Whole-cell patch clamp, part 4: look and feel

In previous blog posts, I have been discussing some aspects of whole-cell patch clamp recordings ([1], [2], [3][4]). Today, I will show some instructive videos that I recorded during experiments. I’m hoping that they will convey the look and feel of the procedure of whole-cell patching in an intact brain using two-photon microscopy to target neurons.

Two-photon targeted patching is the core method of my recent paper on a precise synaptic balance of input currents, focusing on biological questions instead of methods (Rupprecht et al. (2018)). The underlying method has been described before as “shadow-patching”. Dye ejected from the pipette allows to visualize cell bodies as dark shadows in a sea of fluorescence (see Kitamura et al. (2008); also check out Judkewitz et al. (2009)Margrie et al. (2003) and Komai et al. (2008)). Although these papers are very useful resources, they do not allow to understand how the procedure of patching a neuron looks and feels like to the experimenter.
For camera-based whole-cell patch clamp recordings in slices or dissociated cultures, on the other hand, there are a handful of videos on the internet (for example this one). However, this looks quite different from patching targeted by two-photon imaging. Camera-based imaging only allows to patch in thin tissues like cultures or slices since camera-imaging does not provide good optical sectioning and penetration. Here, I will show (uncut) movies of the process of patching, while monitoring the applied pressure and the test pulses.


As a brief introduction to two-photon targeted shadow-patching, the pipette approaches the brain surface while blowing out dye (1). After entering the tissue and after lowering of the pressure (2), the pipette closely approaches a target neuron (3). After gigaseal formation and break-in, allowing for electrophysiological recordings, the targeted neuron fills with the dye while the surroundings turn dark again (4).

All of the videos below are patch clamp recordings in the olfactory cortex homolog of zebrafish in an ex vivo preparation where the entire brain including the nose remain intact. Neurons are labeled using pan-neuronal expression of GCaMP6f, which is barely visible compared to the dye; shadow-imaging is performed using a resonant scanning microscope (described before on this blog). Neuronal somata in this brain area are quite small (5-8 μm in diameter, which is probably half or less the size of a typical mouse principal neuron), which can render targeted patching quite difficult, especially in deeper structures, where resolution degrades due to scattering. All of the recordings below are in more or less superficial regions (<200 μm below the brain surface). Patching deeper neurons usually required much more focused attention from my side, and the pipette tip could not be localized as easily as in the movies below.

For the paper, I produced a “Methods Video”, which due to restrictions from Neuron is limited to a duration of 1 min. I wanted to record not only the fluorescence movie during patching, but also the pressure and the test pulses applied to the electrode. For screen capture, I used the software TinyTake; for video editing, KdenLive (Linux); for text to speech synthesis of the next video, Wavenet provided by Google Cloud, which I have discussed before on this blog). The video is available in the Star Methods section of the paper, and also here:

However, the short duration of the video is maybe appropriate as a short visual summary for a paper, but not ideal for somebody who wants to get an intuition on how shadow patching can be done in reality. Therefore, here’s a longer excerpt of the same recording. I sometimes use this excerpt for presentations:

Still, this is a bit too condensed. Therefore, below you will find the uncut version of this particular patching experience. I admit it is really boring to watch, but I think it is also instructive. Not shown in the video are the changing positions of the two micromanipulators that move the pipette tip and the focus of the microscope; also not shown are small modifications to the laser power, zoom settings, bidirectional scan phase or the electrophysiological recording conditions. And yes, I’m aware that this recording is far from being perfect, but I think it can still be a useful starting point for a prospective electrophysiologist.

Next comes the patching of a different neuron. Usually, I’m using a syringe to apply pressure and suction to the pipette (other people prefer to apply the suction with the mouth). Here, after establishment of the giga-seal, the syringe somehow broke down and was not useable any more. I quickly constructed a temporary mouthpiece out of some tubings and finally managed to successfully break into the cell.

And here yet another successful attempt:

In total, I made around 20 such simultaneous recordings of the two-photon video, the pressure indicator and the test pulses window. Assembling the videos, however, turned out to take quite some time, and therefore I will show only one more movie, this time of a failed attempt. Almost immediately after entering the tissue, I realized that this recording would probably be not successful (the dura covering the brain sticked to the pipette tip for too long). Usually I would have stopped the attempt as early as possible in order not to waste time. In this case, I still tried to patch a neuron in order to get a nice recording of a failed attempt. Failures are not really rare when you try patching, especially in deep and small neurons.

