PT  - JOURNAL ARTICLE
AU  - Ján Antoĺık
AU  - Cyril Monier
AU  - Yves Frégnac
AU  - Andrew P. Davison
TI  - A comprehensive data-driven model of cat primary visual cortex
AID  - 10.1101/416156
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 416156
4099  - http://biorxiv.org/content/early/2018/09/24/416156.short
4100  - http://biorxiv.org/content/early/2018/09/24/416156.full
AB  - Systems neuroscience has produced an extensive body of evidence on the anatomy and function of cerebral cortex, but the transformation of this knowledge into a coherent understanding of the principles of cortical computations has been limited, even in the most well explored cortical regions such as primary visual cortex. Computational modeling has the potential to integrate such fragmented data into models of brain structures that satisfy the broad range of constraints imposed by experiments, hence advancing our understanding of their computational role. However, such integrative modeling efforts have so far been few in number and mostly unsystematic. Here we seek to address this issue by presenting a first snapshot of such a systematic integrationist computational modeling program: a comprehensive multi-scale spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints revealed by past in vivo experiments. The model represents cortical layers 4 and 2/3, corresponding to a 5×5 mm patch of V1. We have subjected the model to numerous visual stimulation protocols covering a wide range of input statistics, from standard artificial stimuli such as sinusoidal gratings to natural scenes with simulated eye-movements. The model expresses over multiple scales a number of statistical and functional properties previously identified experimentally including: spontaneous activity with a physiologically plausible resting conductance regime; contrast-invariant orientation-tuning width; realistic interplay between evoked excitatory and inhibitory conductances; center-surround interaction effects; and stimulus-dependent changes in the precision of the neural code as a function of input statistics. This data-driven model offers numerous insights into how the studied properties interact, and thus contributes to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of V1 and beyond, and grounds this work in a principled open-science approach that has the potential to catalyze future development in the field.