Out-of-equilibrium versus dynamical and thermodynamical transitions for a model protein

Equilibrium and out-of-equilibrium transitions of an off-lattice protein model have been identified and studied. In particular, the out-of-equilibrium dynamics of the protein undergoing mechanical unfolding is investigated, and by using a work fluctuation relation, the system free energy landscape is evaluated. Three different structural transitions are identified along the unfolding pathways. Furthermore, the reconstruction of the the free and potential energy profiles in terms of inherent structure formalism allows us to put in direct correspondence these transitions with the equilibrium thermal transitions relevant for protein folding/unfolding. Through the study of the fluctuations of the protein structure at different temperatures, we identify the dynamical transitions, related to configurational rearrangements of the protein, which are precursors of the thermal transitions.Comment: Proceedings of the "YKIS 2009 : Frontiers in Nonequilibrium Physics" conference in Kyoto, August 2009. To appear in Progress of Theoretical Physics Supplemen

Unfolding times for proteins in a force clamp

The escape process from the native valley for proteins subjected to a constant stretching force is examined using a model for a Beta-barrel. For a wide range of forces, the unfolding dynamics can be treated as one-dimensional diffusion, parametrized in terms of the end-to-end distance. In particular, the escape times can be evaluated as first passage times for a Brownian particle moving on the protein free-energy landscape, using the Smoluchowski equation. At strong forces, the unfolding process can be viewed as a diffusive drift away from the native state, while at weak forces thermal activation is the relevant mechanism. An escape-time analysis within this approach reveals a crossover from an exponential to an inverse Gaussian escape-time distribution upon passing from weak to strong forces. Moreover, a single expression valid at weak and strong forces can be devised both for the average unfolding time as well as for the corresponding variance. The analysis offers a possible explanation of recent experimental findings for ddFLN4 and ubiquitin.Comment: 6 pages, 4 figures, submitted for pubblication to Physical Review Letter

Death and rebirth of neural activity in sparse inhibitory networks

In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of the neural activity, as expected, but it can also promote neural reactivation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neurons' death). However, the random pruning of the connections is able to reverse the action of inhibition, i.e. in a sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of the neurons (neurons' rebirth). Thus the number of firing neurons reveals a minimum at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by the neurons with higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving an analytic mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, the system passes from a perfectly regular evolution to an irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum.Comment: 19 pages, 10 figures, submitted to NJ

Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks

It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrateComment: 39 pages, 15 figures, to appear in PLOS Computational Biolog

Reconstructing the free energy landscape of a mechanically unfolded model protein

The equilibrium free energy landscape of an off-lattice model protein as a function of an internal (reaction) coordinate is reconstructed from out-of-equilibrium mechanical unfolding manipulations. This task is accomplished via two independent methods: by employing an extended version of the Jarzynski equality (EJE) and the protein inherent structures (ISs). In a range of temperatures around the ``folding transition'' we find a good quantitative agreement between the free energies obtained via EJE and IS approaches. This indicates that the two methodologies are consistent and able to reproduce equilibrium properties of the examined system. Moreover, for the studied model the structural transitions induced by pulling can be related to thermodynamical aspects of folding

Double coherence resonance in neuron models driven by discrete correlated noise

We study the influence of correlations among discrete stochastic excitatory or inhibitory inputs on the response of the FitzHugh-Nagumo neuron model. For any level of correlation the emitted signal exhibits at some finite noise intensity a maximal degree of regularity, i.e., a coherence resonance. Furthermore, for either inhibitory or excitatory correlated stimuli a {\it Double Coherence Resonance} (DCR) is observable. DCR refers to a (absolute) maximum coherence in the output occurring for an optimal combination of noise variance and correlation. All these effects can be explained by taking advantage of the discrete nature of the correlated inputs.Comment: 4 pages, 3 figures in eps, to appear in Physical Review Letter

Collective behavior of heterogeneous neural networks

We investigate a network of integrate-and-fire neurons characterized by a distribution of spiking frequencies. Upon increasing the coupling strength, the model exhibits a transition from an asynchronous regime to a nontrivial collective behavior. At variance with the Kuramoto model, (i) the macroscopic dynamics is irregular even in the thermodynamic limit, and (ii) the microscopic (single-neuron) evolution is linearly stable.Comment: 4 pages, 5 figure

Investigating Gender Bias in Machine Translation. A Case Study between English and Italian

Neural machine translation systems have substantially improved the quality of translation output, yet many issues still need to be addressed: one major problem to be addressed concerns the presence of gender bias, the prejudice against one gender based on the perception that women and men are not equal. In this work, we will manually evaluate the translation of a sentence pattern previously employed for similar purposes by Escud\ue9 Font and Costa-juss\ue0 (2019) in the English-Italian language combination using two of the most popular MT systems, DeepL and Google Translate. The sets of sentences include 40 male- and female-dominated occupations and three adjectives, beautiful, wise and strong. The aim of this study is to evaluate gender bias, that becomes apparent when translating from a gender-neutral language to a gender-marked language, and to verify whether adjectives usually associated with female or male entities can affect the final MT output. Furthermore, we provide some relevant insights about gender bias in MT for post-editors and MT users, with a particular focus on the under-representation of women in the Italian language

Coherence resonance due to correlated noise in neuronal models

We study the regularity of noise-induced excitations in the FitzHugh-Nagumo (FHN) neuronal model subject to excitatory and inhibitory high-frequency input with and without correlations. For each value of the correlation a relative maximum of spike coherence can be observed for intermediate noise strengths (coherence resonance). Moreover, the FHN system exhibits an absolute maximum of coherent spiking for intermediate values of both the noise amplitude and the strength of correlation (double coherence resonance). The underlying mechanisms can be explained by means of the discrete input statistics