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Tag Archives: machine learning
Self-supervised denoising of calcium imaging data
This blog post is about algorithms based on deep networks to denoise raw calcium imaging movies. More specifically, I will write about the difficulties to interprete their outputs, and on how to address these limitations in future work. I will … Continue reading
Video introduction to CASCADE
A video with tutorial on CASCADE, a supervised method to infer spike rates from calcium imaging data. Continue reading
Annual report of my intuition about the brain (2021)
How does the brain work and how can we understand it? I want to make it a habit to report some of the thoughts about the brain that marked me most during the past twelve month at the end of … Continue reading
Annual report of my intuition about the brain (2019)
How does the brain work and how can we understand it? I want to make it a habit to report some of the thoughts about the brain that marked me most during the past twelve month at the end of … Continue reading
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
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 … Continue reading
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
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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Understanding style transfer
‘Style transfer’ is a method based on deep networks which extracts the style of a painting or picture in order to transfer it to a second picture. For example, the style of a butterfly image (left) is transferred to the … Continue reading
Posted in Data analysis, machine learning
Tagged deep learning, machine learning, Network analysis, Python
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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 CNN, deep learning, machine learning, Network analysis, Python
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A blog about neurophysiology 