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# Tag Archives: machine learning

## 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

## 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 Data analysis, machine learning, Matlab
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## Deep learning, part IV (2): Compressing the dynamic range in raw audio signals

In a recent blog post about deep learning based on raw audio waveforms, I showed what effect a naive linear dynamic range compression from 16 bit (65536 possible values) to 8 bit (256 possible values) has on audio quality: Overall perceived quality … Continue reading

## Deep learning, part IV: Deep dreams of music, based on dilated causal convolutions

As many neuroscientists, I’m also interested in artificial neural networks and am curious about deep learning networks. I want to dedicate some blog posts to this topic, in order to 1) approach deep learning from the stupid neuroscientist’s perspective and 2) to get a feeling … Continue reading

Posted in machine learning
Tagged deep learning, machine learning, recurrent networks, theoretical neuroscience
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## Deep learning, part III: understanding the black box

## Deep learning, part II : frameworks & software

## Deep learning, part I

As many neuroscientists, I’m also interested in artificial neural networks and am curious about deep learning networks, which have gained a lot of public attention in the last couple of years. I’m not very familiar with machine learning, but I want to dedicate some blog … Continue reading