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Effects of noise and metabolic cost on cortical task representations
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
cognition
dynamical systems
neuroscience
prefrontal cortex
recurrent neural networks
rhesus macaque
General Neuroscience
General Biochemistry,Genetics and Molecular Biology
General Immunology and Microbiology
dynamical systems
neuroscience
prefrontal cortex
recurrent neural networks
rhesus macaque
General Neuroscience
General Biochemistry,Genetics and Molecular Biology
General Immunology and Microbiology
URI
https://hdl.handle.net/20.500.14018/26807
Abstract
Cognitive flexibility requires both the encoding of task-relevant and the ignoring of task-irrelevant stimuli. While the neural coding of task-relevant stimuli is increasingly well understood, the mechanisms for ignoring task-irrelevant stimuli remain poorly understood. Here, we study how task performance and biological constraints jointly determine the coding of relevant and irrelevant stimuli in neural circuits. Using mathematical analyses and task-optimized recurrent neural networks, we show that neural circuits can exhibit a range of representational geometries depending on the strength of neural noise and metabolic cost. By comparing these results with recordings from primate prefrontal cortex (PFC) over the course of learning, we show that neural activity in PFC changes in line with a minimal representational strategy. Specifically, our analyses reveal that the suppression of dynamically irrelevant stimuli is achieved by activity-silent, sub-threshold dynamics. Our results provide a normative explanation as to why PFC implements an adaptive, minimal representational strategy.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2025-01-21
Language
ISBN
Identifiers
10.7554/eLife.94961.2