In All Likelihood, Deep Belief Is Not Enough

Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively assess and compare the performance of statistical models is given by the likelihood. One class of statistical models which has recently gained increasing popularity and has been applied to a variety of complex data are deep belief networks. Analyses of these models, however, have been typically limited to qualitative analyses based on samples due to the computationally intractable nature of the model likelihood. Motivated by these circumstances, the present article provides a consistent estimator for the likelihood that is both computationally tractable and simple to apply in practice. Using this estimator, a deep belief network which has been suggested for the modeling of natural image patches is quantitatively investigated and compared to other models of natural image patches. Contrary to earlier claims based on qualitative results, the results presented in this article provide evidence that the model under investigation is not a particularly good model for natural image

Optimal Population Coding, Revisited

Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool – Fisher information – can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information

Inferring decoding strategy from choice probabilities in the presence of noise correlations

The activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship often quantified by choice probabilities. While choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. Here, we derive the mathematical relationship between choice probabilities, read-out weights and noise correlations within the standard neural decision making model. Our solution allows us to prove and generalize earlier observations based on numerical simulations, and to derive novel predictions. Importantly, we show how the read-out weight profile, or decoding strategy, can be inferred from experimentally measurable quantities. Furthermore, we present a test to decide whether the decoding weights of individual neurons are optimal, even without knowing the underlying noise correlations. We confirm the practical feasibility of our approach using simulated data from a realistic population model. Our work thus provides the theoretical foundation for a growing body of experimental results on choice probabilities and correlations

Bayesian Inference for Generalized Linear Models for Spiking Neurons

Generalized Linear Models (GLMs) are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli. When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show how the posterior distribution over model parameters of GLMs can be approximated by a Gaussian using the Expectation Propagation algorithm. In this way, we obtain an estimate of the posterior mean and posterior covariance, allowing us to calculate Bayesian confidence intervals that characterize the uncertainty about the optimal solution. From the posterior we also obtain a different point estimate, namely the posterior mean as opposed to the commonly used maximum a posteriori estimate. We systematically compare the different inference techniques on simulated as well as on multi-electrode recordings of retinal ganglion cells, and explore the effects of the chosen prior and the performance measure used. We find that good performance can be achieved by choosing an Laplace prior together with the posterior mean estimate

Spheroids of Bladder Smooth Muscle Cells for Bladder Tissue Engineering

Cell-based tissue engineering (TE) has been proposed to improve treatment outcomes in end-stage bladder disease, but TE approaches with 2D smooth muscle cell (SMC) culture have so far been unsuccessful. Here, we report the development of primary bladder-derived 3D SMC spheroids that outperform 2D SMC cultures in differentiation, maturation, and extracellular matrix (ECM) production. Bladder SMC spheroids were compared with 2D cultures using live-dead staining, qRT-PCR, immunofluorescence, and immunoblotting to investigate culture conditions, contractile phenotype, and ECM deposition. The SMC spheroids were viable for up to 14 days and differentiated rather than proliferating. Spheroids predominantly expressed the late myogenic differentiation marker MyH11, whereas 2D SMC expressed more of the general SMC differentiation marker α-SMA and less MyH11. Furthermore, the expression of bladder wall-specific ECM proteins in SMC spheroids was markedly higher. This first establishment and analysis of primary bladder SMC spheroids are particularly promising for TE because differentiated SMCs and ECM deposition are a prerequisite to building a functional bladder wall substitute. We were able to confirm that SMC spheroids are promising building blocks for studying detrusor regeneration in detail and may provide improved function and regenerative potential, contributing to taking bladder TE a significant step forward

Bayesian estimation of orientation preference maps

Imaging techniques such as optical imaging of intrinsic signals, 2-photon calcium imaging and voltage sensitive dye imaging can be used to measure the functional organization of visual cortex across different spatial and temporal scales. Here, we present Bayesian methods based on Gaussian processes for extracting topographic maps from functional imaging data. In particular, we focus on the estimation of orientation preference maps (OPMs) from intrinsic signal imaging data. We model the underlying map as a bivariate Gaussian process, with a prior covariance function that reflects known properties of OPMs, and a noise covariance adjusted to the data. The posterior mean can be interpreted as an optimally smoothed estimate of the map, and can be used for model based interpolations of the map from sparse measurements. By sampling from the posterior distribution, we can get error bars on statistical properties such as preferred orientations, pinwheel locations or pinwheel counts. Finally, the use of an explicit probabilistic model facilitates interpretation of parameters and quantitative model comparisons. We demonstrate our model both on simulated data and on intrinsic signaling data from ferret visual cortex

Laparoscopic Ureteroureterostomy vs. Common Sheath Ureteral Reimplantation in Children With Duplex Kidney Anomalies

Purpose: Laparoscopic ureteroureterostomy (LUU) has been proposed as an alternative to common sheath ureteral reimplantation (CSUR) in children with symptomatic duplex kidneys. However, data is limited for LUU in the pediatric population. The aim of this study was to analyze our experience with LUU and to compare the results with those after CSUR to assess whether a less invasive surgical approach could be a valid alternative. Patients and methods: The data of all children with duplex kidneys who underwent either LUU or CSUR at our center from 2006 to 2018 were reviewed retrospectively. After parental counseling, the option of LUU was provided as an alternative to CSUR for unilateral procedures and in the absence of vesicoureteral reflux to the receiving ureter. Baseline characteristics, indication for surgery, hospitalization and operative times, and intraoperative, post-operative, and late complications were analyzed. Preoperative and 1-year post-operative sonographies were reviewed by a pediatric radiologist. Increasing renal pelvic diameter (Δ >5 mm) was regarded as a sign of ureteral obstruction. Results: Forty children were included in this study, with 16 children receiving LUU and 24 children receiving CSUR. The children had a mean age of 2.7 years (7 months-9.8 years) and were followed up in our outpatient clinic for an average of 3.9 years (3 months-10.6 years) after surgery. The median hospital stay was 2 days shorter after LUU. Initially, a considerably longer time was needed for LUU, but after more experience was gained, similar operative times were observed for both procedures. Complications were encountered in both groups. After LUU, two patients developed anastomotic leakage: one was managed conservatively, and one required temporary nephrostomy. In the CSUR group, one patient developed vesicoureteral obstruction during follow-up and required reoperation with LUU. The occurrence of post-operative urinary tract infections was similar in both groups. No complications related to the ureteral stump after LUU arose. Conclusion: LUU is a safe and efficacious treatment option for children with duplex kidney anomalies and can be used as an alternative to CSUR. All children receiving LUU showed a non-obstructive, patent anastomosis and no signs for stenotic compromise of the receiving ureter

Validation of Composite Systems by Discrepancy Propagation

Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests. Validating such systems by means of simulation offers a promising and less expensive alternative, but requires an assessment of the simulation accuracy and therefore end-to-end measurements. Additionally, covariate shifts between simulations and actual usage can cause difficulties for estimating the reliability of such systems. In this work, we present a validation method that propagates bounds on distributional discrepancy measures through a composite system, thereby allowing us to derive an upper bound on the failure probability of the real system from potentially inaccurate simulations. Each propagation step entails an optimization problem, where -- for measures such as maximum mean discrepancy (MMD) -- we develop tight convex relaxations based on semidefinite programs. We demonstrate that our propagation method yields valid and useful bounds for composite systems exhibiting a variety of realistic effects. In particular, we show that the proposed method can successfully account for data shifts within the experimental design as well as model inaccuracies within the used simulation.Comment: 20 pages incl. 10 pages appendi