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Theoretical Neuroscience Podcast

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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22 episodes
monthly
Avg 1 hr 38 min
English

Episodes 22

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S1 · E45
September 23 · 1 hr 41 min
On neuromorphic computing - with Mihai Petrovici - #45
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S1 · E44
August 26 · 1 hr 43 min
On how energy determines where proteins are produced in neurons - with Tatjana Tchumatchenko - #44
For neurons to function, the appropriate ion-channel proteins must be present where they are needed, including in distal dendrites and axon terminals. This requires energy, and the guest's group has developed a mechanistic mathematical model to investigate how neurons can reduce this cost by optimizing where proteins are synthesized within the cell—locally or at more distant sites. The model's predictions agree with experimental findings, suggesting that energy optimization is a fundamental operating principle of neurons.
July 25 · 1 hr 10 min
On the computational neuroscience legacy of Valentino Braitenberg - with Ad Aertsen - #43
The prominent and colorful neuroscientist Valentino Braitenberg was born 100 years ago. He co-founded the Max Planck Institute of Biological Cybernetics in Tübingen in Germany, where he made seminal contributions to neuroanatomy, synthetic psychology, and theories for cerebellar, fly vision and cortical function. He was celebrated at the recent Braitenberg*100 symposium which I attended together with today's guest. Ad Aertsen is an outstanding computational neuroscientist and worked with Braitenberg back in the days.
S1 · E42
June 20 · 1 hr 43 min
On neuronal identity and representational drift - with Timothy O'Leary - #42
A bursting neuron can maintain its firing-pattern identity throughout an animal's life, even though the ion-channel proteins underlying this identity are turned over on the timescale of days. Today's guest has proposed that neuronal identities are stored in the specific protein production rules, which are regulated by intracellular calcium signaling. And how can animals reliably perform a learned task for weeks, even when the underlying neural representation drifts over time, so-called representational drift?
S1 · E41
May 23 · 1 hr 29 min
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.
S1 · E40
April 25 · 1 hr 25 min
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.
S1 · E39
March 28 · 2 hr 18 min
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.
S1 · E38
February 28 · 1 hr 35 min
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.
S1 · E37
January 31 · 1 hr 28 min
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.
S1 · E36
January 3 · 2 hr 4 min
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.
S1 · E35
Dec 6, 2025 · 1 hr 31 min
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.
S1 · E34
Nov 8, 2025 · 1 hr 38 min
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.
S1 · E33
Oct 11, 2025 · 1 hr
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 Computational Neuroscience Meeting in Florence in July this year. Electric or magnetic recording and/or stimulation are key clinical tools for helping patients, and the three panelists have all used computational methods to aid this endeavor.
S1 · E32
Sep 13, 2025 · 2 hr 17 min
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 adversarial testing of two well-known theories for consciousness, Global Neuronal Workspace Theory (GNWT) and Integrated Information Theory (IIT), were presented earlier this year. In this podcast one of the proponents and developers of IIT describes this candidate theory, and also the design of, and results from, the adversarial study.
S1 · E31
Aug 16, 2025 · 2 hr 13 min
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, decided to investigate. Her book "Elusive cures: why neuroscience hasn't solved brain disorders - and how we can change that" came out this summer. Here she argues that we need to consider the brain as a complex adaptive system, not as a chain of dominos as in the typical linear thinking.
S1 · E29
Jul 19, 2025 · 1 hr 30 min
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 developed for plasticity both at excitatory and inhibitory synapses, it has been difficult to make such rules co-exist in network models. Recently the group of the guest has explored how co-dependent plasticity rules can remedy the situation and, for example, assure that long-term memories can be stored in excitatory synapses while inhibitory synapses assure long-term stability.
S1 · E29
Jun 21, 2025 · 1 hr 24 min
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 when a simplified abstract model when modelling neural dynamics? What are the problems of simplifying too much, or too little? This was the topic of the panel discussion between a science philosopher (MC), author of the recent book "The Brain Abstracted", and an experienced modeler (TS) at the FENS Regional Meeting in Oslo in June 2025.
S1 · E28
May 24, 2025 · 2 hr 4 min
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 2012 the group of the guest published the first such whole-cell model for a very simple bacterium (M. genitalia). In 2020 a model of the larger E. coli bacterium comprising 10.000 equations and 19.000 model parameters was presented. How are such models built, and what can they do?
S1 · E27
Apr 26, 2025 · 1 hr 13 min
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 to address many scientific questions. In 2011, the guest published a multipurpose rodent pyramidal-cell model which has been actively used by the community ever since. We talk about how such models are made, and how his group later built human neuron models to explore network dynamics in brains of depressed patients.
S1 · E26
Mar 29, 2025 · 1 hr 24 min
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 codes, that is, of how groups of neurons together encode information. In 2019 today's guest published a seminal paper with collaborators at UCL in London where analysis of optophysiological data from 10.000 neurons in mouse visual cortex revealed an intriguing population code balancing the needs for efficient and robust coding. We discuss the paper and (towards the end) also how new AI tools may be a game-changer for neuroscience data analysis.

Showing 1–20 of 22 episodes