Cortical feedback and gating in odor discrimination and generalization

A central question in neuroscience is how context changes perception. In the olfactory system, for example, experiments show that task demands can drive divergence and convergence of cortical odor responses, likely underpinning olfactory discrimination and generalization. Here, we propose a simple statistical mechanism for this effect based on unstructured feedback from the central brain to the olfactory bulb, which represents the context associated with an odor, and sufficiently selective cortical gating of sensory inputs. Strikingly, the model predicts that both convergence and divergence of cortical odor patterns should increase when odors are initially more similar, an effect reported in recent experiments. The theory in turn predicts reversals of these trends following experimental manipulations and in neurological conditions that increase cortical excitability.

Experimental and theoretical assessment of protonated Hoogsteen 9-methylguanine–1-methylcytosine base-pair dissociation: kinetics within a statistical reaction framework

Statistical mechanism-driven dissociation of Hoogsteen guanine–cytosine base pair.

Social Network Modelling using tools of Statistical Mechanics

The objective of this paper is to study a treatment to social network analysis using the principles of statistical mechanics. After revisiting the popular models and random graph frameworks of complex networks, a formalism to statistical mechanism based on the conventional concepts like phase space, interactions and ensembles is devised. Specific machine learning techniques are employed for the purpose of figuring out the relevant phase-space equations. Thereafter, specific applications of the formalism is explored in the context of business partnership optimization and disease transmission. Several analogues with the statistical mechanics treatment of thermodynamics have also been made.

Cortical feedback and gating in olfactory pattern completion and separation

A central question in neuroscience is how context changes perception of sensory stimuli. In the olfactory system, for example, experiments show that task demands can drive merging and separation of cortical odor responses, which underpin olfactory generalization and discrimination. Here, we propose a simple statistical mechanism for this effect, based on unstructured feedback from the central brain to the olfactory bulb, representing the context associated with an odor, and sufficiently selective cortical gating of sensory inputs. Strikingly, the model predicts that both pattern separation and completion should increase when odors are initially more similar, an effect reported in recent experiments. The theory predicts reversals of these trends following experimental manipulations and neurological conditions such as Alzheimer’s disease that increase cortical excitability.

Statistical mechanism of passenger mobility behaviors for different transportations

Passengers’ boarding time interval is of great significance for analysis of collective mobility behaviors. In this paper, we empirically investigate the boarding time interval of mobility behaviors based on three large-scale reservation records of passengers traveling by three different types of transportation from a travel agency platform, namely airplane, intercity bus and car rental. The statistical results show that similar properties exist in the passengers’ mobility behaviors, for example, there are similar burstiness [Formula: see text] and memory [Formula: see text] for different time interval distribution, which indicates that the passengers’ mobility behaviors are periodical. Furthermore, we present a probability model to regenerate the empirical results by assuming that the passengers’ next boarding time interval will generate between a short time of 1–7 days with probability [Formula: see text] and a random long time with probability [Formula: see text]. The simulation results show that the presented model can reproduce the burstiness and memory effect of the boarding time interval when [Formula: see text] for three empirical datasets, which suggests the periodical behaviors with the probability [Formula: see text]. This work helps in deeply understanding the regularity of human mobility behaviors.

Rational arbitration between statistics and rules in human sequence learning

Detecting and learning temporal regularities is essential to accurately predict the future. Past research indicates that humans are sensitive to two types of sequential regularities: deterministic rules, which afford sure predictions, and statistical biases, which govern the probabilities of individual items and their transitions. How does the human brain arbitrate between those two types? We used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence learning. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over several distinct hypothesis spaces, underlies the human capability to learn both statistics and rules.

A Plausible Accelerating Function of Intermediate States in Cancer Metastasis

Epithelial-to-mesenchymal transition (EMT) is a fundamental cellular process and plays an essential role in development, tissue regeneration, and cancer metastasis. Interestingly, EMT is not a binary process but instead proceeds with multiple partial intermediate states. However, the functions of these intermediate states are not fully understood. Here, we focus on a general question about how the number of partial EMT states affects cell transformation. First, by fitting a hidden Markov model of EMT with experimental data, we propose a statistical mechanism for EMT in which many unobservable microstates may exist within one of the observable macrostates. Furthermore, we find that increasing the number of intermediate states can accelerate the EMT process and that adding parallel paths or transition layers accelerates the process even further. Last, a stabilized intermediate state traps cells in one partial EMT state. This work advances our understanding of the dynamics and functions of EMT plasticity during cancer metastasis.