Autoneurohoda

Autoneurohoda is a theoretical framework in neuroscience and bioengineering that seeks to describe a self-regulated neural architecture capable of coordinating autonomic and somatic neural signals through internally generated feedback. The term combines auto- (self) with neuro- (nerve) and hoda (a path or guided process), and it is used mainly in speculative discussions and some niche theoretical writings rather than as an established biological mechanism.

In its proposed conception, autoneurohoda envisions modular neural circuits that continuously monitor activity and metabolic state, compare outputs against internal setpoints, and adjust synaptic strengths, firing patterns, or neuromodulator release to maintain homeostasis or to optimize task performance. The framework aims to account for how organisms might achieve autonomous regulation without reliance on external controllers, and it has been extended to consider applications in neuroprosthetics or brain-computer interfaces that must operate in closed loops.

Research status is largely theoretical. Autoneurohoda is supported mainly by computational models and analogy with closed-loop neuromodulation; there is little to no direct empirical evidence establishing it as a distinct, biologically defined mechanism. Debates focus on definitional clarity, how to measure internal setpoints, and the extent to which such self-regulation can be separated from conventional autonomic or central control systems. Ethical and practical challenges arise when considering enhancement, device interfaces, or cross-species extrapolations.

See also: Autonomic nervous system; Neuromodulation; Brain-computer interface; Closed-loop control; Neural plasticity.