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While some models aim to explain qualitative features of brain activity, other aim to reproduce experimental data quantitatively. If so, model parameters must be adjusted to make the model predictions fit the experimental data.
A complication is that in most neurobiological applications, there is not a unique best fit: many parameter combinations give equally good model fits.
Recently, the guest, together with colleagues, made the tool AutoMIND to fit spiking network models to data.
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Reproducibility is key for scientific progress. If research results cannot be reproduced and trusted, other researchers cannot build on them.
Reproducibility is a challenge also in computational neuroscience, and today's guest has worked on how this can be remedied, for example, through standardiz…
An important discovery that has come out of computational neuroscience, is that cortical neurons in vivo appear to receive so-called balanced inputs.
In the balanced state the excitatory and inhibitory synaptic inputs to a neuron are about equal, and action potentials occur when a fluctuation temp…
Historically, the analysis of neural recordings focused on responses of single neurons recorded by single-contact electrodes. Modern electrodes with multiple electrode contacts can instead record spikes (action potentials) from hundreds of neurons simultaneously.
Manifold analysis of the overall p…
Neurons need particular sodium and potassium concentration gradients across their membranes to function. These gradients are set up by so-called ion pumps which require energy stored in ATP molecules to run.
ATP is the common energy currency in the brain and is produced from nutrients delivered by…
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