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.

神经科学努力提供关于动物的神经系统如何产生行为和认知状态（如意识）的解释，人工智能和机器学习则努力提供越来越好的预测模型。本研究提出了模拟器理论和神经模拟器作为生物脑活动的电路和规模无关的预测模型，无需详细的机制解释即可实现与模型化的生物有别无法区分的行为和认知状态，为神经动力学和行为的基于预测的模型提供概念和实证框架。