Tag Archives: Data analysis

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

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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

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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

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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

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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

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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

Posted in Calcium Imaging, Data analysis, electrophysiology, Imaging, machine learning, Neuronal activity | Tagged , , , , | 2 Comments

A short report from a Cold Spring Harbor lab course

One of the best things of being a PhD student is that one is supposed to learn new things. As part of this mission, I attended a two-week laboratory course in the Cold Spring Harbor Laboratories on ‘Advanced Techniques in … Continue reading

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Whole-cell patch clamp, part 3: Limitations of quantitative whole-cell voltage clamp

Before I first dived into experimental neuroscience, I imagined whole-cell voltage clamp recordings to be the holy grail of precision. Directly listening to the currents that take place inside of a living neuron! How beautiful and precise, compared to poor-resolution techniques like fMRI or … Continue reading

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Spike detection competition

The main drawback of functional calcium imaging is its slow dynamics. This is not only due to limited frame rates, but also due to calcium dynamics, which are a slow transient readout of fast spiking activity. A perfect algorithm would infer the … Continue reading

Posted in Calcium Imaging, Data analysis, machine learning | Tagged , , | 2 Comments

Neuroscience on Youtube

Recently, I’ve been to the Basel ICON conference, where the recent Nobel laureate Eric Betzig gave an impressive talk on microscopy techniques (including lattice light sheet, SIM and expansion microscopy). Some days ago, I found a similar talk by Eric … Continue reading

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