5.2 Spyking Circus
5.2.5 Parameters in spike interface
Spyking circus have a lot of parameters that can be changed but only few of them nor- mally need to be changed. Below are only the parameters that someone can change when the spyking circus package is called through the spike interface framework. For more details about all the parameters that spyking circus have there is the documentation
4of the code.
4https://spyking-circus.readthedocs.io/en/latest/code/config.html
The Variables Explanation
Detect_sign=-1 Can be negative (default), positive or both (- 1, 1, 0).
Adjacency_radius=100 Channel neighborhood adjacency radius cor- responding to geom file
Detect_threshold=6 Value of λreferred to the chapter: Filtering and spike detection
Template_width_ms=3ms
Filter=True If True, then a low-pass filtering is per-
formed
Merge_spikes=True If True, then an automated Merge of the clusters is performed.
Auto_merge=0.75 Between 0 (aggressive) and 1 (no merging).
Num_workers=None The number of the cpu cores that will be used. If it is None the half of the available cpu cores are used.
Whitening_max_elts=1000 Max number of events per electrode for the whitenning
Clustering_max_elts=10000 Max number of events per electrode for the clustering
5.2.6 Visualise the results with Spyking circus
Spyking Circus has a Matlab Gui (see Fig. 5.6) for the visualization of the results. The Gui also gives you the option to merge two templates or delete a selected template .
Figure 5.6: The matlab Gui of Spyking Circus showing 2 of the 16 wells.
As you can see, the GUI is divided in several panels:
A A view of the templates
B A view of the features that gave rise to this templates C A view of the amplitudes over time
D A view for putative repeats, depending on your stimulation
E A view of the InterSpike Interval Distribution, for the given template F A view of the Autocorrelation
Chapter 6 Discussion
6.1 Manual curation of spike sorting results
After the automatic spike sorting of the recordings most of the time there is the need to refine the results. The most famous package for manual curation is the phy1 which is an open-source python library providing a graphical user interface for visualization and manual curation of large-scale electrophysiological data. Unfortunately phy can open datasets spike-sorted with only the following programs: KiloSort, SpykingCircus and klusta. But the spike interface framework gives you the option to export your spike- sorted data in all of these three formats regardless of the spike sorting algorithm you are applying in your recordings. The need for manual curation arises from three main reasons:
1) In some cases the automatic sorting has 2 different groups of spikes for the same neuron. So you have to merge the two clusters in one.
2) On the other hand the opposite also happens often. It makes one cluster for dif- ferent neurons so you have to split this cluster.
3) Groups of spikes that the algorithm found that aren’t from well-isolated neurons at all, but they are just plain noise that should be discarded.
1https://phy.readthedocs.io/en/latest/
Figure 6.1: A diagram of a sample spike sorting pipeline and phy (manual curation).
Figure is modified from(Buccino et al., n.d.).
6.2 Future directions
In this thesis we have investigated spike sorting in HD-MEA and the electrophysiology properties of the neuronal extracellular recording. Two different spike sorting packages has been tested with prominent results for the multi modal MEA.
There are a number of possibilities to extend the work presented here. The first step is to take a good recording which will be manually evaluated in order to restrict all the miss classification or any errors in the detection step. Then this recording should be used to compare different spike sorting packages. After the comparison of spike sorting algorithms and different pre-processing and post-processing step it can be extracted a standardize automated pipeline for processing the extracellular recordings.
Appendices
Appendix A
A.1. Theoretical issues about neuronal activity
The stochastic nature of the neuronal activity
When a stimulus is presented several successive times to a sensory neuron, the resulting successive spike trains are similar, but not identical: one can observe significant trial- to-trial variability [37] . This represents the stochastic nature of neuronal activity. That is a very important observation and it implies that theoretical models for studying the neural code maybe should be developed in a probabilistic context. For example, the mathematical process of encoding, transforming a physical signal to a pattern response of neural activity, should not be a classical function, but rather a conditional probability distribution of observing a response given the stimulus.
Modeling neuron activity
There are two main models of the neurons: biological models and the computational.
Biological models are describing the biological, chemical and physical aspects in action during neuronal activity. An example of a biological model is the famous Hogdkin- Huxley model [13] which describes the emission of an action potential with four non- linear differential equations modeling the dynamics of ionic channels in the cell mem- brane. In order to understand how neurons interact to perform computations all together we have the computational models which are describing the networks of neurons. Those models are not concerned with the cellular mechanisms involved in neural activity: each neuron is considered as an idealized mathematical object, performing a simple compu- tation on its inputs, and giving the result in its output. The fundamental issue of those models is to understand the emergence of complex behaviors in a network of simple components (neurons). Some examples of them are the McCulloch and Pitts neurons or Hopfield networks.
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