Neural Readout of a Latency Code in the Active Electrosensory System

The latency of spikes relative to a stimulus conveys sensory information across modalities. However, in most cases it remains unclear whether and how such latency codes are utilized by postsynaptic neurons. In the active electrosensory system of mormyrid fish, a latency code for stimulus amplitude in electroreceptor afferent nerve fibers (EAs) is hypothesized to be read out by a central reference provided by motor corollary discharge (CD). Here we demonstrate that CD enhances sensory responses in postsynaptic granular cells of the electrosensory lobe, but is not required for reading out EA input. Instead, diverse latency and spike count tuning across the EA population gives rise to graded information about stimulus amplitude that can be read out by standard integration of converging excitatory synaptic inputs. Inhibitory control over the temporal window of integration renders two granular cell subclasses differentially sensitive to information derived from relative spike latency versus spike count.

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface

Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description—wavelet average coefficients (WAC)—to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

Correlated variability in primate superior colliculus depends on functional class

Correlated variability (spike count correlations, rSC) in a population of neurons can constrain how information is read out, depending on behavioral task and neuronal tuning. Here we tested whether rSC also depends on neuronal functional class. We recorded from populations of neurons in macaque superior colliculus (SC), a structure that contains well-defined functional classes. We found that during a guided saccade task, different classes of neurons exhibited differing degrees of rSC. "Delay class" neurons displayed the highest rSC, especially during the delay epoch of saccade tasks that relied on working memory. This was only present among Delay class neurons within the same hemisphere. The dependence of rSC on functional class indicates that subpopulations of SC neurons occupy distinct circuit niches with distinct inputs. Such subpopulations should be accounted for differentially when attempting to model or infer population coding principles in the SC, or elsewhere in the primate brain.

A universal probabilistic spike count model reveals ongoing modulation of neural variability

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work has demonstrated that this variability has predictable structure, can be modulated by sensory input and behaviour, and bears critical signatures of the underlying network dynamics and computations. However, current methods for characterising neural variability are primarily geared towards sensory coding in the laboratory: they require trials with repeatable experimental stimuli and behavioural covariates. In addition, they make strong assumptions about the parametric form of variability, rely on assumption-free but data-inefficient histogram-based approaches, or are altogether ill-suited for capturing variability modulation by covariates. Here we present a universal probabilistic spike count model that eliminates these shortcomings. Our method builds on sparse Gaussian processes and can model arbitrary spike count distributions (SCDs) with flexible dependence on observed as well as latent covariates, using scalable variational inference to jointly infer the covariate-to-SCD mappings and latent trajectories in a data efficient way. Without requiring repeatable trials, it can flexibly capture covariate-dependent joint SCDs, and provide interpretable latent causes underlying the statistical dependencies between neurons. We apply the model to recordings from a canonical non-sensory neural population: head direction cells in the mouse. We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations. Our approach paves the way to understanding the mechanisms and computations underlying neural variability under naturalistic conditions, beyond the realm of sensory coding with repeatable stimuli.

Modeling of Activity-Induced Changes in Signal Propagation Speed of Mechano-Electrically Stimulated Nerve Fiber

One of the important questions in the research on neural coding is how the preceding axonal activity affects the signal propagation speed of the following one. We present an approach to solving this problem by introducing a multi-level spike count for activity quantification and fitting a family of linear regression models to the data. The best-achieved score is R2=0.89 and the comparison of different models indicates the importance of long and very short nerve fiber memory. Further studies are required to understand the complex axonal mechanisms responsible for the discovered phenomena.

Species Identification and Morphological Trait Diversity Assessment in Ryegrass (Lolium spp.) Populations from Texas Blacklands

Ryegrass (Lolium spp.) is a troublesome weed in major wheat producing regions in the U.S. High diversity and adaptive potential are known to contribute to its success as a weed species and also create difficulties in correct species identification in fields. The objective of this research was to characterize diversity for 16 different morphological traits among 56 Lolium populations collected from wheat (Triticum aestivum L.) production fields across the Texas Blacklands region and identify specific Lolium species based on taxonomical characteristics. Populations were highly diverse (both at inter- and intra-population levels) for the traits studied, and a taxonomical comparison with USDA-GRIN reference samples revealed that all the populations were variants of Italian ryegrass [Lolium perenne L. ssp. multiflorum (Lam.) Husnot] with a few offtypes of perennial (Lolium perenne L.) or probable hybrids between the two species. Hierarchical clustering grouped the populations into 6 clusters based on their similarities for the morphological traits investigated. Principal component analysis showed that the variability for yield traits greatly contributed to the total diversity. Pre-flowering plant height (stage 10 on Feekes scale) was positively correlated with tiller count, shoot biomass, spike count, but not with total seed count/plant, whereas plant height at maturity (stage 11.3-11.4 on Feekes scale) was highly correlated with total seeds/plant. Further, basal node color was positively correlated with plant growth habit, regrowth rate, and leaf color. Leaf blade width was positively correlated with survival to pinoxaden and multiple herbicides, whereas, spike count was negatively correlated with survival to mesosulfuron. The high levels of intra- as well as inter-population variability documented in this study indicates the potential of this species to rapidly adapt to herbicides, and emphasizes the need for implementing diverse management tactics including the integration of harvest weed seed control.

Wavelet Average Coefficient: a New Statistical Feature for Capturing the Temporal Pattern of Spike Trains, With Application in Brain Machine Interface

Motor brain machine interfaces (BMI) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders 1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; 2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description - wavelet average coefficients (WAC) - to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.