Of course, shadow-patching might look somewhat different in a different brain region or with a different microscope or at a different tissue depth. To give you an idea, here is a recording with lower light levels due to lower dye concentration and laser power and with some problems related to the microscope (which I was too lazy to debug back then) which did not allow to zoom in as much as for the previous recordings. For someone not familiar with the particular setup, it is probably quite difficult to accurately see the pipette tip – which is crucial to move the pipette to the right location, in particular in the z-direction.

Posted in Calcium Imaging, electrophysiology, Imaging, Microscopy, Neuronal activity, zebrafish | Tagged , , , | 1 Comment

Precise synaptic balance of excitation and inhibition

The main paper of my PhD just got published: Rupprecht and Friedrich, Precise Synaptic Balance in the Zebrafish Homolog of Olfactory Cortex, Neuron (2018). (PDF)

You might like it if you are also interested in

  • Classical balanced networks
  • Things you can do with whole-cell voltage clamp
  • Olfactory cortex
  • More recent ideas about balanced networks
  • Coordination of excitatory and inhibitory synaptic inputs in single neurons
  • Adult zebrafish

To summarize this work in one sentence, this is a study of the coordination of excitatory and inhibitory synaptic inputs in single neurons. If you want to know the details, you should definitely read the paper.

The main part of the study is purely experimental, but one of its strengths is that it connects the experimental findings with computational concepts about balanced networks. The concept of a balanced state has been brought up in the mid-90s by Shadlen & Newsome and van Vreeswijk & Sompolinsky (among others). More recent theoretical work has, in my opinion, contributed a lot to identifying and correcting some weaknesses of the classical balanced network, and has come up with new concepts about circuit function of balanced networks that are of general interest to those who want to understand how the brain works. If you’re interested in a discussion of these concepts, I can recommend the following review articles as starting points (which are also discussed in our paper):

  1. Denève S, Machens CK, Efficient Codes and Balanced Networks, Nature Neuroscience (2016).
  2. Hennequin G, Agnes EJ, Vogels TP, Inhibitory Plasticity: Balance, Control, and Codependence, Annual Review of Neuroscience (2017)..

But let’s for one moment think beyond the scope of this work, which focused on synaptic inputs on the single-cell level – let’s think about the subcellular level. One thing I’d be interested in would be to have a closer look at the coordination of synaptic inputs on small dendritic segments instead of entire neurons. There is already a handful of studies that go into that direction, using mechanisms of synaptic plasticity (Chiu et al., Neuron, 2018) or the anatomical distribution of synapses (Iascone et al., bioRxiv) as entry points.

I’m really looking forward to seeing more research going into this subcellular level of neuronal processing. I can understand that people find population codes as observed by calcium imaging and extracellular recordings of interest, especially with respect to behavior. But I’m also convinced that mechanistic insights into how neurons work can be better obtained by investigating cellular and sub-cellular processes. Our published study investigates a variety of details on the cellular level; but this is only a small fraction of the many things that go unnoticed if you only look at the firing of neurons and not at underlying processes, for example the synaptic inputs.

Posted in Calcium Imaging, Data analysis, electrophysiology, Network analysis, Neuronal activity, zebrafish | Tagged , , , , | Leave a comment

Alvarez lenses and other strangely shaped optical elements

In typical microscopes, lenses or mirrors are moved forth and back to change the position of their focus. Tunable lenses like the electro-tunable lens or the TAG lens, on the other hand, are deformed by an external force and thereby change their focal length. One interesting concept that I had not noticed until recently is the idea of the Alvarez lens, named after its inventor (described in this 1964 patent). I came across it in a 2017 paper from the lab of Monika Ritsch-Marte from Innsbruck/Austria. The following picture adapted from their paper very nicely illustrates the effect:


By lateral displacement of the two lens elements against each other one can focus or de-focus the beam. In two papers from this lab (paper 1, paper 2), the authors used a method that sort of replaces this lateral (slow) movement with a (fast) rotation of a galvo mirror by using a creative optical configuration (check out the paper for the details, it is a pleasure to read).

There a couple of things to notice: The Alvarez lens is a bit more complex in 2D (the above schematic illustrates a (de-)focusing system for 1D only). The authors use diffractive instead of refractive Alvarez lenses. They use only visible light (no near-infrared light, which I would prefer). And they mention some other shortcomings of their approach.

