RESEARCH ARTICLE Columnar Architecture Improves Noise Robustness in a Model Cortical Network

Cortical columnar architecture was discovered decades ago yet there is no agreed upon ex-planation for its function. Indeed, some have suggested that it has no function, it is simply an epiphenomenon of developmental processes. To investigate this problem we have con-structed a computer model of one square millimeter of layer 2/3 of the primary visual cortex (V1) of the cat. Model cells are connected according to data from recent paired cell studies, in particular the connection probability between pyramidal cells is inversely proportional both to the distance separating the cells and to the distance between the preferred parame-ters (features) of the cells. We find that these constraints, together with a columnar architec-ture, produce more tightly clustered populations of cells when compared to the random architecture seen in, for example, rodents. This causes the columnar network to converge more quickly and accurately on the pattern representing a particular stimulus in the pres-ence of noise, suggesting that columnar connectivity functions to improve pattern recogni-tion in cortical circuits. The model also suggests that synaptic failure, a phenomenon exhibited by weak synapses, may conserve metabolic resources by reducing transmitter re-lease at these connections that do not contribute to network function