Sensory States Research Articles

ASSESSMENT OF THE FUNCTIONAL STATE OF THE VISUAL SYSTEM OF HIGHER SCHOOL STUDENTS

The functional state of the visual system of higher school students in conditions of stable intensification (technical, informational, social) is advisable, provided that its effectiveness is achieved. It reflects not so much the efficiency of the functional system of the organism, which characterizes the achievement of the consequence, expressed in a specific result, as the property of the system itself to fulfill the set goal under the given conditions with the required quality. The problem of the state of the visual system of students as a common resource of the visual analyzer is transferred to the field of psychophysiology and pedagogy. Indicators of the effectiveness of the state of the visual system of students characterize the degree of adaptability of the system to the performance of the assigned tasks and are generalizing indicators of the optimal functioning of the visual system. In psychophysiological research of an applied nature, such indicators are considered to be the functional level of the system, the stability of the reaction and the level of its functional capabilities as predictors of the sensory state. A particularly productive area of psychophysiological research of vision in this regard can be the study of the distribution physiological conditions of the visual system. In general, scientific research of the visual system as a resource for information processing is developing similarly to the research of the visual analyzer. In the models of the selection education system, the concept of the visual process as a filtering mechanism for the selection of information has not been sufficiently studied. In resource models, the theories of the resources of the visual system as sensory perception have come to replace the common nonspecific resources.

Bharata’s <i>bibhatsa-rasa</i>, Shudraka’s <i>Mrcchakatika</i> and Christian missionaries’ disgust

The ancient Indian aesthetic theory identifies bībhatsa, “disgust / aversion”, as one of the nine sensory states that determine the mood of dramatic and poetic works and by means of visual / verbal techniques affect a spectator/a reader. This term from Bharata’s Nāṭyaśāstra was adopted by Christian missions in India and used as an argument against the cultural traditions of the conquered subcontinent. The translation into Marathi (1864) of The Little Clay Cart, a Sanskrit drama by Shudraka, became the object of violent public controversy initiated by Rev. Henry Ballantine who found the image of the protagonist Vasantasena, a hereditary courtesan, “disgusting” and the play “shameful”. The final subjugation ofIndia after the defeat of the Sepoy Mutiny (1857–1858), and its transition under the British crown rule intensified the process of emotional indoctrination of subjects by resort to the notion of “disgusting” understood as anything not compatible with the Christian morality norms.

The Computational, Pharmacological, and Physiological Determinants of Sensory Learning under Uncertainty

SummaryThe ability to represent and respond to uncertainty is fundamental to human cognition and decision-making. Noradrenaline (NA) is hypothesized to play a key role in coordinating the sensory, learning, and physiological states necessary to adapt to a changing world, but direct evidence for this is lacking in humans. Here, we tested the effects of attenuating noradrenergic neurotransmission on learning under uncertainty. We probed the effects of the β-adrenergic receptor antagonist propranolol (40 mg) using a between-subjects, double-blind, placebo-controlled design. Participants performed a probabilistic associative learning task, and we employed a hierarchical learning model to formally quantify prediction errors about cue-outcome contingencies and changes in these associations over time (volatility). Both unexpectedness and noise slowed down reaction times, but propranolol augmented the interaction between these main effects such that behavior was influenced more by prior expectations when uncertainty was high. Computationally, this was driven by a reduction in learning rates, with people slower to update their beliefs in the face of new information. Attenuating the global effects of NA also eliminated the phasic effects of prediction error and volatility on pupil size, consistent with slower belief updating. Finally, estimates of environmental volatility were predicted by baseline cardiac measures in all participants. Our results demonstrate that NA underpins behavioral and computational responses to uncertainty. These findings have important implications for understanding the impact of uncertainty on human biology and cognition.

Future climates: Markov blankets and active inference in the biosphere.

We formalize the Gaia hypothesis about the Earth climate system using advances in theoretical biology based on the minimization of variational free energy. This amounts to the claim that non-equilibrium steady-state dynamics-that underwrite our climate-depend on the Earth system possessing a Markov blanket. Our formalization rests on how the metabolic rates of the biosphere (understood as Markov blanket's internal states) change with respect to solar radiation at the Earth's surface (i.e. external states), through the changes in greenhouse and albedo effects (i.e. active states) and ocean-driven global temperature changes (i.e. sensory states). Describing the interaction between the metabolic rates and solar radiation as climatic states-in a Markov blanket-amounts to describing the dynamics of the internal states as actively inferring external states. This underwrites climatic non-equilibrium steady-state through free energy minimization and thus a form of planetary autopoiesis.

Pain phenotypes classified by machine learning using electroencephalography features

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

A Compositional Neural Architecture for Language

Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception-action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de)compositional meaning. The model's architecture-a multidimensional coordinate system based on neurophysiological models of sensory processing-proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations, and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation.

Neurobehavioural signatures in race car driving: a case study

Recent technological developments in mobile brain and body imaging are enabling new frontiers of real-world neuroscience. Simultaneous recordings of body movement and brain activity from highly skilled individuals as they demonstrate their exceptional skills in real-world settings, can shed new light on the neurobehavioural structure of human expertise. Driving is a real-world skill which many of us acquire to different levels of expertise. Here we ran a case-study on a subject with the highest level of driving expertise—a Formula E Champion. We studied the driver’s neural and motor patterns while he drove a sports car on the “Top Gear” race track under extreme conditions (high speed, low visibility, low temperature, wet track). His brain activity, eye movements and hand/foot movements were recorded. Brain activity in the delta, alpha, and beta frequency bands showed causal relation to hand movements. We herein demonstrate the feasibility of using mobile brain and body imaging even in very extreme conditions (race car driving) to study the sensory inputs, motor outputs, and brain states which characterise complex human skills.

