  Living organisms must actively maintain themselves in order to continue
existing. Autopoiesis is a key concept in the study of living organisms, where
the boundaries of the organism is not static by dynamically regulated by the
system itself. To study the autonomous regulation of self-boundary, we focus on
neural homeodynamic responses to environmental changes using both biological
and artificial neural networks. Previous studies showed that embodied cultured
neural networks and spiking neural networks with spike-timing dependent
plasticity (STDP) learn an action as they avoid stimulation from outside. In
this paper, as a result of our experiments using embodied cultured neurons, we
find that there is also a second property allowing the network to avoid
stimulation: if the agent cannot learn an action to avoid the external stimuli,
it tends to decrease the stimulus-evoked spikes, as if to ignore the
uncontrollable-input. We also show such a behavior is reproduced by spiking
neural networks with asymmetric STDP. We consider that these properties are
regarded as autonomous regulation of self and non-self for the network, in
which a controllable-neuron is regarded as self, and an uncontrollable-neuron
is regarded as non-self. Finally, we introduce neural autopoiesis by proposing
the principle of stimulus avoidance.
