{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["3(12)"],"submitter":["Ali A"],"pubmed_abstract":["Predictive coding is a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modeling to demonstrate that such architectural hardwiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimize their energy consumption while operating in predictive environments, the networks self-organize into prediction and error units with appropriate inhibitory and excitatory interconnections and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down-driven predictions, we demonstrate, via virtual lesioning experiments, that networks perform predictions on two timescales: fast lateral predictions among sensory units and slower prediction cycles that integrate evidence over time."],"journal":["Patterns (New York, N.Y.)"],"pagination":["100639"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9768680"],"repository":["biostudies-literature"],"pubmed_title":["Predictive coding is a consequence of energy efficiency in recurrent neural networks."],"pmcid":["PMC9768680"],"pubmed_authors":["Johannes van Gerven MA","de Groot E","Ali A","Ahmad N","Kietzmann TC"],"additional_accession":[]},"is_claimable":false,"name":"Predictive coding is a consequence of energy efficiency in recurrent neural networks.","description":"Predictive coding is a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modeling to demonstrate that such architectural hardwiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimize their energy consumption while operating in predictive environments, the networks self-organize into prediction and error units with appropriate inhibitory and excitatory interconnections and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down-driven predictions, we demonstrate, via virtual lesioning experiments, that networks perform predictions on two timescales: fast lateral predictions among sensory units and slower prediction cycles that integrate evidence over time.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-25T18:47:31.033Z","creation":"2025-04-06T07:42:12.609Z"},"accession":"S-EPMC9768680","cross_references":{"pubmed":["36569556"],"doi":["10.1016/j.patter.2022.100639"]}}