Intermediate intrinsic diversity enhances neural population coding

Abstract

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.generalized linear models | intrinsic biophysics | neural variability | stimulus coding | ion channels B iological systems including brains must function efficiently under many constraints, including constraints on the numbers of individual neurons dedicated to a given task. Brain function therefore depends on an appropriate division of labor, with specific neurons dedicated to different functions. For example, different types of retinal ganglion cells represent visual information at different timescales (1), and distinct classes of cortical interneurons play diverse roles in coordinating network activity (2). Whereas attempts to understand how distinct classes of cells encode information have proven successful (1), the importance of within-type variability remains poorly understood (3, 4) although has recently become a topic of great interest (5-8).Although neuron-to-neuron variability is often viewed as an epiphenomenon of biological imprecision (3, 4), having neurons of the same type that respond to different stimulus features may improve stimulus encoding. This variability may be leveraged to improve functions such as stimulus encoding if heterogeneous output of neurons of a single type is collectively used for population coding. Such populations of neurons could efficiently represent complex stimuli by collectively covering the relevant stimulus space (1, 9, 10). Network interactions could further increase the efficiency of information transmission by decorrelating neural responses and reducing the redundancy between their outputs (11-13). In contrast, eliminating redundancy (also referred to as biological degeneracy, ref. 14) may make stimulus coding less robu...

Heterogeneity of intrinsic biophysical properties among cochlear nucleus neurons improves the population coding of temporal information

“…Furthermore, under more realistic scenarios using inputs that were highly similar but not identical, heterogeneity among the neuronal firing responses still improved the neural coding of a common signal. Studies in the olfactory bulb have shown similar effects that we show here, such that the diversity of the intrinsic properties of mitral cells receiving common inputs improved coding by reducing redundancy and generating heterogeneity in the temporal patterning of activity (Padmanabhan and Urban 2010; Tripathy et al 2013). Intrinsic biophysical heterogeneity may thus be a common mechanism for improved sensory coding.…”

“…Spiking activity by individual neurons could account for ϳ22% of the stimulus variance, with high coherence with the stimulus in the frequency range of 10 -80 Hz. Individual neurons had an average information rate of ϳ2.5 bits/spike, comparable to estimates of coding efficiency in other peripheral, auditory or sensory areas (Bialek et al 1991;Borst and Theunissen 1999;Butts et al 2010;Rieke et al 1997;Tripathy et al 2013). There was a strong positive correlation between a neuron's reliability and how much information each spike carried: the more reliable the firing, the more informative was each spike.…”

“…Neurons in NA respond with highly reliable patterns when presented with repeated white noise current stimuli in vitro (Bryant and Segundo 1976;Kreeger et al 2012), and in vivo when presented with repeated frozen acoustic stimulation (Steinberg and Peña 2011), like neurons in other brain areas in vitro (Mainen and Sejnowski 1995;Tripathy et al 2013) and in vivo (Butts et al 2010;de Ruyter van Steveninck et al 1997;Reinagel and Reid 2002). One prediction from these findings is that any two neurons receiving identical, or nearly identical, input should respond with highly correlated spike patterns, assuming these neurons have similar intrinsic properties (Bryant et al 1973;Ermentrout et al 2008;Galán et al 2006;Reyes 2003;Street and Manis 2007).…”

“…However, if every neuron responded with an identical pattern to a given input, then the information encoded by a population of neurons receiving similar inputs would be highly redundant. A solution to this problem is to introduce some form of neural heterogeneity among the set of synaptic inputs, network connectivity, or intrinsic properties (Friedrich and Laurent 2001;Gupta 2000;Padmanabhan and Urban 2010;Tripathy et al 2013).…”

The interplay between cooperativity and diversity in model threshold ensembles

“…The observed behaviour emerges as a statistical average of the whole cell ensemble that may however show a significant diversity at the single-cell level [2]. The diversity of the basic units in complex biological systems, such as a biomembrane [3,4] and the brain [5,6], increases functionality and reliability. In particular, a certain level of neural response diversity can be beneficial to coding and decoding processes [6,7].…”