The nature of the self is traditionally modeled as a persistent internal state, memory struc-ture, or representational object. However, empirical phenomena such as the loss and recoveryof subjectivity during deep sleep and general anesthesia challenge models that require contin-uous state preservation. In this work, we propose and computationally validate an alternativehypothesis: the self is not a stored entity but an emergent adaptive filtering process that arisesduring ongoing sensory interaction.Using spiking neural network (SNN) models implemented in Brian2, we demonstrate thatself-like functional properties emerge selectively in response to temporally correlated inputsand collapse under decorrelated stimulation. Through a series of controlled computationalexperiments, we show that (i) organized network dynamics depend on temporal correlationrather than signal energy, (ii) apparent memory effects arise from altered adaptive dynamicsrather than stored representations, and (iii) functional identity can be restored after completeloss of activity, despite the absence of preserved instantaneous state.We further introduce a formal decomposition of system state into fast and slow components,providing a principled account of how self-like behavior can disappear and re-emerge whilemaintaining functional continuity. The results support a view of the self as a transient, input-dependent adaptive filter rather than a persistent internal object.
Andrejs Bistricenko (Wed,) studied this question.