Still, I find the principle very interesting and inspiring, and I hope that somebody will invest his or her time to put a system together that is not only a proof-of-principle, but an optimized system that reaches the best possible performance. This would probably also be a nice playground for a study of optical modeling and optimization: to find out which shape of the lens could perform much better than the Alvarez lens (like this study, but a bit more systematic with respect to possible lens surfaces).

Overall, this is a fascinating piece of optics, and I got interested also because I had always been intrigued by optical scanning methods where a simple movement of the beam is translated to a complex scanning scheme by an optical element (see for example this blog post on entirely passive scanning at MHz rates). For a long time, I hoped that a method similar to an Alvarez lens and based on a strangely shaped mirror (or lens) surface could be used to transform a linearly scanned pattern into something more complex (like a spiral scan, or a 2D raster scan). In theory, this is possible, but in practice the finite beam diameter would create a lot of problems. In addition, constructing an arbitrarily shaped mirror with good surface flatness and broadband reflective coatings would be quite costly.

One field where I long thought that such an approach could be applied, because it would be applicable for many microscopes, is the un-distortion of the non-linear angular scanning trajectory of resonant scanners (described in detail in a previous blog post). The idea would be that an optical element (the ‘black box lens’ in the schematic below) placed after the resonant scanner would somehow convert the angular dependency \sim \sin(\omega t) into a relationship that is rather linear in time, \sim t. Such that at time points close to the turnaround of the sine (blue time point below), the ‘black box lens’ would increase the angular deflection angle, eventually inversing the sine function:


I have the suspicion that this problem is practically not solvable due to the finite beam diameter, but it would be interesting to know whether there is a solution for this problem at least for the assumption of infinitely small scanning beams using geometric optics. This could be done by a lens whose diffractive power increases with distance x from the center of the lens.

Let’s assume a scan angle \alpha = \sin( \omega t). The scanned beam hits the black box lens at a position x(t) = \tan(\alpha) \cdot d with the distance d between the resonant scanner and the lens. The refractive power f(x) of the lens must therefore change with x such that the outgoing beam is linear in time. In approximative ABCD optics:

\left( \begin{array}{cc} 1 & 0 \\ -\frac{1}{f(x(t))} & 1 \end{array} \right) \cdot \left( \begin{array}{cc} x(t) \\ \alpha (t) \end{array} \right) \stackrel{!}{=} \left( \begin{array}{cc} x(t) \\ t \end{array} \right)

This results in the following expression for the local radius of the lens depending on the location x:

f(x) = \frac{x}{\arctan(x/d) - \arcsin(\arctan(x/d))}

You can find the equations and some plots also in a Jupyter notebook on Github. For small absolute values of x, f(x) diverges, indicating an infinite curvature, i.e., a lens that simply transmits light without deflection. With increasing/decreasing x, f(x) tends to zero, indicating increasing refractory power and a stronger local curvature of the lens surface.

Such a lens would only work optimally at one single zoom setting, which is probably one of the many reasons why nobody ever has tried this out. But it’s still interesting to think about it.

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Entanglement of temporal and spatial scales in the brain, but not in the mind

In physics, many problems can be solved by a separation of scales and thereby become tractable. For example, let’s have a look at surface waves on water: they are rather easy to understand when the water wave-length is much larger or much smaller than the depth of the water, but not if both scales are similar (wikipedia).

To give another example, light scattered by small particles (like fat bubbles in milk, or water drops in a cloud) can be described more easily if the wavelength of the light is much larger (Rayleigh scattering) or much smaller than the particles, but not if it is of the same order of magnitude (Mie scattering). Separation of scales is often key to making a problem tractable by mathematics.

What physicists like even more than separation of spatial scales, is the separation of different temporal scales. For example, consider two variables A(t) and B(t) that are influenced by each other:

\tau_1 \frac{d A}{d t} = f(A,B) \\ \\ \tau_2 \frac{d B}{d t} = g(A,B)

If the timescales separate, for example \tau_1 \gg \tau_2, the variable B(t) is basically seen as constant by the variable A(t). In this case, the variables can be decoupled, and the problem is often solvable. (Sidenote: In very simple and idealized systems without separations of scales, for example during some sort of phase transitions, mathematical physics can still come to the rescue and provide some clean solutions. But in most systems, this is not the case.)

