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Tag Archives: machine learning
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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The most interesting machine learning AMAs on Reddit
It is very clear that Reddit is part of the rather wild zone of the internet. But especially for practical questions, Reddit can be very useful, and even more so for anything connected to the internet or computer technology, like machine … Continue reading
Posted in Data analysis, machine learning
Tagged deep learning, machine learning, Python, theoretical neuroscience
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How deconvolution of calcium data degrades with noise
How does the noisiness of the recorded calcium data affect the performance of spiking-inferring deconvolution algorithms? I cannot offer a rigorous treatment of this question (Update August 2020: Now I have treated this question rigorously.) , but some intuitive examples. … Continue reading
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
