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Tag Archives: deep learning
Accurately computing noise levels for calcium imaging data
It is fascinating how much data quality can vary between different calcium imaging data sets. In this blog post, I will discuss a metric to quantify and compare data quality and in particular shot noise between calcium imaging datasets. This … Continue reading
Open PhD position in my research group
Are you a finishing Master’s student with a quantitative background and are interested in neuroscience? This is your opportunity. Project: You will be supervised by Dr. Peter Rupprecht and Prof. Fritjof Helmchen at the Brain Research Institute, University of Zurich. … Continue reading
Online spike inference with GCaMP8
Calcium imaging is used to record the activity of neurons in living animals. Often, these activity patterns are analyzed after the experiments to investigate how the brain works. Alternatively, it is also possible to extract the activity patterns in real … Continue reading
Detecting single spikes from calcium imaging
There are two mutually exclusive holy grails of calcium imaging: First, recording from the highest number of neurons simultaneously. Second, detecting spike patterns with single-spike precision. This blog post focuses on the latter. Many studies have claimed to demonstrate single-spike … Continue reading
Non-linearity of calcium indicators: history-dependence of spike reporting
Calcium indicators are used to report the calcium concentration inside single cells. In neurons, calcium imaging can be used as a readout of neuronal activity (action potentials). However, some calcium indicators like GCaMP transform the calcium concentration of a cell … Continue reading
Spike inference with GCaMP8: new pretrained models available
Calcium imaging is only an indirect readout of neuronal activity via fluorescence signals. To estimate the true underlying firing rates of these neurons, methods for “spike inference” have been developed. They are useful to denoise calcium imaging data and make … Continue reading
A collaborative review on error signals in predictive processing
Predictive processing is one of the most influential ideas from computational neuroscience for the experimental neurosciences. However, definitions of predictive processing vary broadly, to the extent that “predictive coding” is used sometimes in a very narrow sense (there are specific … Continue reading
Three recent interesting papers on computational neuroscience
Three papers:
1. The Neuron as a Direct Data-Driven Controller
2. A learning algorithm beyond backpropagation
3. Continuous vs. discrete representations in a recurrent network Continue reading
Interesting papers on behavioral timescale synaptic plasticity (theory)
Behavioral timescale synaptic plasticity (BTSP) is a form of single-shot learning observed in hippocampal place cells in mice (Bittner et al., 2015, 2017). This finding is both interesting and inspiring for computational neuroscience for several reasons. In the first place, … Continue reading
Ambizione fellowship and an open PhD position
I’m glad to share that I am going to start my own junior research group at the University of Zurich in March 2023! As an Ambizione fellow, I will receive funding for my own salary, some equipment, consumables and a … Continue reading
A blog about neurophysiology 