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.

Highlights

  • Neuroimaging and especially functional magnetic resonance imaging has come a long way since the first experiments in the early 1990s

  • What kinds of models or computations are consistent with the success of functional magnetic resonance imaging (fMRI)? If the brain is a computing device, it would have to be of a particular type for fMRI to be useful given its limitations in measuring neural activity

  • One key question is whether functional smoothness breaks down at more advanced layers in Deep learning networks (DLNs) as it did in the untrained random neural networks considered in the previous section

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Summary

University of Oxford

The success of fMRI places constraints on the nature of the neural code. We evaluate a number of reasonable coding schemes and demonstrate that only a subset 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 neural code and ventral stream, as well as what can be successfully investigated with fMRI

Introduction
Smoothness and the Neural Code
Functional Smoothness
Neural Network Models
Level of Distortion
Deep Learning Networks
Deep Learning Network Simulation
Findings
Discussion
Full Text
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