A Spiking Neural Network Model of Elementary Self-Consciousness via Endogenous Default Mode Network Dynamics
The episode discusses a paper modeling elementary self-consciousness using a large spiking neural network and Default Mode Network dynamics. Hosts discuss how internal rhythms generate autonomous activity, how this creates a persistent 'Self' representation, and suggestions for future improvements like incorporating Spike-Timing-Dependent Plasticity to model learning and porting the model to neuromorphic hardware.