Richard C. Gerkin

Intermediate intrinsic diversity enhances neural population coding

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.

High-speed odor transduction and pulse tracking by insect olfactory receptor neurons

Significance How fast can animals smell? Whereas we know how fast our eyes are (in the cinema, images at 24 Hz fuse for humans, whereas our retina can resolve flickers at more than 100 Hz), olfactory perception is believed to be slow. After all, we take a sniff and later another one. Odor plumes in the air, however, can fluctuate at a millisecond time scale. Here, we show that insect olfactory receptor neurons can have response latencies shorter than 2 ms and resolve odorant fluctuations at more than 100 Hz. This high temporal resolution could facilitate odor-background segregation, and it has important implications for underlying cellular processes (transduction), ecology (odor recognition), and technology (development of fast sensors). Sensory systems encode both the static quality of a stimulus (e.g., color or shape) and its kinetics (e.g., speed and direction). The limits with which stimulus kinetics can be resolved are well understood in vision, audition, and somatosensation. However, the maximum temporal resolution of olfactory systems has not been accurately determined. Here, we probe the limits of temporal resolution in insect olfaction by delivering high frequency odor pulses and measuring sensory responses in the antennae. We show that transduction times and pulse tracking capabilities of olfactory receptor neurons are faster than previously reported. Once an odorant arrives at the boundary layer of the antenna, odor transduction can occur within less than 2 ms and fluctuating odor stimuli can be resolved at frequencies more than 100 Hz. Thus, insect olfactory receptor neurons can track stimuli of very short duration, as occur when their antennae encounter narrow filaments in an odor plume. These results provide a new upper bound to the kinetics of odor tracking in insect olfactory receptor neurons and to the latency of initial transduction events in olfaction.

Predicting human olfactory perception from chemical features of odor molecules

How will this molecule smell? We still do not understand what a given substance will smell like. Keller et al. launched an international crowd-sourced competition in which many teams tried to solve how the smell of a molecule will be perceived by humans. The teams were given access to a database of responses from subjects who had sniffed a large number of molecules and been asked to rate each smell across a range of different qualities. The teams were also given a comprehensive list of the physical and chemical features of the molecules smelled. The teams produced algorithms to predict the correspondence between the quality of each smell and a given molecule. The best models that emerged from this challenge could accurately predict how a new molecule would smell. Science, this issue p. 820 Results of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule. It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors (“garlic,” “fish,” “sweet,” “fruit,” “burnt,” “spices,” “flower,” and “sour”). Regularized linear models performed nearly as well as random forest–based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.

The number of olfactory stimuli that humans can discriminate is still unknown

It was recently proposed (Bushdid et al., 2014) that humans can discriminate between at least a trillion olfactory stimuli. Here we show that this claim is the result of a fragile estimation framework capable of producing nearly any result from the reported data, including values tens of orders of magnitude larger or smaller than the one originally reported in (Bushdid et al., 2014). Additionally, the formula used to derive this estimate is well-known to provide an upper bound, not a lower bound as reported. That is to say, the actual claim supported by the calculation is in fact that humans can discriminate at most one trillion olfactory stimuli. We conclude that there is no evidence for the original claim. DOI: http://dx.doi.org/10.7554/eLife.08127.001

Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types

For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature-based database of electrophysiological properties (www.neuroelectro.org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at theta-band frequencies.

Accurately estimating neuronal correlation requires a new spike-sorting paradigm

Neurophysiology is increasingly focused on identifying coincident activity among neurons. Strong inferences about neural computation are made from the results of such studies, so it is important that these results be accurate. However, the preliminary step in the analysis of such data, the assignment of spike waveforms to individual neurons (“spike-sorting”), makes a critical assumption which undermines the analysis: that spikes, and hence neurons, are independent. We show that this assumption guarantees that coincident spiking estimates such as correlation coefficients are biased. We also show how to eliminate this bias. Our solution involves sorting spikes jointly, which contrasts with the current practice of sorting spikes independently of other spikes. This new “ensemble sorting” yields unbiased estimates of coincident spiking, and permits more data to be analyzed with confidence, improving the quality and quantity of neurophysiological inferences. These results should be of interest outside the context of neuronal correlations studies. Indeed, simultaneous recording of many neurons has become the rule rather than the exception in experiments, so it is essential to spike sort correctly if we are to make valid inferences about any properties of, and relationships between, neurons.

