Theoretical Neuroscience Podcast – Détails, épisodes et analyse

The most prominent visual characteristic of neurons is their dendrites.

Even more than 100 years after their first observation by Cajal, their function is not fully understood. Biophysical modeling based on cable theory is a key research tool for exploring putative functions, and today’s guest is one the leading researchers in this field.  

We talk about of passive and active dendrites, the kind of filtering of synaptic inputs they support, the key role of synapse placements, and how the inclusion of dendrites may facilitate AI.    

The greatest mystery of all is why a group of atoms, like the ones constituting me, can feel anything.  The mind-brain problem has puzzled philosophers for millennia.

Thanks to pioneers like Christof Koch, consciousness studies have recently become a legitimate field of scientific inquiry. 

 In this vintage episode, recorded in February 2021, we discuss many aspects of the phenomenon, including an intriguing candidate theory: Integrated Information Theory.

Over the last decade topological analysis has been established as a new tool for analysis of spiking data.

Today’s guest has been a pioneer in adapting this mathematical technique for use in our field and explains concepts and example applications. 

We also also talk about so-called threshold-linear network model, a generalization of Hopfield networks exhibiting a much richer dynamics, where Carina has done some exciting mathematical explorations

Not all interesting network activity occurs in cortex. Networks in the spinal cord, the long thin tubular structure extending downwards from the neck, is responsible for setting up rhythmic motor activity needed for moving around.

How do these so-called central pattern generators work?

Today’s guest has, together with colleagues in Copenhagen, developed a neuron-based network theory for how these rhythmic oscillations may arise even without pace-maker neurons driving the collective. 

We know a lot about of how neurons in the primary visual cortex (V1) of mammals respond to visual stimuli.

But how does the vast information contained in the spiking of millions of neurons in V1 give rise to our visual percepts? 

The guest’s theory is that V1 acts as a “saliency detector” directing the gaze to the most important object in the visual scene. Then V1 in collaboration with higher visual areas determines what this object is in an iterative feedforward-feedback loop. 

A key goal of computational neuroscience is to build mathematical models linking single-neuron activity to systems-level activity.

The guest has taken some bold steps in this direction by developing and exploring a multi-area model for the macaque visual cortex, and later also a model for the human cortex, using millions of simplified spiking neuron models.  

We discuss the many design choices, the challenge of running the models, and what has been learned so far.

It is widely thought that spikes (action potentials) are the main carrier of information in the brain.

But what is the neural code, that is, what aspects of the spike trains carry the information? The detailed temporal structure or maybe only the average firing rate?  And is there information in the correlation between spike trains in populations of similar neurons?  

The guest has thought about these and other coding questions throughout his career.

Starting from the pioneering work of Hodgkin, Huxley and Rall in the 1950s and 60s, we have a well-founded biophysics-based mathematical understanding of how neurons integrate signals from other neurons and generate action potentials.

Today’s guest wrote the classic book “Biophysics of Computation” on the subject in 1998.

We discuss its contents, what has changed in the last 25 years, and also touch on his other main research interest: consciousness research.  

The book “Models of the Mind” published in 2021 gives an excellent popular account of the history and questions of interest in theoretical neuroscience.

I could think of no other person more suitable to invite for the inaugural episode of the podcast than its author Grace Lindsay.

In the podcast we discuss highlights from the book as well as recent developments and the future of our field.  

Computational neuroscientists use many software tools, and NEURON has become the leading tool for biophysical modeling of neurons and neural network.

Today’s guest has been the leading developer of NEURON since the infancy almost 50 years ago.

We talk about how the tool got started and the development up until today’s modern version of the software, including CoreNEURON optimized for parallel execution of large-scale network models on multicore supercomputers.