TY - JOUR
T1 - Intermediate intrinsic diversity enhances neural population coding
AU - Tripathy, Shreejoy J.
AU - Padmanabhan, Krishnan
AU - Gerkin, Richard C.
AU - Urban, Nathaniel N.
PY - 2013/5/14
Y1 - 2013/5/14
N2 - 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.
AB - 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.
KW - Generalized linear models
KW - Intrinsic biophysics
KW - Ion channels
KW - Neural variability
KW - Stimulus coding
UR - http://www.scopus.com/inward/record.url?scp=84877847932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877847932&partnerID=8YFLogxK
U2 - 10.1073/pnas.1221214110
DO - 10.1073/pnas.1221214110
M3 - Article
C2 - 23630284
AN - SCOPUS:84877847932
SN - 0027-8424
VL - 110
SP - 8248
EP - 8253
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 20
ER -