I am convinced that problems do not become easier by a separation of scales only for physics or mathematics. I think that this applies even more to our intuition and our own understanding of the world. Automatically, we try to disentangle systems by using hierarchies and separations of length- and timescales, and if we are unable to do so, our intuition fails, as does the physics analysis.

What about the brain? In my opinion, the brain is one of those system that will defy human attempts to understand it by separating temporal processes or spatial modules. The brain consists of an enormous amount of different temporal and spatial scales that, however, overlap with each other and cannot be easily segregated. For example, on the timescale of few 100 ms, many different processes are non-stationary and therefore relevant at the same time: neuromodulation of many kinds; spike frequency adaptation and  presynaptic adaptation and facilitation; diffusion of proteins across spines, or ions across dendrites; calcium spikes; NMDA currents; et cetera. At a timescale of 1000 ms or 10 ms, it is a different but overlapping set of processes that are non-stationary. To put it short, it seems likely to me that the brain consists of a temporal and spatial continuum of processes, rather than a hierarchy.

Why would this be so? Because, as far as I can see, there is no incentive for nature to prevent the entanglement of temporal and spatial scales of all those processes. In contrast, those interactions may offer advantages that emerge randomly by evolution, at the cost of a higher complexity. Nature, which does not need to understand itself, probably does not care much about an increase of complexity, unlike the biologists working to disentangle the chaos.
It is perhaps misleading to personify ‘nature’ and to speak of an ‘incentive’. It is probably more acceptable to derive these processes from ‘entropic forces’, which make any ordered system, including the organic and cellular systems invented by evolution, less ordered and therefore more chaotic over time. Even if there was order once (think of a glass of water which is strictly colored green in the left and blue in the right half), random changes, which is the driving force of evolution, will undo this order (nothing can prevent that green and blue water will mix over time by random motion of its molecules, that is, diffusion).

In addition to the deficiency of our mind and of mathematical tools when it comes to entangled scales, I suspect based on personal experience that humans are to some extent unable to bring together knowledge from different hierarchies. In neuroscience, most researchers stick to one small level of observation and the related processes; and in most cases it is very difficult to bridge the gaps between levels. For example, “autism” can be addressed by a neurologist who thinks about case studies and very specific behavioral observations of her patients; by a geneticists looking for combinations of genes that make a certain autistic feature in humans more likely; or by a neurophysiologist studying neurons in animals or in vitro models of autism, trying to dissect the contribution of neuronal connectivity or ion channel expression. Many people believe (or hope) that with sufficient knowledge and understanding, these different levels of observation will fuse together, resulting in a complete understanding that pervades all levels. I would argue – and I’d like to be disproven – that a more pessimistic view seems to be more realistic and that humans will probably never achieve an understanding of neuronal circuits and the brain that is deep enough to bridge the gaps between the levels.

The limitations of both our mathematical tools and our mind when it comes to complex systems is obvious when we think of deep learning. For this field of machine learning, other than for the brain, we know all the basic principles (because we have defined them ourselves): Back-propagation of errors, gradient descent algorithms for optimization, weight-sharing in convolutional networks, rectified linear units (or maybe LSTM units), and a few more. Compared with the brain, the system is not very complex, and we can observe everything throughout the process without interfering with its operation. Still, although the process is 100% transparent, people struggle and fail to understand what is happening and why. There does not seem to be a simple answer to the question how it works. “What I cannot create, I do not understand”, Feynman famously wrote. But the act of creation does not automatically come with understanding.

Experimental neuroscience might face similar, but probably even more complex problems. The way to “understand” a neuronal process that is accepted by most researchers is a (mathematical or non-mathematical) model that can both reproduce and predict experimental results. However, if biology indeed consists of many processes and components that are entangled in space and time, also a model needs to be built that is entangled on several temporal and spatial scales. This can be done – no problem. However, this model will again resist attempts by mathematics or human intuition to understand it, similar to our current lack of understanding of the less complex deep networks. Therefore, the machine (the model, the computer program) will still be able to deal with the complexity and “understand” the brain, but I am not sure that human intuition will be able to follow.

I don’t want to deny all the pieces of progress that have been made to achieve a better understanding of the brain. I rather want to point out the limitation of the human mind when it comes to putting the pieces together.

