Theoretical Neuroscience Podcast

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.

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.

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.

Sara Solla, a theoretical neuroscientist with a physics background, shares her journey from neural networks to pioneering manifold analyses of motor cortex activity. She explains how modern multi-electrode technology allows for population-level insights, revealing low-dimensional structures in motor tasks. Solla discusses the implications of these findings for brain-machine interfaces and how structured sensory inputs contribute to low-dimensional coding. Her experiences at Bell Labs and collaborations with notable figures add depth to the conversation, making complex concepts accessible.

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.

Nicolas Brunel, a computational neuroscientist renowned for his foundational work on balanced cortical networks, dives into the mechanics of how neurons receive balanced inputs. He discusses the importance of understanding spontaneous cortical activity and explores the balance of excitatory and inhibitory signals that leads to irregular firing. Brunel also connects this theory to practical implications in memory storage and coding efficiency, highlighting interesting parallels between neural dynamics and methods from physics while addressing potential breakdowns in balance linked to conditions like epilepsy.

In this engaging discussion, computational neuroscientists Anthony Burkitt, Nada Yousif, and Esra Neufeld explore the cutting-edge intersection of neurotechnology and clinical applications. They dive into the evolution of cochlear implants and the intricacies of subretinal visual prostheses. With insights on deep brain stimulation, Esra reveals how personalized modeling has accelerated spinal stimulation tuning. The trio also discusses the future of ultrasound neuromodulation and the importance of transparent models in regulatory safety. An enlightening dialogue on transforming patient care!

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.

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.

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.