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To formalize the neural substrates of self-referential cognition, the authors implemented a 10,000-neuron Izhikevich spiking neural network partitioned into a sensory processing layer and an intrinsically bursting Default Mode Network (DMN) pacemaker layer driven by tonic neuromodulatory currents. Mechanistically grounding self-sustaining baseline activity addresses a longstanding barrier in modeling autonomous internal states and artificial consciousness. Simulations reveal that hierarchically dominant top-down projections from the pacemaker subsystem preserve stable, autonomous bioelectric rhythms against transient sensory perturbations, yielding a persistent dynamical representation of "Self."
Autonomous internal representations do not require complex meta-cognitive architectures; an intrinsically bursting pacemaker subsystem coupled with hierarchical top-down connectivity is sufficient to sustain an invariant baseline "self" through external sensory disruptions.
Understanding the neurobiological mechanisms underlying self-referential cognition and baseline self-consciousness remains a fundamental challenge in computational neuroscience. In this work, we propose a large-scale computational model incorporating a 10,000-neuron spiking neural network (SNN) based on Izhikevich dynamics. The network is structured into two interacting subsystems: a sensory processing layer (5,000 regular-spiking cortical neurons) and an endogenous Default Mode Network (DMN) pacemaker subsystem (5,000 intrinsically bursting neurons). The DMN layer is modulated by continuous tonic currents reflecting ascending brainstem neuromodulation, maintaining intrinsic, autonomous bioelectric rhythms independent of external sensory input. To represent top-down cognitive modulation, synaptic weights are hierarchically structured such that DMN-to-network projections exceed sensory-level connections. Through numerical simulations using a modified two-step Euler integration scheme, we demonstrate how endogenous pacemaker activity interacts with transient external sensory perturbations, providing an elementary mathematical framework for the emergence of a persistent, self-sustaining neural representation of"Self".