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Blue light-induced artifacts in glass pipette-based recording electrodes

Recently, I was carrying out whole-cell voltage-clamp and LFP recordings with simultaneous optogenetic activation of a channelrhodopsin using blue light. Whole-cell voltage clamp techniques can record the input currents seen by a neuron (previously on this blog [1], [2]); an LFP records the very small synaptic currents in bulk brain tissue (nicely reviewed by Oscar Herreras); and optogentics with genetically encoded rhodopsins can make neurons fire using light pulses.

For the LFP recordings, I used the same glass pipette that I had used before for the whole-cell recording of a nearby neuron. In the LFP, I saw a light-evoked response which I first thought was a rhodopsin-evoked synaptic current. However, it turned out that I could make the same observation when positioning the pipette tip in the bath instead of in the tissue, which meant that this was clearly not a synaptic current, but an artifact. When changing the pipette resistance by gently breaking the pipette tip, the light-evoked voltage remained the same, whereas the evoked currents changed proportionally with the pipette resistance Rp, or more generally with the resistance between the two electrodes:


I found out that this sort of artifact has been described in the context of tetrode recordings several years ago by Han et al. (2009; supplementary figure 1) and has been sort of explained with the Becquerel effect (here), which is better known as the photovoltaic effect. According to Han et al., the effect is stronger for blue light and affects the recorded currents on a slow timescale, such that highpass-filtering of the recorded signal, which is used to detect spikes in tetrode recordings, gets rid of this artifact.

In addition, Han et al. state:

We have not seen the artifact with pulled glass micropipettes (such as previously used in Boyden et al., 2005 and Han and Boyden, 2007, or in the mouse recordings described below). Thus, for recordings of local field potentials and other slow signals of importance for neuroscience, hollow glass electrodes may prove useful.

Contrary to this suggestion, my above measurements indicate that using a glass electrode does not or not always get rid of the artifact. To better understand this artifact, I checked whether it was mediated by the chloride silver electrode in the glass pipette or rather by the ground electrode, and found that both contributed more or less equally to the artifact in this experiment. Protection of the electrode by some sort of cover reduced the magnitude of the artifact.

What does this mean for whole-cell or LFP recordings using a glass pipette? For whole-cell recordings, the resistance between the two electrodes is much larger than for the two traces shown in the plots above, typically between 50 and 2000 MΩ. This reduces the artifact-induced current recorded in voltage-clamp to something less than 5 pA for 50 MΩ cells, and much less for neurons with higher membrane resistance. In most cases, this is negligible.

For glass pipette-based LFP recordings, however, the light-induced voltage change (few hundred μV, as shown above) is of the same magnitude as a strong LFP signal (see for example figure 1 in Friedrich et al., 2004). Therefore, in order to measure a LFP signal in response to blue light-activated rhodopsins, one needs to take into account the artifacts induced by the photovoltaic effect. This can for example be done by measuring the light-evoked voltage change with the glass pipette both in the tissue and in the bath and subtracting the latter measurement from the previous one on a pipette-by-pipette basis.

I would also be curious about other reports (if there are any) on light-induced artifacts with recording electrodes and under which circumstances (if there are any) they might play a non-negligible role.

Posted in Data analysis, electrophysiology, Neuronal activity | Tagged , | 2 Comments

Open access 3D electron microscopy datasets of brains

One of the coolest technical developments in neuroscience during the last decade has been driven by 3D electron microscopy (3D EM). This allowed to cut large junks of small brains (or small junks of big brains) into 8-50 nm thick slices, which are then imaged with nanometer resolution, resulting in 3D stacks of imaged tissue. Here, I want to highlight some of those datasets which are easily accessible in the internet but, at least from my impression, under-used by other researchers.

But also the technical concepts and breakthroughs underlying 3D EM are very interesting. The three main approaches, serial block-face electron microscopy (SBEM), serial section transmission or scanning electron microscopy (ssTEM or ssSEM) and focused ion beam SEM (FIB-SEM) have been very nicely reviewed by a colleague of mine, Benjamin Titze, including some very beautiful and instructive figures (special recommendation for Fig. 4). Of course, this is only a part of the challenge: First, the brain tissue must be stained with heavy metals to be visible for electrons. Second, after data acquisition, human annotators or algorithms have to extract neuronal morphologies or synapse distributions from the huge datasets.

However, I find the raw 3D EM data very interesting by itself. Those datasets are still rare, but many people do not know that some of them are easily accessible to anyone with an internet connection. And it is a true pleasure to have the full screen filled with the overwhelming clutter of neuronal dendrites and to follow them in 3D just by scrolling with the mouse.

