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Tag Archives: deep learning
Online spike rate inference with Cascade
To infer spike rates from calcium imaging data for a time point t, knowledge about the calcium signal both before and after time t is required. Our algorithm Cascade (Github) uses by default a window that is symmetric in time … Continue reading
Temporal dispersion of spike rates from deconvolved calcium imaging data
On Twitter, Richie Hakim asked whether the toolbox Cascade for spike inference (preprint, Github) induces temporal dispersion of the predicted spiking activity compared to ground truth. This kind of temporal dispersion had been observed in a study from last year … Continue reading
Annual report of my intuition about the brain (2020)
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
Posted in Data analysis, machine learning, Network analysis, Neuronal activity, Reviews
Tagged complexity, deep learning, evolution, self-organization
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Simultaneous calcium imaging and extracellular recording from the same neuron
Calcium imaging is a powerful method to record from many neurons simultaneously. But what do the recorded signals really mean? This question can only be properly addressed by experiments which record both calcium signals and action potentials from the same … 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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