TY - JOUR AB - In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics. AU - Chalk, Matthew J AU - Masset, Paul AU - Gutkin, Boris AU - Denève, Sophie ID - 680 IS - 6 JF - PLoS Computational Biology SN - 1553734X TI - Sensory noise predicts divisive reshaping of receptive fields VL - 13 ER - TY - GEN AB - Based on the intuitive derivation of the dynamics of SIM allele frequency pM in the main text, we present a heuristic prediction for the long-term SIM allele frequencies with χ > 1 stresses and compare it to numerical simulations. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9851 TI - Heuristic prediction for multiple stresses ER - TY - GEN AB - We show how different combination strategies affect the fraction of individuals that are multi-resistant. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9852 TI - Resistance frequencies for different combination strategies ER - TY - GEN AB - Includes derivation of optimal estimation algorithm, generalisation to non-poisson noise statistics, correlated input noise, and implementation of in a multi-layer neural network. AU - Chalk, Matthew J AU - Masset, Paul AU - Gutkin, Boris AU - Denève, Sophie ID - 9855 TI - Supplementary appendix ER - TY - CONF AB - Recently there has been a proliferation of automated program repair (APR) techniques, targeting various programming languages. Such techniques can be generally classified into two families: syntactic- and semantics-based. Semantics-based APR, on which we focus, typically uses symbolic execution to infer semantic constraints and then program synthesis to construct repairs conforming to them. While syntactic-based APR techniques have been shown successful on bugs in real-world programs written in both C and Java, semantics-based APR techniques mostly target C programs. This leaves empirical comparisons of the APR families not fully explored, and developers without a Java-based semantics APR technique. We present JFix, a semantics-based APR framework that targets Java, and an associated Eclipse plugin. JFix is implemented atop Symbolic PathFinder, a well-known symbolic execution engine for Java programs. It extends one particular APR technique (Angelix), and is designed to be sufficiently generic to support a variety of such techniques. We demonstrate that semantics-based APR can indeed efficiently and effectively repair a variety of classes of bugs in large real-world Java programs. This supports our claim that the framework can both support developers seeking semantics-based repair of bugs in Java programs, as well as enable larger scale empirical studies comparing syntactic- and semantics-based APR targeting Java. The demonstration of our tool is available via the project website at: https://xuanbachle.github.io/semanticsrepair/ AU - Le, Xuan AU - Chu, Duc Hiep AU - Lo, David AU - Le Goues, Claire AU - Visser, Willem ID - 941 T2 - Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis TI - JFIX: Semantics-based repair of Java programs via symbolic PathFinder ER -