  A central goal in neuroscience is to provide explanations for how animal
nervous systems can generate actions and cognitive states such as consciousness
while artificial intelligence (AI) and machine learning (ML) seek to provide
models that are increasingly better at prediction. Despite many decades of
research we have made limited progress on providing neuroscience explanations
yet there is an increased use of AI and ML methods in neuroscience for
prediction of behavior and even cognitive states. Here we propose emulator
theory (ET) and neural emulators as circuit- and scale-independent predictive
models of biological brain activity and emulator theory (ET) as an alternative
research paradigm in neuroscience. ET proposes that predictive models trained
solely on neural dynamics and behaviors can generate functionally
indistinguishable systems from their sources. That is, compared to the
biological organisms which they model, emulators may achieve indistinguishable
behavior and cognitive states - including consciousness - without any
mechanistic explanations. We posit ET via several conjectures, discuss the
nature of endogenous and exogenous activation of neural circuits, and discuss
neural causality of phenomenal states. ET provides the conceptual and empirical
framework for prediction-based models of neural dynamics and behavior without
explicit representations of idiosyncratically evolved nervous systems.
