7 episodes

The podcast focuses on topics in theoretical/computational neuroscience and is primarily aimed at students and researchers in the field.

Theoretical Neuroscience Podcast Gaute Einevoll

    • Science

The podcast focuses on topics in theoretical/computational neuroscience and is primarily aimed at students and researchers in the field.

    On topological data analysis and Hopfield-like network models - with Carina Curto - #7

    On topological data analysis and Hopfield-like network models - with Carina Curto - #7

    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

    • 2 hrs 14 min
    On central pattern generators in the spinal cord - with Henrik Lindén - #6

    On central pattern generators in the spinal cord - with Henrik Lindén - #6

    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. 

    • 1 hr 26 min
    On how vision works - with Li Zhaoping - #5

    On how vision works - with Li Zhaoping - #5

    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. 

    • 1 hr 21 min
    On multi-area cortex models - with Sacha van Albada - #4

    On multi-area cortex models - with Sacha van Albada - #4

    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.

    • 1 hr 32 min
    On the neural code - with Arvind Kumar - #3

    On the neural code - with Arvind Kumar - #3

    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.

    • 1 hr 25 min
    On biophysics of computation – with Christof Koch - #2

    On biophysics of computation – with Christof Koch - #2

    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.  

    • 1 hr 20 min

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