Quantitative Biology

This paper reviews the main perspectives of language acquisition and language comprehension. In language acquisition, we have reviewed the different types of language acquisitions like first language acquisition, second language acquisition, sign language acquisition and skill acquisition. The experimental techniques for neurolinguistic acquisition detection is also discussed. The findings of experiments for acquisition detection is also discussed, it includes the region of brain activated after acquisition. Findings shows that the different types of acquisition involve different regions of the brain. In language comprehension, native language comprehension and bilingual's comprehension has been considered. Comprehension involve different brain regions for different sentence or word comprehension depending upon their semantic and syntax. The different fMRIEEG analysis techniques (statistical or graph theoretical) are also discoursed in our review. Tools for neurolinguistics computations are also discussed.

Hippocampal reverse replay is thought to contribute to learning, and particularly reinforcement learning, in animals. We present a computational model of learning in the hippocampus that builds on a previous model of the hippocampal-striatal network viewed as implementing a three-factor reinforcement learning rule. To augment this model with hippocampal reverse replay, a novel policy gradient learning rule is derived that associates place cell activity with responses in cells representing actions. This new model is evaluated using a simulated robot spatial navigation task inspired by the Morris water maze. Results show that reverse replay can accelerate learning from reinforcement, whilst improving stability and robustness over multiple trials. As implied by the neurobiological data, our study implies that reverse replay can make a significant positive contribution to reinforcement learning, although learning that is less efficient and less stable is possible in its absence. We conclude that reverse replay may enhance reinforcement learning in the mammalian hippocampal-striatal system rather than provide its core mechanism.

It is recently demonstrated that cortical activity can track the time courses of phrases and sentences during speech listening. Here, we propose a plausible neural processing framework to explain this phenomenon. It is argued that the brain maintains the neural representation of a linguistic unit, i.e., a word or a phrase, in a processing buffer until the unit is integrated into a higher-level structure. After being integrated, the unit is removed from the buffer and becomes activated long-term memory. In this model, the duration each unit is maintained in the processing buffer depends on the linguistic structure of the speech input. It is shown that the number of items retained in the processing buffer follows the time courses of phrases and sentences, in line with neurophysiological data, whether the syntactic structure of a sentence is mentally parsed using a bottom-up or top-down predictive model. This model generates a range of testable predictions about the link between linguistic structures, their dynamic psychological representations and their neural underpinnings.

We summarize the original formulation of the free energy principle, and highlight some technical issues. We discuss how these issues affect related results involving generalised coordinates and, where appropriate, mention consequences for and reveal, up to now unacknowledged, differences to newer formulations of the free energy principle. In particular, we reveal that various definitions of the "Markov blanket" proposed in different works are not equivalent. We show that crucial steps in the free energy argument which involve rewriting the equations of motion of systems with Markov blankets, are not generally correct without additional (previously unstated) assumptions. We prove by counterexample that the original free energy lemma, when taken at face value, is wrong. We show further that this free energy lemma, when it does hold, implies equality of variational density and ergodic conditional density. The interpretation in terms of Bayesian inference hinges on this point, and we hence conclude that it is not sufficiently justified. Additionally, we highlight that the variational densities presented in newer formulations of the free energy principle and lemma are parameterised by different variables than in older works, leading to a substantially different interpretation of the theory. Note that we only highlight some specific problems in the discussed publications. These problems do not rule out conclusively that the general ideas behind the free energy principle are worth pursuing.

This tutorial paper refers to the use of graph-theoretic concepts for analyzing brain signals. For didactic purposes it splits into two parts: theory and application. In the first part, we commence by introducing some basic elements from graph theory and stemming algorithmic tools, which can be employed for data-analytic purposes. Next, we describe how these concepts are adapted for handling evolving connectivity and gaining insights into network reorganization. Finally, the notion of signals residing on a given graph is introduced and elements from the emerging field of graph signal processing (GSP) are provided. The second part serves as a pragmatic demonstration of the tools and techniques described earlier. It is based on analyzing a multi-trial dataset containing single-trial responses from a visual ERP paradigm. The paper ends with a brief outline of the most recent trends in graph theory that are about to shape brain signal processing in the near future and a more general discussion on the relevance of graph-theoretic methodologies for analyzing continuous-mode neural recordings.

