Category Archives: Neuronal activity

Annual report of my intuition about the brain

There are not many incentives for young neuroscientists to think aloud about big questions. Due to lack both of knowledge and authority, discussing very broad questions like how the brain works risks to be embarrassing at best. Still, I feel … Continue reading

Posted in Calcium Imaging, electrophysiology, machine learning, Neuronal activity | Tagged , , , , | 1 Comment

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 … Continue reading

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 … Continue reading

Posted in Calcium Imaging, Data analysis, electrophysiology, Network analysis, Neuronal activity, zebrafish | Tagged , , , , | 1 Comment

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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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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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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The basis of feature spaces in deep networks

In a new article on Distill, Olah et al. write up a very readable and useful summary of methods to look into the black box of deep networks by feature visualization. I had already spent some time with this topic … Continue reading

Posted in machine learning, Network analysis, Neuronal activity | Tagged , , , , | 2 Comments