A simple model for low variability in neural spike trains

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, which can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters, but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like Linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity this model has the potential to explain low variability in other areas

Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding

According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for sensory structures performing non-linear computations on high dimensional stimuli is still an open challenge. Here we develop a method to characterize the sensitivity of the retinal network to perturbations of a stimulus. Using closed-loop experiments, we explore selectively the space of possible perturbations around a given stimulus. We then show that the response of the retinal population to these small perturbations can be described by a local linear model. Using this model, we computed the sensitivity of the neural response to arbitrary temporal perturbations of the stimulus, and found a peak in the sensitivity as a function of the frequency of the perturbations. Based on a minimal theory of sensory processing, we argue that this peak is set to maximize information transmission. Our approach is relevant to testing the efficient coding hypothesis locally in any context where no reliable encoding model is known

Pairwise Ising model analysis of human cortical neuron recordings

During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investigate their impact on neuronal behavior, we apply a pairwise Ising model analysis by inferring the maximum entropy model that reproduces single and pairwise moments of the neuron's spiking activity. In this work we first review the inference algorithm introduced in Ferrari,Phys. Rev. E (2016). We then succeed in applying the algorithm to infer the model from a large ensemble of neurons recorded by multi-electrode array in human temporal cortex. We compare the Ising model performance in capturing the statistical properties of the network activity during wakefulness and deep sleep. For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data.Comment: 8 pages, 3 figures, Geometric Science of Information 2017 conferenc

Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons

Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer {population models of interacting neurons that collectively encode stimulus information}. The key to disentangling intrinsic from extrinsic correlations is to infer the {couplings between neurons} separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus

Quality of life with ivabradine in patients with angina pectoris

Background—To explore the effect of ivabradine on angina-related quality of life (QoL) in patients participating in the Study Assessing the Morbidity–Mortality Benefits of the If Inhibitor Ivabradine in Patients with Coronary Artery Disease (SIGNIFY) QoL substudy. Methods and Results—QoL was evaluated in a prespecified subgroup of SIGNIFY patients with angina (Canadian Cardiovascular Society class score, ≥2 at baseline) using the Seattle Angina Questionnaire and a generic visual analogue scale on health status. Data were available for 4187 patients (2084 ivabradine and 2103 placebo). There were improvements in QoL in both treatment groups. The primary outcome of change in physical limitation score at 12 months was 4.56 points for ivabradine versus 3.40 points for placebo (E, 0.96; 95% confidence interval, –0.14 to 2.05; P=0.085). The ivabradine−placebo difference in physical limitation score was significant at 6 months (P=0.048). At 12 months, the visual analogue scale and the other Seattle Angina Questionnaire dimensions were higher among ivabradine-treated patients, notably angina frequency (P<0.001) and disease perception (P=0.006). Patients with the worst QoL at baseline (ie, those in the lowest tertile of score) had the best improvement in QoL for 12 months, with improvements in physical limitation and a significant reduction in angina frequency (P=0.034). The effect on QoL was maintained over the study duration, and ivabradine patients had better scores on angina frequency at every visit to 36 months. Conclusions—Treatment with ivabradine did not affect the primary outcome of change in physical limitation score at 12 months. It did produce consistent improvements in other self-reported QoL parameters related to angina pectoris, notably in terms of angina frequency and disease perception

A small-correlation expansion to quantify information in noisy sensory systems

Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations. Here we develop a small-correlation expansion to compute the stimulus information carried by a large population of neurons, yielding interpretable analytical expressions in terms of the neurons' firing rates and pairwise correlations. We validate the approximation on synthetic data and demonstrate its applicability to electrophysiological recordings in the vertebrate retina, allowing us to quantify the effects of noise correlations between neurons and of memory in single neurons

Individual Responses to Business Tendency Surveys and the Forecasting of Manufacturing Production

We compare the performances of balances of opinion to those of indicators introduced by Mitchell, Smith and Weale for the one-quarter forecasting of the manufacturing production growth rate. These indicators take into account the heterogeneity of the response behaviours of the entrepreneurs taking part in the Business Tendency Survey. The responses which are the most tightly linked to the overall fluctuations of manufactured production contribute to the variability of these indicators to a larger extent than the responses of the other surveyed. The application of Mitchell, Smith and Weale to British and German data seems to suggest that these indicators perform better in short-term forecasting than the balances of opinion, but their application to Swedish and Portuguese data suggests not. In our study carried out using French data, their predictive performances turn out to be inferior or, at best, equivalent to those of the balances of opinion.Business Tendency Surveys, Quantification, Dis-Aggregate Indicators, Short-Term Forecasting

Unique growth pattern of human mammary epithelial cells induced by polymeric nanoparticles.

Due to their unique properties, engineered nanoparticles (NPs) have found broad use in industry, technology, and medicine, including as a vehicle for drug delivery. However, the understanding of NPs' interaction with different types of mammalian cells lags significantly behind their increasing adoption in drug delivery. In this study, we show unique responses of human epithelial breast cells when exposed to polymeric Eudragit® RS NPs (ENPs) for 1-3 days. Cells displayed dose-dependent increases in metabolic activity and growth, but lower proliferation rates, than control cells, as evidenced in tetrazolium salt (WST-1) and 5-bromo-2'-deoxyuridine (BrdU) assays, respectively. Those effects did not affect cell death or mitochondrial fragmentation. We attribute the increase in metabolic activity and growth of cells culture with ENPs to three factors: (1) high affinity of proteins present in the serum for ENPs, (2) adhesion of ENPs to cells, and (3) activation of proliferation and growth pathways. The proteins and genes responsible for stimulating cell adhesion and growth were identified by mass spectrometry and Microarray analyses. We demonstrate a novel property of ENPs, which act to increase cell metabolic activity and growth and organize epithelial cells in the epithelium as determined by Microarray analysis