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# Tag Archives: Data analysis

## The power of correlation functions

During my physics studies, I got to know several mathematical tools that turned out to be extremely useful to describe the world and to analyze data, for example vector calculus, fourier analysis or differential equations. Another tool that I find … Continue reading

Posted in Calcium Imaging, Data analysis, electrophysiology
Tagged Data analysis, electrophysiology, Matlab, Microscopy, Python
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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 … Continue reading

Posted in Data analysis, electrophysiology, Neuronal activity
Tagged Data analysis, electrophysiology
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## 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 … Continue reading

Posted in Data analysis, machine learning, Microscopy, Network analysis, zebrafish
Tagged Data analysis, Microscopy, Network analysis, zebrafish
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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 … Continue reading

## A list of cognitive biases

There are a handful of cognitive biases that are well-known to most scientists: confirmation bias, the Dunning-Kruger effect, the hindsight bias, the recency effect, the planning fallacy, loss aversion, etc.. Although they should not be taken as universal laws (for example, … Continue reading

## Layer-wise decorrelation in deep-layered artificial neuronal networks

The most commonly used deep networks are purely feed-forward nets. The input is passed to layers 1, 2, 3, then at some point to the final layer (which can be 10, 100 or even 1000 layers away from the input). … Continue reading

Posted in Data analysis, machine learning, Neuronal activity
Tagged CNN, Data analysis, deep learning, machine learning, Network analysis, Python
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## A convolutional network to deconvolve calcium traces, living in an embedding space of statistical properties

As mentioned before (here and here), the spikefinder competition was set up earlier this year to compare algorithms that infer spiking probabilities from calcium imaging data. Together with Stephan Gerhard, a PostDoc in our lab, I submitted an algorithm based on convolutional networks. Looking … Continue reading