Morphological Analysis of Activity-Reduced Adult-Born Neurons in the Mouse Olfactory Bulb

Adult-born neurons (ABNs) are added to the olfactory bulb (OB) throughout life in rodents. While many factors have been identified as regulating the survival and integration of ABNs into existing circuitry, the understanding of how these factors affect ABN morphology and connectivity is limited. Here we compare how cell intrinsic [small interfering RNA (siRNA) knock-down of voltage gated sodium channels NaV1.1–1.3] and circuit level (naris occlusion) reductions in activity affect ABN morphology during integration into the OB. We found that both manipulations reduce the number of dendritic spines (and thus likely the number of reciprocal synaptic connections) formed with the surrounding circuitry and inhibited dendritic ramification of ABNs. Further, we identified regions of ABN apical dendrites where the largest and most significant decreases occur following siRNA knock-down or naris occlusion. In siRNA knock-down cells, reduction of spines is observed in proximal regions of the apical dendrite. This suggests that distal regions of the dendrite may remain active independent of NaV1.1–1.3 channel expression, perhaps facilitated by activation of T-type calcium channels and NMDA receptors. By contrast, circuit level reduction of activity by naris occlusion resulted in a global depression of spine number. Together, these results indicate that ABNs retain the ability to develop their typical overall morphological features regardless of experienced activity, and activity modulates the number and location of formed connections.

Origins of correlated spiking in the mammalian olfactory bulb

Significance Neurons exhibit temporally correlated patterns of activity, and the brain is believed to process information in part by exploiting these correlations. Here we use new analytic tools to show that in the olfactory bulb, the first processing station for smell in mammals, these correlations emerge primarily from the animal’s own breathing pattern, and also from the sparse connectivity of the cells that ultimately transmit olfactory information to higher brain areas. These results inform our understanding of how, and how well, the brain can represent information about smell and provide insight into the importance of active sampling processes in sensory coding. Mitral/tufted (M/T) cells of the main olfactory bulb transmit odorant information to higher brain structures. The relative timing of action potentials across M/T cells has been proposed to encode this information and to be critical for the activation of downstream neurons. Using ensemble recordings from the mouse olfactory bulb in vivo, we measured how correlations between cells are shaped by stimulus (odor) identity, common respiratory drive, and other cells’ activity. The shared respiration cycle is the largest source of correlated firing, but even after accounting for all observable factors a residual positive noise correlation was observed. Noise correlation was maximal on a ∼100-ms timescale and was seen only in cells separated by <200 µm. This correlation is explained primarily by common activity in groups of nearby cells. Thus, M/T-cell correlation principally reflects respiratory modulation and sparse, local network connectivity, with odor identity accounting for a minor component.

Collaborative infrastructure for test-driven scientific model validation

One of the pillars of the modern scientific method is model validation: comparing a scientific models predictions against empirical observations. Today, a scientist demonstrates the validity of a model by making an argument in a paper and submitting it for peer review, a process comparable to code review in software engineering. While human review helps to ensure that contributions meet high-level goals, software engineers typically supplement it with unit testing to get a more complete picture of the status of a project. We argue that a similar test-driven methodology would be valuable to scientific communities as they seek to validate increasingly complex models against growing repositories of empirical data. Scientific communities differ from software communities in several key ways, however. In this paper, we introduce SciUnit, a framework for test-driven scientific model validation, and outline how, supported by new and existing collaborative infrastructure, it could integrate into the modern scientific process.

Cortical up state activity is enhanced after seizures: a quantitative analysis.

In the neocortex, neurons participate in epochs of elevated activity, or Up states, during periods of quiescent wakefulness, slow-wave sleep, and general anesthesia. The regulation of firing during and between Up states is of great interest because it can reflect the underlying connectivity and excitability of neurons within the network. Automated analysis of the onset and characteristics of Up state firing across different experiments and conditions requires a robust and accurate method for Up state detection. Using measurements of membrane potential mean and variance calculated from whole-cell recordings of neurons from control and postseizure tissue, the authors have developed such a method. This quantitative and automated method is independent of cell- or condition-dependent variability in underlying noise or tonic firing activity. Using this approach, the authors show that Up state frequency and firing rates are significantly increased in layer 2/3 neocortical neurons 24 hours after chemoconvulsant-induced seizure. Down states in postseizure tissue show greater membrane-potential variance characterized by increased synaptic activity. Previously, the authors have found that postseizure increase in excitability is linked to a gain-of-function in BK channels, and blocking BK channels in vitro and in vivo can decrease excitability and eliminate seizures. Thus, the authors also assessed the effect of BK-channel antagonists on Up state properties in control and postseizure neurons. These data establish a robust and broadly applicable algorithm for Up state detection and analysis, provide a quantitative description of how prior seizures increase spontaneous firing activity in cortical networks, and show how BK-channel antagonists reduce this abnormal activity.