A simple model for detailed visual cortex maps predicts fixed hypercolumn sizes

Abstract

Orientation hypercolumns in the visual cortex are delimited by the repeating pinwheel patterns of orientation selective neurons. We design a generative model for visual cortex maps that reproduces such orientation hypercolumns as well as ocular dominance maps while preserving retinotopy. The model uses a neural placement method based on t-distributed stochastic neighbour embedding (t-SNE) to create maps that order common features in the connectivity matrix of the circuit. We find that, in our model, hypercolumns generally appear with fixed cell numbers independently of the overall network size. These results would suggest that existing differences in absolute pinwheel densities are a consequence of variations in neuronal density. Indeed, available measurements in the visual cortex indicate that pinwheels consist of a constant number of [~]30, 000 neurons. Our model is able to reproduce a large number of characteristic properties known for visual cortex maps. We provide the corresponding software in our MAPStoolbox for Matlab. In briefWe present a generative model that predicts visual map structures in the brain and a large number of their characteristic properties; a neural placement method for any given connectivity matrix. HighlightsO_LIGenerative model with retinotopy, orientation preference and ocular dominance. C_LIO_LIPrediction of constant neuronal numbers per orientation hypercolumn. C_LIO_LICurated data shows constant [~]30, 000 neurons per pinwheel across species. C_LIO_LISimple explanation for constant pinwheel and orientation hypercolumn ratios. C_LIO_LIPrecise prediction of [~]80% nearest neighbour singularities with opposing polarity. C_LIO_LIModel asymptotically approaches realistic normalised pinwheel densities. C_LIO_LISmall brains with < [~]300 potential pinwheels exhibit salt-and-pepper maps. C_LIO_LIDifferent map phenotypes can exist even for similar connectivity. C_LI