Sparse coding in striate and extrastriate visual cortex

• Published in: Journal of neurophysiology Vol. 105; no. 6; pp. 2907 - 2919
• Main Authors: Willmore, Ben D. B., Mazer, James A., Gallant, Jack L.
• Format: Journal Article
• Language: English
• Published: United States American Physiological Society 01.06.2011
• Subjects: Action Potentials - physiology; Animals; Attention - physiology; Brain Mapping; Electrophysiology; Eye Movements - physiology; Macaca; Macaca mulatta; Male; Models, Neurological; Neurons - physiology; Photic Stimulation; Primates; Reaction Time - physiology; Visual Cortex - cytology; Visual Cortex - physiology; Visual Perception
• ISSN: 0022-3077; 1522-1598; 1522-1598
• DOI: 10.1152/jn.00594.2010

Theoretical studies of mammalian cortex argue that efficient neural codes should be sparse. However, theoretical and experimental studies have used different definitions of the term “sparse” leading to three assumptions about the nature of sparse codes. First, codes that have high lifetime sparseness require few action potentials. Second, lifetime-sparse codes are also population-sparse. Third, neural codes are optimized to maximize lifetime sparseness. Here, we examine these assumptions in detail and test their validity in primate visual cortex. We show that lifetime and population sparseness are not necessarily correlated and that a code may have high lifetime sparseness regardless of how many action potentials it uses. We measure lifetime sparseness during presentation of natural images in three areas of macaque visual cortex, V1, V2, and V4. We find that lifetime sparseness does not increase across the visual hierarchy. This suggests that the neural code is not simply optimized to maximize lifetime sparseness. We also find that firing rates during a challenging visual task are higher than theoretical values based on metabolic limits and that responses in V1, V2, and V4 are well-described by exponential distributions. These findings are consistent with the hypothesis that neurons are optimized to maximize information transmission subject to metabolic constraints on mean firing rate.