Theoretical Neuroscience Podcast
Gaute Einevoll
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S1E41 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…
S1E40 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…
S1E39 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…
S1E38 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…
S1E37 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…
S1E36 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…
S1E35 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…
S1E34 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…
S1E33 On computational neurotechnology for the clinic - with Anthony Burkitt, Nada Yousif & Esra Neufeld - #33
How can computational neuroscience contribute to developing neurotechnology to help people with brain disorders and disabilities? This was the topic of a panel debate I hosted at the 34th Annual…
S1E32 On IIT and adversarial testing of consciousness theories - with Christof Koch - #32
In an adversarial collaboration researchers with opposing theories jointly investigate a disputed topic by designing and implementing a study in a mutually agreed unbiased way. Results from…
S1E31 On how to cure brain diseases - with Nicole Rust - #31
A promise of basic neuroscience research is that the new insights will lead to new cures for brain diseases. But has that happened so far? Today's guest, an accomplished professor of neuroscience,…
S1E29 On co-dependent excitatory and inhibitory plasticity - with Tim Vogels - #30
Synaptic plasticity underlies several key brain functions including learning, information filtering and homeostatic regulation of overall neural activity. While several mathematical rules have been…
S1E29 On the philosophy of simplification in computational neuroscience - with Mazviita Chirimuuta and Terrence Sejnowski - #29
Computational neuroscientists rely on simplification when they make their models. But what is the right level of simplification? When should we, for example, use a biophysically detailed model and…
S1E28 On whole-cell modeling of bacteria - with Markus Covert - #28
A future computational neuroscience project could be to model not only the signal processing properties of neurons, but also all processes that keep a neuron alive for, say, a 100-year life span. In…
S1E27 On construction and clinical use of multipurpose neuron models - with Etay Hay - #27
Numerous neuron models have been made, but most of them are "single-purpose" in that they are made to address a single scientific question. In contrast, multipurpose neuron models are made to be used…
S1E26 On the population code in visual cortex - with Kenneth Harris - #26
With modern electrical and optical measurement techniques, we can now measure neural activity in hundreds or thousands of neurons simultaneously. This allows for the investigation of population…
S1E25 On growing synthetic dendrites – with Hermann Cuntz - #25
The observed variety of dendritic structures in the brains is striking. Why are they so different, and what determine the branching patterns? Following the dictum "if you understand it, you can build…
S1E24 On neuroscience foundation models - with Andreas Tolias - #24
The term "foundation model" refers to machine learning models that are trained on vast datasets and can be applied to a wide range of situations. The large language model GPT-4 is an example. The…
S1E23 On human whole-brain models - with Viktor Jirsa - #23
A holy grail of the multiscale approach for physical brain modelling is to link the different scales from molecules, via cells and local neural networks, up to whole-brain models. The goal of the…
S1E22 On 40 years with the Hopfield network model - with Wulfram Gerstner - #22
In 1982 John Hopfield published the paper "Neural networks and physical systems with emergent collective computational abilities" describing a simple network model functioning as an associative and…
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Theoretical Neuroscience Podcast has published 41 episodes since October 2023, covering topics in Life Sciences, Physics.
Theoretical Neuroscience Podcast is currently highly active with new episodes monthly. Average episode length is 1h 37m.
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