Han et al. (2020) reported a behavioral experiment that assessed the extent to which the human visual system can identify novel images at unseen retinal locations (what the authors call "intrinsic translation invariance") and developed a novel convolutional neural network model (an Eccentricity Dependent Network or ENN) to capture key aspects of the behavioral results. Here we show that their analysis of behavioral data used inappropriate baseline conditions, leading them to underestimate intrinsic translation invariance. When the data are correctly interpreted they show near complete translation tolerance extending to 14° in some conditions, consistent with earlier work (Bowers et al., 2016) and more recent work Blything et al. (in press). We describe a simpler model that provides a better account of translation invariance.

This comment is aimed to point out that the recent work due to Kim, et al. in which the clinical and experiential assessment of a brain network model suggests that asymmetry of synchronization suppression is the key mechanism of hysteresis has coupling with our theoretical hysteresis model of unconscious-conscious interconnection based on dynamics on m-adic trees.

Covert speech, the silent production of words in the mind, has been studied increasingly to understand and decode thoughts. This task has often been compared to speech perception as it brings about similar topographical activation patterns in common brain areas. In studies of speech comprehension, neural oscillations are thought to play a key role in the sampling of speech at varying temporal scales. However, very little is known about the role of oscillations in covert speech. In this study, we aimed to determine to what extent each oscillatory frequency band is used to process words in covert speech and speech perception tasks. Secondly, we asked whether the {\theta} and {\gamma} activity in the two tasks are related through phase-amplitude coupling (PAC). First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes were conducted to determine statistical distinctions in frequency and time. While the perception task dynamically uses all frequencies with more prominent {\theta} and {\gamma} activity, the covert task favoured higher frequencies with significantly higher {\gamma} activity than perception. Moreover, the perception condition produced significant {\theta}-{\gamma} PAC suggesting a linkage of syllabic and phonological sampling. Although this was found to be suppressed in the covert condition, we found significant pseudo-coupling between perception {\theta} and covert speech {\gamma}. We report that covert speech processing is largely conducted by higher frequencies, and that the {\gamma}- and {\theta}-bands may function similarly and differently across tasks, respectively. This study is the first to characterize covert speech in terms of neural oscillatory engagement. Future studies are directed to explore oscillatory characteristics and inter-task relationships with a more diverse vocabulary.

Graphene conductive properties have been long exploited in the field of organic photovoltaics and optoelectronics by the scientific community worldwide. We engineered and characterized a hybrid biointerface in which graphene is coupled with photosensitive polymers, and tested its ability to elicit lighttriggered neural activity modulation in primary neurons and blind retina explants. We designed such a graphene-based device by modifying a photoactive P3HT-based retinal interface, previously reported to rescue light sensitivity in blind rodents, with a CVD graphene layer replacing the conductive PEDOT:PSS layer to enhance charge separation. The new graphene-based device was characterized for its electrochemical features and for the ability to photostimulate primary neurons and blind retina explants, while preserving biocompatibility. Light-triggered responses, recorded by patch-clamp in vitro or MEA ex vivo, show a stronger light-transduction efficiency when the neurons are interfaced with the graphene-based device with respect to the PEDOT:PSS-based one. The possibility to ameliorate flexible photo-stimulating devices via the insertion of graphene, paves the way for potential biomedical applications of graphenebased neuronal interfaces in the context of retinal implants.

When working with task-related fMRI data, one of the most crucial parts of the data analysis consists of determining a proper estimate of the BOLD response. The following document presents a lite model for the Hemodynamic Response Function HRF. Between other advances, the proposed model present less number of parameters compared to other similar HRF alternative, which reduces its optimization complexity and facilitates its potential applications.