Dynamical Mechanism of Sampling-Based Probabilistic Inference under Probabilistic Population Codes

Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions, allow general neural networks (NNs) to perform near-optimal point estimation. However, the mechanism of sampling-based probabilistic inference has not been clarified. In this study, we trained two types of artificial NNs, feedforward NN (FFNN) and recurrent NN (RNN), to perform sampling-based probabilistic inference. Then we analyzed and compared their mechanisms of sampling. We found that sampling in RNN was performed by a mechanism that efficiently uses the properties of dynamical systems, unlike FFNN. In addition, we found that sampling in RNNs acted as an inductive bias, enabling a more accurate estimation than in maximum a posteriori estimation. These results provide important arguments for discussing the relationship between dynamical systems and information processing in NNs.

High-resolution sweep metagenomics using fast probabilistic inference

Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP pipeline for identifying and estimating the relative sequence abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our pipeline facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

Outcome uncertainty influences probability perception and risk attitudes

Subjective inferences of probability play a critical role in decision-making. How we learn about choice options, through description or experience, influences how we perceive their likelihoods, an effect known as the description–experience (DE) gap. Classically, the DE gap details how low probability described options are perceptually inflated as compared to equiprobable experience ones. However, these studies assessed probability perception relative to a ‘sure-bet’ option, and it remained unclear whether the DE gap occurs when humans directly trade-off equiprobable description and experience options and whether choice patterns are influenced by the prospects of gain and loss. We addressed these questions through two experiments where humans chose between description and experience options with equal probabilities of either winning or losing points. Contrary to early studies, we found that gain-seeking participants preferred experience options across all probability levels and, by contrast, loss-mitigating participants avoided the experience options across all probability levels, with a maximal effect at 50%. Our results suggest that the experience options were perceived as riskier than descriptive options due to the greater uncertainty associated with their outcomes. We conclude by outlining a novel theory of probabilistic inference where outcome uncertainty modulates probability perception and risk attitudes.

Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey

Real world decision making problems often involve both discrete and continuous variables and require a combination of probabilistic and deterministic knowledge. Stimulated by recent advances in automated reasoning technology, hybrid (discrete+continuous) probabilistic reasoning with constraints has emerged as a lively and fast growing research field. In this paper we provide a survey of existing techniques for hybrid probabilistic inference with logic and algebraic constraints. We leverage weighted model integration as a unifying formalism and discuss the different paradigms that have been used as well as the expressivity-efficiency trade-offs that have been investigated. We conclude the survey with a comparative overview of existing implementations and a critical discussion of open challenges and promising research directions.

Contribution of genome scale metabolic modeling to niche theory

Standard niche modeling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome-scale descriptions of these organisms. This study overcomes this shortcoming by proposing a new conceptual niche that encompasses the whole metabolic capabilities of an organism. The so-called metabolic niche resumes well-known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasizing that the presence-absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organism's niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better the genome-scale description in ecological studies.