The “free energy principle” provides an account of sentience in terms of active inference. Physics studies the properties that self-organising systems require to distinguish themselves from their lived world. Neurobiology studies functional brain architectures. Biological self-organization is an inevitable emergent property of any dynamical system. If a system can be differentiated from its external milieu, its internal and external states must be conditionally independent, inducing a “Markov blanket” separating internal and external states. This equips internal states with an information geometry providing probabilistic “beliefs” about external states. Bayesian belief updating can be demonstrated in the context of communication using simulations of birdsong. This “free energy” is optimized in Bayesian inference and machine learning (where it is known as an evidential lower bound). Internal states will appear to infer—and act on—their world to preserve their integrity.

 

References

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Salvatori, T., Mali, A., Buckley, C. L., Lukasiewicz, T., Rao, R. P., Friston, K., & Ororbia, A. (2023). Brain-inspired computational intelligence via predictive coding. arXiv preprint arXiv:2308.07870. 
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