Figure 2. | What the success of brain imaging implies about the neural code

Open accessCopyright infoDownload PDF

What the success of brain imaging implies about the neural code

Figure 2.

Affiliation details

University College London, United Kingdom; The Alan Turing Institute, United Kingdom
Figure 2.
Download figureOpen in new tabFigure 2. As models become more complex with added layers, similarity structure becomes harder to recover, which might parallel function along the ventral stream. 

(A) For the artificial neural network coding schemes, similarity to the prototype falls off with increasing distortion (i.e., noise). The models, numbered 1–11, are (1) vector space coding, (2) gain control coding, (3) matrix multiplication coding, (4), perceptron coding, (5) 2-layer network, (6) 3-layer network, (7) 4-layer network, (8) 5-layer network, (9) 6-layer network (10) 7-layer network, and (11), 8-layer network. The darker a model is, the simpler the model is and the more the model preserves similarity structure under fMRI. (B) A deep artificial neural network and the ventral stream can be seen as performing related computations. As in our simulation results, neural similarity should be more difficult to recover in the more advanced layers.

DOI: http://dx.doi.org/10.7554/eLife.21397.005