: Quantitative evaluation using parallel in vivo two-photon Ca2+ imaging of Purkinje cell dendrites indicated that 96.6% of the complex spikes were detected using our spike-sorting method from extracellular single-unit recordings obtained from anesthetized mice. Comparison with Existing Method(s): No reports have conducted a quantitative evaluation of spike-sorting algorithms used for the classification of extracellular spikes recorded from cerebellar Purkinje cells. Conclusions: Our method could be expected to contribute to research in information processing in the cerebellar cortex and the development of a fully automatic spike-sorting algorithm by providing ground-truth data useful for deep learning.

scholarly journals Using deep neural networks to detect complex spikes of cerebellar Purkinje Cells

2019 ◽  
Author(s):  
Akshay Markanday ◽  
Joachim Bellet ◽  
Marie E. Bellet ◽  
Ziad M. Hafed ◽  
Peter Thier
Keyword(s):  
Motor Control ◽  
Deep Learning ◽  
Action Potential ◽  
Purkinje Cell ◽  
Purkinje Cells ◽  
Local Field ◽  
Visual Inspection ◽  
Complex Spike ◽  
Cerebellar Purkinje Cells ◽  
Complex Spikes

AbstractOne of the most powerful excitatory synapses in the entire brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivary neuron is capable of generating a large electrical event, called “complex spike”, at the level of the postsynaptic Purkinje cell, comprising of a fast initial spike of large amplitude followed by a slow polyphasic tail of small amplitude spikelets. Several ideas discussing the role of the cerebellum in motor control are centered on these complex spike events. However, these events are extremely rare, only occurring 1-2 times per second. As a result, drawing conclusions about their functional role has been very challenging, as even few errors in their detection may change the result. Since standard spike sorting approaches cannot fully handle the polyphasic shape of complex spike waveforms, the only safe way to avoid omissions and false detections has been to rely on visual inspection of long traces of Purkinje cell recordings by experts. Here we present a supervised deep learning algorithm for rapidly and reliably detecting complex spikes as an alternative to tedious visual inspection. Our algorithm, utilizing both action potential and local field potential signals, not only detects complex spike events much faster than human experts, but it also excavates key features of complex spike morphology with a performance comparable to that of such experts.Significance statementClimbing fiber driven “complex spikes”, fired at perplexingly low rates, are known to play a crucial role in cerebellum-based motor control. Careful interpretations of these spikes require researchers to manually detect them, since conventional online or offline spike sorting algorithms (optimized for analyzing the much more frequent “simple spikes”) cannot be fully trusted. Here, we present a deep learning approach for identifying complex spikes, which is trained on local field and action potential recordings from cerebellar Purkinje cells. Our algorithm successfully identifies complex spikes, along with additional relevant neurophysiological features, with an accuracy level matching that of human experts, yet with very little time expenditure.

scholarly journals Developing open-source software for bioimage analysis: opportunities and challenges

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 302
Author(s):  
Florian Levet ◽  
Anne E. Carpenter ◽  
Kevin W. Eliceiri ◽  
Anna Kreshuk ◽  
Peter Bankhead ◽  
...  
Keyword(s):  
Computer Science ◽  
Open Source ◽  
Computational Methods ◽  
Open Source Software ◽  
Analysis Software ◽  
Efficient Manner ◽  
Bioimage Analysis ◽  
The Common ◽  
Science Labs

Fast-paced innovations in imaging have resulted in single systems producing exponential amounts of data to be analyzed. Computational methods developed in computer science labs have proven to be crucial for analyzing these data in an unbiased and efficient manner, reaching a prominent role in most microscopy studies. Still, their use usually requires expertise in bioimage analysis, and their accessibility for life scientists has therefore become a bottleneck. Open-source software for bioimage analysis has developed to disseminate these computational methods to a wider audience, and to life scientists in particular. In recent years, the influence of many open-source tools has grown tremendously, helping tens of thousands of life scientists in the process. As creators of successful open-source bioimage analysis software, we here discuss the motivations that can initiate development of a new tool, the common challenges faced, and the characteristics required for achieving success.

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