Population codes of prior knowledge learned through environmental regularities

Abstract How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Human...

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Autores principales:	Silvan C. Quax, Sander E. Bosch, Marius V. Peelen, Marcel A. J. van Gerven
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/070e29e0febd449ab4cfcbe68bdf522a
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spelling	oai:doaj.org-article:070e29e0febd449ab4cfcbe68bdf522a2021-12-02T14:12:07ZPopulation codes of prior knowledge learned through environmental regularities10.1038/s41598-020-79366-z2045-2322https://doaj.org/article/070e29e0febd449ab4cfcbe68bdf522a2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79366-zhttps://doaj.org/toc/2045-2322Abstract How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings, clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.Silvan C. QuaxSander E. BoschMarius V. PeelenMarcel A. J. van GervenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Silvan C. Quax Sander E. Bosch Marius V. Peelen Marcel A. J. van Gerven Population codes of prior knowledge learned through environmental regularities
description	Abstract How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings, clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.
format	article
author	Silvan C. Quax Sander E. Bosch Marius V. Peelen Marcel A. J. van Gerven
author_facet	Silvan C. Quax Sander E. Bosch Marius V. Peelen Marcel A. J. van Gerven
author_sort	Silvan C. Quax
title	Population codes of prior knowledge learned through environmental regularities
title_short	Population codes of prior knowledge learned through environmental regularities
title_full	Population codes of prior knowledge learned through environmental regularities
title_fullStr	Population codes of prior knowledge learned through environmental regularities
title_full_unstemmed	Population codes of prior knowledge learned through environmental regularities
title_sort	population codes of prior knowledge learned through environmental regularities
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/070e29e0febd449ab4cfcbe68bdf522a
work_keys_str_mv	AT silvancquax populationcodesofpriorknowledgelearnedthroughenvironmentalregularities AT sanderebosch populationcodesofpriorknowledgelearnedthroughenvironmentalregularities AT mariusvpeelen populationcodesofpriorknowledgelearnedthroughenvironmentalregularities AT marcelajvangerven populationcodesofpriorknowledgelearnedthroughenvironmentalregularities
_version_	1718391826016108544

Population codes of prior knowledge learned through environmental regularities

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