title: What the success of brain imaging implies about the neural code.
abstract: The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of the neural code and ventral stream, as well as what can be successfully investigated with fMRI.
authors.affiliation: Experimental Psychology, University College London, London, United Kingdom.
authors.forename: Olivia
authors.identifier: 0000-0002-1891-0972
authors.initials: O
authors.lastname: Guest
authors.affiliation: Experimental Psychology, University College London, London, United Kingdom.|The Alan Turing Institute, London, United Kingdom.
authors.forename: Bradley C
authors.identifier: 0000-0002-7883-7076
authors.initials: BC
authors.lastname: Love
mesh_terms: D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D001931:Brain Mapping; D002415:Cats; D004285:Dogs; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*
keywords: BOLD response; convolutional neural network; deep artificial neural network; human; neural code; neural plausibility; neuroscience; ventral stream