The structure of hippocampal CA1 interactions optimizes spatial coding across experience

Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.

The phonological determinants of tone in English loanwords in Mandarin

This paper presents the results of a corpus study and an online loanword adaptation experiment examining the tonal adaptation of English loanwords in Mandarin. Using maximum entropy models, I control for the substantial influences of lexical tone distributions and standardisation, and uncover phonological determinants of tone beyond these lexical and conventional factors. The most important phonological determinant of tone in the corpus was English voicing, while in the experiment it was English stress-aligned pitch contours. I argue that these distinct tonal adaptation patterns constitute two different perceptual mappings, one from F0 perturbations to tone and the other from English intonation to tone, both arising due to particular borrowing contexts. I suggest that increasingly close contact between English and Mandarin may lead to more intonation-driven tonal adaptation in the latest wave of borrowing. The maximum entropy approach holds promise for the analysis of complex cases of tonal adaptation in other languages.

Macroscopic quantities of collective brain activity during wakefulness and anesthesia

The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

SiGMoiD: A superstatistical generative model for binary data

AbbstractIn modern biological physics, there is a great interest in building generative probabilistic models for ensembles of covarying binary variables. A popular approach is to use the maximum entropy principle. Here, one builds generative models that use as constraints lower level statistics estimated from the data. While extremely popular, maximum entropy models have conceptual as well as practical issues; they rely on the modelers’ choice of constraints and are computationally expensive to infer when the number of variables is large (n > 100). Here, we address both these issues with Superstastistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy based framework where we imagine that the data as arising from superstatistical system; individual binary variables are coupled to the same bath whose intensive variables fluctuate from sample to sample. Moreover, instead of choosing the constraints, in SiGMoiD we choose only the number of constraints and let the algorithm infer them from the data itself. Notably, we show that SiGMoiD is orders of magnitude faster than current maximum entropy-based models and allows us to model collections of very large number of binary variables. We also discuss future directions.

Modelling the potential non-breeding distribution of Spoon-billed Sandpiper Calidris pygmaea

Summary The Spoon-billed Sandpiper Calidris pygmaea is a ‘Critically Endangered’ migratory shorebird. The species faces an array of threats in its non-breeding range, making conservation intervention essential. However, conservation efforts are reliant on identifying the species’ key stopover and wintering sites. Using Maximum Entropy models, we predicted Spoon-billed Sandpiper distribution across the non-breeding range, using data from recent field surveys and satellite tracking. Model outputs suggest only a limited number of stopover sites are suitable for migrating birds, with sites in the Yellow Sea and on the Jiangsu coast in China highlighted as particularly important. All the previously known core wintering sites were identified by the model including the Ganges-Brahmaputra Delta, Nan Thar Island and the Gulf of Mottama. In addition, the model highlighted sites subsequently found to be occupied, and pinpointed potential new sites meriting investigation, notably on Borneo and Sulawesi, and in parts of India and the Philippines. A comparison between the areas identified as most likely to be occupied and protected areas showed that very few locations are covered by conservation designations. Known sites must be managed for conservation as a priority, and potential new sites should be surveyed as soon as is feasible to assess occupancy status. Site protection should take place in concert with conservation interventions including habitat management, discouraging hunting, and fostering alternative livelihoods.

Changes in weak pair-wise correlations during running reshapes network state in the main olfactory bulb

Neural codes for sensory representations are thought to reside in a broader space defined by the patterns of spontaneous activity that occur when stimuli are not being presented. To understand the structure of this spontaneous activity in the olfactory system, we performed high-density recordings of population activity in the main olfactory bulb of awake mice. We found that spontaneous activity patterns of ensembles of mitral and tufted (M/T) cells in the main olfactory bulb changed dramatically during locomotion, including decreases in pairwise correlations between neurons and increases in the entropy of the population. Maximum entropy models of the ensemble activity revealed that pair-wise interactions were better at predicting patterns of activity when the animal was stationary than while running, suggesting that higher order (3rd, 4th order) interactions between neurons shape activity during locomotion. Taken together, we found that locomotion influenced the structure of spontaneous population activity at the earliest stages of olfactory processing, 1 synapse away from the sensory receptors in the nasal epithelium.New and NoteworthyThe organization and structure of spontaneous population activity in the olfactory system places constraints of how odor information is represented. Using high-density electrophysiological recordings of mitral and tufted cells, we found that running increases the dimensionality of spontaneous activity, implicating higher-order interactions among neurons during locomotion. Behavior thus flexibly alters neuronal activity at the earliest stages of sensory processing.

A network model of freight data with spatial dependence

In this article, we analyse the origin–destination matrix arising from freight flows that occur in single-mode transport networks and compare unbiased maximum-entropy models of the corresponding networks. An original model based on earlier results allows to reconstruct a weighted network, from degree and strength sequences, taking distances into account. As an application, the properties of the European railroad freight are analysed in detail in year 2010, with a focus on spatial effects.

MPF–BML: a standalone GUI-based package for maximum entropy model inference

Summary Learning underlying correlation patterns in data is a central problem across scientific fields. Maximum entropy models present an important class of statistical approaches for addressing this problem. However, accurately and efficiently inferring model parameters are a major challenge, particularly for modern high-dimensional applications such as in biology, for which the number of parameters is enormous. Previously, we developed a statistical method, minimum probability flow–Boltzmann Machine Learning (MPF–BML), for performing fast and accurate inference of maximum entropy model parameters, which was applied to genetic sequence data to estimate the fitness landscape for the surface proteins of human immunodeficiency virus and hepatitis C virus. To facilitate seamless use of MPF–BML and encourage more widespread application to data in diverse fields, we present a standalone cross-platform package of MPF–BML which features an easy-to-use graphical user interface. The package only requires the input data (protein sequence data or data of multiple configurations of a complex system with large number of variables) and returns the maximum entropy model parameters. Availability and implementation The MPF–BML software is publicly available under the MIT License at https://github.com/ahmedaq/MPF-BML-GUI. Supplementary information Supplementary data are available at Bioinformatics online.