Statistical Analysis of Decoding Performances of Diverse Populations of Neurons.

A central theme in computational neuroscience is determining the neural correlates of efficient and accurate coding of sensory signals. Diversity, or heterogeneity, of intrinsic neural attributes is known to exist in many brain areas and is thought to significantly affect neural coding. Recent theoretical and experimental work has argued that in uncoupled networks, coding is most accurate at intermediate levels of heterogeneity. Here we consider this question with data from in vivo recordings of neurons in the electrosensory system of weakly electric fish subject to the same realization of noisy stimuli; we use a generalized linear model (GLM) to assess the accuracy of (Bayesian) decoding of stimulus given a population spiking response. The long recordings enable us to consider many uncoupled networks and a relatively wide range of heterogeneity, as well as many instances of the stimuli, thus enabling us to address this question with statistical power. The GLM decoding is performed on a single long time series of data to mimic realistic conditions rather than using trial-averaged data for better model fits. For a variety of fixed network sizes, we generally find that the optimal levels of heterogeneity are at intermediate values, and this holds in all core components of GLM. These results are robust to several measures of decoding performance, including the absolute value of the error, error weighted by the uncertainty of the estimated stimulus, and the correlation between the actual and estimated stimulus. Although a quadratic fit to decoding performance as a function of heterogeneity is statistically significant, the result is highly variable with low R2 values. Taken together, intermediate levels of neural heterogeneity are indeed a prominent attribute for efficient coding even within a single time series, but the performance is highly variable.

Instructional Video did not reduce anxiety during pediatric cast removal: a prospective cohort study.

The aim of the study was to determine if the use of an Instructional Video will decrease anxiety during cast removal. We enrolled 60 healthy children undergoing their first cast removal following conservative fracture treatment. Patients were divided into one of three groups (1) No Video (control group), (2) watching a video of a well-tolerated pediatric cast removal (Instructional Video), or (3) watching a nonmedical Children's Video during cast removal. We assessed anxiety to the cast saw by recording heart rate in the waiting room, during the procedure, and 1-2 min after the procedure. There were no significant differences in waiting room, procedure, and post-procedure heart rates between the two interventions and the control group. The mean change in heart rate from baseline to the procedure room for the Instructional Video cohort exhibited a similar increase (25.8 beats/min) in heart rate during cast removal as the No Video group (26.3 beats/min), while the Children's Video had the smallest change in heart rate (17.7 beats/min) with a trend towards significance (P = 0.12). The results were not statistically significant for the full linear mixed-effect model on the three measurements. When we use age to control for variability in the data, we have a moderate effect size between Children's Video and control (η2P = 0.0592), revealing that certain ages likely benefited from the Children's Video intervention. Distraction using a Children's Video may help reduce anxiety during cast removal whereas the Instructional Video did not reduce anxiety as hypothesized.

Firing rate distributions in a feedforward network of neural oscillators with intrinsic and network heterogeneity.

The level of firing rate heterogeneity in a population of cortical neurons has consequences for how stimuli are processed. Recent studies have shown that the right amount of firing rate heterogeneity (not too much or too little) is a signature of efficient coding, thus quantifying the relative amount of firing rate heterogeneity is important. In a feedforward network of stochastic neural oscillators, we study the firing rate heterogeneity stemming from two sources: intrinsic (different individual cells) and network (different effects from presynaptic inputs). We find that the relationship between these two forms of heterogeneity can lead to significant changes in firing rate heterogeneity. We consider several networks, including noisy excitatory synaptic inputs, and noisy inputs with both excitatory and inhibitory inputs. To mathematically explain these results, we apply a phase reduction and derive asymptotic approximations of the firing rate statistics assuming weak noise and coupling. Our analytic calculations reveals how the interaction between intrinsic and network heterogeneity results in different firing rate distributions. Our work shows the importance of the phase-resetting curve (and various transformations of it revealed by our analytic calculations) in controlling firing rate statistics.