Intermediate intrinsic diversity enhances neural population coding

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 110; no. 20; pp. 8248 - 8253
• Main Authors: Tripathy, Shreejoy J., Padmanabhan, Krishnan, Gerkin, Richard C., Urban, Nathaniel N.
• Format: Journal Article
• Language: English
• Published: Washington, DC National Academy of Sciences 14.05.2013; National Acad Sciences
• Subjects: Action Potentials - physiology; Algorithms; Animals; Bayes Theorem; Behavioral neuroscience; Biodiversity; Biological and medical sciences; Biological Sciences; Biological variation; Biophysics; Brain; Electrophysiology; Fundamental and applied biological sciences. Psychology; Generalized linear models; Ion Channels - metabolism; Linear Models; Mice; Modeling; Models, Neurological; Neurobiology; Neurons; Neurons - metabolism; Neurons - physiology; Neuroscience; Olfactory bulb; Olfactory Bulb - pathology; Parametric models; Physiological stimulation; Physiology; Population parameters; Reproducibility of Results; statistical analysis; Vertebrates: nervous system and sense organs; generalized linear models; Stimulus; stimulus coding; intrinsic biophysics; Ionic channel; Models; neural variability; ion channels
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1221214110

Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.