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Theoretical Neuroscience Podcast: Daily Summaries Delivered
by Gaute Einevoll
The podcast focuses on topics in theoretical/computational neuroscience and is primarily aimed at students and researchers in the field.
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- 1 weeks ago1h 29m
On functional effects of neuronal heterogeneity - with David Dahmen - #41
Most neural network models till date have assumed all neurons to be identical, or at least that all neurons within a population are identical. In reality, no two neurons are completely the same. Is this due to unavoidable "biological noise" that the nervous system has to cope with, or can it be a useful feature included by design? The guest co-wrote the recent paper "How heterogeneity shapes dynamics and computation in the brain" addressing this question.
- Apr 25, 20261h 25m
On smelling your way to the fruit with ring models - with Katherine Nagel - #40
Fruit flies need a short-term (working) memory to keep their direction when they navigate their way to the fruit by smelling. Mean-field ring models was theoretically suggested to encode stimulus orientations 30 years and was observed in fruit-fly compass neurons 10 years ago. But how does odor input come into the picture to set the compass course? The group of the guest has studied the question with a host of different experimental and theoretical methods.
- Mar 28, 20262h 18m
On modeling neural population activity with mean-field models - with Tilo Schwalger - #39
Starting with the work of pioneers like Wilson and Cowan in the 1970s, mean‑field models have become a dominant tool for modeling neural activity at the level of neuronal populations. Despite their popularity, most mean‑field models have been heuristic and not systematically derived from the underlying 'microscopic' dynamics of individual neurons. Today's guest has made important contributions towards remedying this situation.
- Feb 28, 20261h 35m
On extracting spiking network models from experiments - with Richard Gao - #38
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.
- Jan 31, 20261h 28m
On reproducibility of modeling and 10 years with the Potjans-Diesmann network model - with Hans Ekkehard Plesser - #37
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 standardized model description and model sharing. He also recently organised a workshop celebrating a decade with the (reproducible) Potjans-Diesmann neural network model, which has become an important community tool.
- Jan 3, 20262h 4m
On low-dimensional manifolds in motor cortex - with Sara Solla - #36
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 population activity of these neurons has become a critical tool for interpretation of such data. The podcast guest is a pioneer in the development and use of such analysis.
- Dec 6, 20251h 31m
On modeling metabolic networks in the brain – with Polina Shichkova - #35
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 the blood by a complicated set of chemical reactions known as a metabolic network. Today's guest has just published a comprehensive model of such a network and explains how it can shed light on differences between young and brains.
- Nov 8, 20251h 38m
On balanced neural networks - with Nicolas Brunel - #34
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 temporarily makes the excitation dominate. The theory, for example, explains the observed irregular firing of cortical neurons in the background state. Today's guest was one of the key developers of the theory in the late 1990s.
About Theoretical Neuroscience Podcast
The podcast focuses on topics in theoretical/computational neuroscience and is primarily aimed at students and researchers in the field.
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