Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

Prospective trial of different antimicrobial treatment durations for presumptive canine urinary tract infections

Abstract: Background: Avoidance of unnecessary antimicrobial administration is a key tenet of antimicrobial stewardship; knowing the optimal duration of therapy obviates over-treatment. However, little research has been performed to establish course lengths for common canine infections. In clinical practice, antimicrobial therapy is frequently prescribed in dogs presenting lower urinary tract signs (haematuria, pollakiuria and dysuria/stranguria). The proposed length of treatment in International Consensus guidelines has decreased with each iteration, but these recommendations remain arbitrary and largely extrapolated from experience in people. Methods: The objective of this prospective, multi-centre study is to find the shortest course duration that is non-inferior to the standard duration of 7 days of amoxicillin/clavulanate in terms of clinical outcomes for female dogs with lower urinary tract signs consistent with a urinary tract infection. An electronic data capture platform will be used by participating veterinarians working in clinical practice in the United Kingdom. Eligible dogs must be female, aged between 6 months and 10 years and have lower urinary tract signs of up to seven days’ duration. Enrolment will be offered in cases where the case clinician intends to prescribe antimicrobial therapy. Automatic pseudo-randomisation to treatment group will be based on the day of presentation (Monday-Friday); all antimicrobial courses will be completed on the Sunday after presentation generating different treatment durations. Follow-up data will be collected 1, 8 and 22–26 days after completion of the antimicrobial course to ensure effective safety netting, and to monitor short-term outcome and recurrence rates. Informed owner consent will be obtained in all cases. The study is approved by the Ethical Review Board of the University of Nottingham and has an Animal Test Certificate from the Veterinary Medicine’s Directorate. Discussion: This study has been designed to mirror current standards of clinical management; conclusions should therefore, be widely applicable and guide practising veterinarians in their antimicrobial decision-making process. A duration-response curve will be created allowing determination of the optimal treatment duration for the management of female dogs with lower urinary tract signs. It is hoped that these results will contribute valuable information to improve future antimicrobial stewardship as part of a wider one-health perspective

Going to sleep in the supine position is a modifiable risk factor for late pregnancy stillbirth; findings from the New Zealand multicentre stillbirth case-control study

Objective: Our objective was to test the primary hypothesis that maternal non-left, in particular supine going-to-sleep position, would be a risk factor for late stillbirth (≥28 weeks of gestation). Methods: A multicentre case-control study was conducted in seven New Zealand health regions, between February 2012 and December 2015. Cases (n=164) were women with singleton pregnancies and late stillbirth, without congenital abnormality. Controls (n=569) were women with on-going singleton pregnancies, randomly selected and frequency matched for health region and gestation. The primary outcome was adjusted odds of late stillbirth associated with self-reported going-to-sleep position, on the last night. The last night was the night before the late stillbirth was thought to have occurred or the night before interview for controls. Going to- sleep position on the last night was categorised as: supine, left-side, right-side, propped or restless. Multivariable logistic regression adjusted for known confounders. Results: Supine going-to-sleep position on the last night was associated with increased late stillbirth risk (adjusted odds ratios (aOR) 3.67, 95% confidence interval (CI) 1.74 to 7.78) with a population attributable risk of 9.4%. Other independent risk factors for late stillbirth (aOR, 95% CI) were: BMI (1.04, 1.01 to 1.08) per unit, maternal age ≥40 (2.88, 1.31 to 6.32), birthweight <10th customised centile (2.76, 1.59 to 4.80), and <6 hours sleep on the last night (1.81, 1.14 to 2.88). The risk associated with supine-going-to sleep position was greater for term (aOR 10.26, 3.00 to 35.04) than preterm stillbirths (aOR 3.12, 0.97 to 10.05). Conclusions: Supine going-to-sleep position is associated with a 3.7 fold increase in overall late stillbirth risk, independent of other common risk factors. A public health campaign encouraging women not to go-to-sleep supine in the third trimester has potential to reduce late stillbirth by approximately 9%

Segregation of object and background motion in the retina

An important task in vision is to detect objects moving within a stationary scene. During normal viewing this is complicated by the presence of eye movements that continually scan the image across the retina, even during fixation. To detect moving objects, the brain must distinguish local motion within the scene from the global retinal image drift due to fixational eye movements. We have found that this process begins in the retina: a subset of retinal ganglion cells responds to motion in the receptive field centre, but only if the wider surround moves with a different trajectory. This selectivity for differential motion is independent of direction, and can be explained by a model of retinal circuitry that invokes pooling over nonlinear interneurons. The suppression by global image motion is probably mediated by polyaxonal, wide-field amacrine cells with transient responses. We show how a population of ganglion cells selective for differential motion can rapidly flag moving objects, and even segregate multiple moving objects

Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets

Temporal trends in fetal mortality at and beyond term and induction of labor in Germany 2005-2012 : data from German routine perinatal monitoring

Purpose: While a variety of factors may play a role in fetal and neonatal deaths, postmaturity as a cause of stillbirth remains a topic of debate. It still is unclear, whether induction of labor at a particular gestational age may prevent fetal deaths. Methods: A multidisciplinary working group was granted access to the most recent set of relevant German routine perinatal data, comprising all 5,291,011 hospital births from 2005 to 2012. We analyzed correlations in rates of induction of labor (IOL), perinatal mortality (in particular stillbirths) at different gestational ages, and fetal morbidity. Correlations were tested with Pearson's product-moment analysis (α = 5 %). All computations were performed with SPSS version 22. Results: Induction rates rose significantly from 16.5 to 21.9 % (r = 0.98; p \ 0.001). There were no significant changes in stillbirth rates (0.28-0.35 per 100 births; r = 0.045; p = 0.806). Stillbirth rates 2009-2012 remained stable in all gestational age groups irrespective of induction. Fetal morbidity (one or more ICD-10 codes) rose significantly during 2005–2012. This was true for both children with (from 33 to 37 %, r = 0.784, p \ 0.001) and without (from 25 to 31 %, (r = 0.920, p \ 0.001) IOL. Conclusions: An increase in IOL at term is not associated with a decline in perinatal mortality. Perinatal morbidity increased with and without indiction of labor