Measures of Neural Similarity

Abstract

One fundamental question is what makes two brain states similar. For example, what makes the activity in visual cortex elicited from viewing a robin similar to a sparrow? One common assumption in fMRI analysis is that neural similarity is described by Pearson correlation. However, there are a host of other possibilities, including Minkowski and Mahalanobis measures, with each differing in its mathematical, theoretical, and neural computational assumptions. Moreover, the operable measures may vary across brain regions and tasks. Here, we evaluated which of several competing similarity measures best captured neural similarity. Our technique uses a decoding approach to assess the information present in a brain region, and the similarity measures that best correspond to the classifier's confusion matrix are preferred. Across two published fMRI datasets, we found the preferred neural similarity measures were common across brain regions but differed across tasks. Moreover, Pearson correlation was consistently surpassed by alternatives.

Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior

“…(See also refs. 66 , 67 for comparing distance versus linear-based similarity measures.) In general, there were no differences between the two decoding schemes, although in one instance (task-rule decoding), minimum-distance classifiers significantly outperformed logistic classification (Supplementary Fig.…”

Constructing neural network models from brain data reveals representational transformations underlying adaptive behavior

“…To ensure robustness of all fMRI decoding analyses, we additionally performed logistic classifications (linear decoding) to compare with minimum-distance-based classifiers. (See also refs 62,63 for comparing distance versus linear-based similarity measures.) In general, there were no differences between the two decoding schemes, although in one instance (task-rule decoding), minimum-distance classifiers significantly outperformed logistic classification (Supplementary Fig.…”

Visual object categorization in infancy

“…Besides the topography, categorical distinctions in the visual cortex also emerge from dissimilarities between distributed patterns of neural activity evoked by individual objects 7,17,18 . Thus, in visual areas, activity patterns recorded with functional MRI (fMRI) are more similar (i.e., less discriminable) for two animate objects (e.g., parrot and camel) than between an animate and an inanimate object (e.g., parrot and car).…”