Neurodata.io is probably the best place to start. After a simple registration, one can directly access some of those EM datasets in the browser: ndwebtools.neurodata.io/coll_list, or through other tools. Not all of the datasets are of the highest quality (and it is not always easy to judge data quality for a lay person), but most of them offer highly interesting views into the complexity of the brain (scroll wheel for going through the slice, ctrl + scroll wheel for zooming). Here I want to highlight a few of them. They can be accessed by clicking on the neurodata/ndwebtools link above.

The following excerpt by Lee et al. (2016) shows a small zoom-in into the somato-sensory cortex in mouse. A thick dendrite (between the red arrows) is passing vertically through the image. In this ssSEM datasets, synapses look really nice (yellow arrow, with a beautiful vesicle cloud below), but they look even nicer in 3D, so you should have a look at the 3D data yourself.


The following picture from a dataset from the Cardona lab shows a small zoom-in of the drosophila brain. (I assume that the scale bar generated for this dataset is a bit off; the 100 nm shown here probably correspond to 500 nm.) The red arrow highlights a filament of the cytoskeleton, probably a microtubule in charge of transport along the dendrite. The pink arrow indicates one of the many mitochondria with its cristae. The yellow arrow indicates a local darkening at the contact site between two neurites, and I have no idea what this is. A gap junction? A strange synapse? A precipitate, i.e., an artifact of the staining procedure?


In hippocampus CA1, things look very similar, in a ssTEM dataset used by Bloss et al. (2018). This study focuses on clustering of synapses from single axons. Axons can easily be recognized by their dark and thick myelin sheath (red arrows). If you have a lot of time, you can scroll through the dataset and try to find a node of Ranvier. – As in almost every 3D EM dataset, there are planes or entire regions with low quality staining or low signal to noise imaging or something else that went wrong. Sometimes this is very local, just a blurring of boundaries (yellow arrows) that is difficult to interpret.


And here is a zoomed-out view of a single plane of a dataset by Wanner et al. (2016) of the olfactory bulb of larval zebrafish. Here, the large roundish shapes are not cross-sections of dendrites, but neuronal somata:


I just want to encourage people to browse through these datasets. Browsing in 3D is much more interesting than watching these still images. – Or if you are teaching students about neuroscience, why not send them a link such that they can discover neurons themselves by scrolling and zooming through the brains? I haven’t seen many people who were not fascinated when first encountering 3D EM data and who were not overwhelmed by the sheer amount of dendritic arborizations! (And this is a bit funny, if we keep in mind that electron microscopy does not see much of the more complex level of cells, the crowded microenvironment, which is a chaos of competing, interacting, diffusing little protein machines.)

As an alternative to neurodata.io that is accessible even without any registration, a couple of test datasets are available with neuroglancer, a rendering software developed by Google. Check out the dataset from Takemura et al. (2015) by following this link. It is an isotropically resolved dataset (8 nm in x, y and z). You can use the scroll wheel and ctrl to browse through the stack or to zoom in and out. The software includes three EM viewports and an additional rendering of a number of selected neurons.

Another way to explore 3D EM data is to go to eyewire.org, where one can discover 3D EM datasets of neurons (retina, based on Briggman et al., 2011) within the framework of a game – which is fun. Over the last couple of years, the user interface has become very pleasant. The downside compared to the other options is that one cannot discover freely in a big dataset; plus, there is no labeling of the inner organelles or vesicles of the neurons, which is part of the fun for the other datasets.

To understand more details about these EM images, I found it interesting to go through the first chapter of the book Dendrites (“Dendritic structure”), which can accessed almost to its full extent via Google Books.

Full disclosure: my current host lab is working on 3D EM data in zebrafish. My own projects do not involve electron microscopy directly.

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How well do CNNs for spike detection generalize to unseen datasets?

Some time ago, Stephan Gerhard and I have used a convolutional neural network (CNN) to detect neuronal spikes from calcium imaging data. (I have mentioned this before, here, here, and on Github.)

This method is covered by the spikefinder paper that was recently published (Berens et al., 2018), based on a competition that featured ground truth for a training and a test dataset. This was a great and useful competition. But there are some important caveats (which are mentioned in the discussion of the main paper). Here, I will discuss one of the caveats.

Continue reading

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