Collective Spiking Dynamics in Cortical Networks

by Jens Wilting
Doctoral thesis
Date of Examination:2020-09-24
Date of issue:2020-10-14
Advisor:Dr. Viola Priesemann
Referee:Dr. Viola Priesemann
Referee:Prof. Dr. Florentin Wörgötter
Referee:Prof. Dr. Hansjörg Scherberger
Referee:Prof. Dr. Theo Geisel
Referee:Prof. Dr. Stefan Klumpp
Referee:Prof. Dr. Peter Sollich
Persistent Address: http://dx.doi.org/10.53846/goediss-8254

 

 

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Abstract

English

Even though information processing in the cortex most likely emerges from the collective interplay of billions of neurons, even basic properties of collective dynamics in cortical networks are still not known with certainty. In this dissertation, which is a collection of four published articles, we argue that this is likely because their assessment is hampered by spatial subsampling, i.e., the limitation that only a tiny fraction of all neurons can be recorded simultaneously with millisecond precision. We derive an estimator that allows to classify spreading dynamics even under strong subsampling. Building on this estimator, we identify that consistently for rat, cat, and monkey cortex operates in a "reverberating" regime, which allows input to reverberate in the network for hundreds of milliseconds. We present a framework how cortical networks can self-organize to this reverberating regime, or to input-driven and bursting states depending on the input to the network. Finally, we discuss how the reverberating regime can form the substrate for adaptive computation.
Keywords: propagation; collective dynamics; neuronal networks; cortex; criticality; reverberation; subsampling; self-organization; homeostatic plasticity
 
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