Bayesian encoding and decoding as distinct perspectives on neural coding

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated diff...

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Vydané v:	Nature neuroscience Ročník 26; číslo 12; s. 2063 - 2072
Hlavní autori:	Lange, Richard D., Shivkumar, Sabyasachi, Chattoraj, Ankani, Haefner, Ralf M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.12.2023 Nature Publishing Group
Predmet:	631/114/116/2394 631/114/116/2395 631/378/3917 Animal Genetics and Genomics Approximation Bayes Theorem Bayesian analysis Behavioral Sciences Biological Techniques Biomedical and Life Sciences Biomedicine Brain Circuits Coding Decoding Hypotheses Inference Mathematical models Models, Neurological Neural coding Neural networks Neurobiology Neurosciences Perspective Probability Variables
ISSN:	1097-6256, 1546-1726, 1546-1726
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Popis
Shrnutí:	The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain. This paper characterizes two distinct philosophies underlying previous work on how Bayesian computations are linked to neural data, highlighting how different theories may be motivated by different tacit assumptions and thereby explain different data.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 These authors contributed equally.
ISSN:	1097-6256 1546-1726 1546-1726
DOI:	10.1038/s41593-023-01458-6