#Neuroscience
Neuroscience is where most of my current research life is centered, and where many of the questions that occupy me on a daily basis originate. Unsurprisingly, a large part of what I write on this blog grows out of that work. Posts tagged this way explore neural systems from experimental and theoretical angles alike, ranging from behavior and imaging to circuit dynamics and plasticity. The posts often cover what I have newly learned about how brains function, how neural data can be analyzed, and how computational models can help us understand neural phenomena. Much of this inevitably overlaps with computational approaches, but the focus here lies on the biological phenomena themselves, on how brains organize activity, adapt over time, and give rise to perception and memory.
There are currently 40 articles with this tag (newest first):
Distinguishing correlation from the coefficient of determination: Proper reporting of r and R²
I noticed that people sometimes report R² (‘R-squared’) instead of the Pearson correlation coeffi...
Rate models as a tool for studying collective neural activity
Rate models provide simplified representations of neural activity in which the precise spike timi...
On the role of gap junctions in neural modelling: Network example
As a follow-up to our previous post on gap junctions, we will now explore how gap junctions can b...
On the role of gap junctions in neural modelling
Gap junctions are specialized intercellular connections that facilitate direct electrical and che...
Shared dynamics, diverse responses: decoding decision-making in premotor cortex
Last week, I presented a recent study by Genkin et al., The dynamics and geometry of choice in th...
New teaching material: Functional imaging data analysis – From calcium imaging to network dynamics
We have just completed our new course, Functional Imaging Data Analysis: From Calcium Imaging to ...
Astrocytes enhance plasticity response during reversal learning
Astrocytes, a type of glial cell traditionally considered support cells in the brain, are now rec...
New teaching material: Dimensionality reduction in neuroscience
We just completed a new two-day course on Dimensionality Reduction in Neuroscience, and I am plea...
Long-term potentiation (LTP) and long-term depression (LTD)
Both long-term potentiation (LTP) and long-term depression (LTD) are forms of synaptic plasticity...
Bienenstock-Cooper-Munro (BCM) rule
The Bienenstock-Cooper-Munro (BCM) rule is a cornerstone in theoretical neuroscience, offering a ...
Campbell and Siegert approximation for estimating the firing rate of a neuron
The Campbell and Siegert approximation is a method used in computational neuroscience to estimate...
New preprint: Breaking new ground in brain imaging with three-photon microscopy
Our new preprint on Three-photon in vivo imaging of neurons and glia in the medial prefrontal cor...
Exponential (EIF) and adaptive exponential Integrate-and-Fire (AdEx) model
The exponential Integrate-and-Fire (EIF) model is a simplified neuronal model that captures the e...
Olfactory processing via spike-time based computation
In their work ‘Simple Networks for Spike-Timing-Based Computation, with Application to Olfactory ...
Frequency-current (f-I) curves
In this short tutorial, we will explore the concept of frequency-current (f-I) curves exemplified...
What are alpha-shaped post-synaptic currents?
In some recent posts, we have applied a specific type of integrate-and-fire neuron model, the iaf...
Example of a neuron driven by an inhibitory and excitatory neuron population
In this tutorial, we recap the NEST tutorial ‘Balanced neuron example’. We will simulate a neuron...
Brunel network: A comprehensive framework for studying neural network dynamics
In his work from 2000, Nicolas Brunel introduced a comprehensive framework for studying the dynam...
Oscillatory population dynamics of GIF neurons simulated with NEST
In this tutorial, we will explore the oscillatory population dynamics of generalized integrate-an...
Izhikevich SNN simulated with NEST
In this post, we explore how easy it is to set up a large-scale, multi-population spiking neural...
Connection concepts in NEST
In the previous post, we learned about the basic concepts of the NEST simulator and how to create...
Step-by-step NEST single neuron simulation
While NEST is designed for large-scale simulations of neural spike networks, the underlying model...
NEST simulator – A powerful tool for simulating large-scale spiking neural networks
The NEST simulator is a powerful software tool designed for simulating large-scale networks of sp...
Simulating spiking neural networks with Izhikevich neurons
The Izhikevich neuron model that we have discussed earlier is known for its simplicity and comput...
Izhikevich model
Computational neuroscience utilizes mathematical models to understand the complex dynamics of neu...
Hodgkin-Huxley model
An important step beyond simplified neuronal models is the Hodgkin-Huxley model. This model is ba...
FitzHugh-Nagumo model
In the previous post, we analyzed the dynamics of Van der Pol oscillator by using phase plane an...
Understanding Hebbian learning in Hopfield networks
Hopfield networks, a form of recurrent neural network (RNN), serve as a fundamental model for und...
Integrate and Fire Model: A simple neuronal model
In this post we explore the Integrate-and-Fire model, a simplified representation of a neuron. We...
Assessing animal behavior with machine learning: New DeepLabCut tutorial
I have added a hands-on tutorial to the Assessing Animal Behavior lecture. The tutorial covers th...
Assessing animal behavior with machine learning
High-throughput and multi-modal behavior experiments, coupled with machine learning analysis, unl...
Bioimage analysis with Napari
I’ve added new teaching material on using the free and open-source software (FOSS) Napari for bio...
New publication on Tauopathy
A new study on Tauopathy in which our lab was involved has just been published.
Mutual information and its relationship to information entropy
Mutual information is an essential measure in information theory that quantifies the statistical ...
Information entropy
A fundamental concept that plays a pivotal role in quantifying the uncertainty or randomness of a...
How to read patch clamp recordings in WaveMetrics IGOR binary files (ibw) in Python
This is a mini tutorial on how to read patch clamp recordings in WaveMetrics IGOR binary files (*...
New Teaching Material: Python Cheat Sheets
I’ve started a collection of various Python cheat sheets that contain some useful and commonly us...
New Teaching Material: Statistical data analysis and basic time series analysis with Python
I’ve added two new tutorials in the teaching section on statistical data analysis and basic time ...
New Teaching Material: Analyzing IGOR binary files of patch clamp recordings
I’ve added a new tutorial in the teaching section on how to read and process IGOR binary files (i...
New Teaching Material: Fiji short course
There is a new tutorial in the Teaching Material. It’s a short Fiji tutorial on analyzing biomedi...