Acute acquired concomitant esotropia associated with myopia: is the condition related to any binocular function failure?

To evaluate the sensory motor state, accommodation, and accommodative convergence to accommodation ratio (AC/A) in a cohort of non-amblyopic myopes with acquired concomitant esotropia (Bielschowsky esotropia (BE)). Refraction, near and far deviation, fusional amplitude (FA), near point of accommodation (NPA), AC/A (gradient method), and stereopsis were measured in a cohort of 26 patients (25 phakic and 1 pseudophakic, age: 14-60years) with BE, prospectively recruited from January to September 2019 at St. Orsola-Malpighi Hospital, Bologna, Italy. The Pearson's correlation coefficient was used to correlate distance with near deviation, distance with near divergence FA and angle of deviation with FA (statistical significance: p values < 0.05). Myopia range was 0-17.5 diopters (D) spherical equivalent (mean: 4.4D). A positive correlation resulted between distance (mean: 23.7 prism diopters/PD, range: 4-40PD) and near (mean: 23.7PD, range: 2-45PD) deviation (p < 0.00001) and between distance (mean: 12.6PD, range 0-34PD) and near (mean: 17.0PD, range 3-36PD) divergence FA (p < 0.00001). A non-significant correlation resulted between angle of deviation and divergent FA at near (p = 0.07) and distance (p = 0.13) fixation. NPA was within normal limits for age. AC/C ratio range was 0-8 (mean: 3). Twenty-three patients showed Randot stereopsis. BE shows high variability in the age of onset, degree of associated myopia, AC/A, and divergent FA. A little stronger near divergent FA can justify the better compensation at near fixation. These results show that uncorrected myopia, accommodation, and divergence paralysis do not concur with the genesis of BE.

A role for representations in inflexible behavior

Representationalists have routinely expressed skepticism about the idea that inflexible responses to stimuli (e.g. reflexive responses like the pupillary light reflex) are to be explained in representational terms. Representations are supposed to be more than just causal mediators in the chain of events stretching from stimulus to response, and it is difficult to see how the sensory states driving reflexes are doing more than playing the role of causal intermediaries. One popular strategy for distinguishing representations from mere causal mediators is to require that representations are decoupled from specific stimulus conditions. I believe this requirement on representation is mistaken and at odds with explanatory practices in sensory ecology. Even when sensory states have the job of coordinating a specific output with a specific input, we can still find them doing the work of representations, carrying information needed for organisms to respond successfully to environmental conditions. We can uncover information at work by intervening specifically on the information conveyed by sensory states, leaving their causal role undisturbed.

Toward security monitoring of industrial Cyber-Physical systems via hierarchically distributed intrusion detection

Industrial Cyber-physical systems (ICPSs), integrating communication, computation and control of industrial processes are referred to as a core technology to approach the Industry 4.0. Ensuring the ICPS security is of paramount importance in smart manufacturing. Considering the characteristics of large-scale, geographically-dispersed and multi-dimensional heterogeneous, federated and life-critical natures of ICPSs, this paper investigates a hierarchically distributed intrusion detection scheme that seeks to achieve the all-round safety protection of ICPSs according to the system structure and attacking types of each ICPS layer. For physical system-relevant perceptual executive layer, potential and covert attacks are detected by the clustered sensory system state residual anomaly monitoring based on a process noise and measurement noise-adaptive Kalman filter (PNMN-AKF). PNMN-AKF can perform a joint recursive estimation of dynamic system states, time-varying process and measurement noise covariance matrices by the variational Bayes approximation framework. In cyberspace, potential cyber-attacks are detected by the anomaly monitoring of the statistical distribution of the network transmission characteristics of data transmission layer by introducing a forgetting factor-induced recursive Gaussian mixture model (FF-RGMM). In the application control layer, a regularized sparse deep belief network model is introduced to characterize the misuse behavior for detecting potential attacks. Extensive validation and comparative experiments have been conducted on a numerical simulation system and a comprehensive ICPS simulation platform by using OPNET and a commonly-used benchmark simplified Tennessee Eastman process (STEP) based on Matlab/Simulink. Experimental results demonstrate that the proposed hierarchically distributed intrusion detection method can efficiently recognize potential and covert cyber-attacks in each ICPSs link with low false alarm rate and missing detection rate, which lays a foundation for the overall security monitoring of ICPSs.

Valence and State-Dependent Population Coding in Dopaminergic Neurons in the Fly Mushroom Body

Neuromodulation permits flexibility of synapses, neural circuits, and ultimately behavior. One neuromodulator, dopamine, has been studied extensively in its role as a reward signal during learning and memory across animal species. Newer evidence suggests that dopaminergic neurons (DANs) can modulate sensory perception acutely, thereby allowing an animal to adapt its behavior and decision making to its internal and behavioral state. In addition, some data indicate that DANs are not homogeneous but rather convey different types of information as a heterogeneous population. We have investigated DAN population activity and how it could encode relevant information about sensory stimuli and state by taking advantage of the confined anatomy of DANs innervating the mushroom body (MB) of the fly Drosophila melanogaster. Using invivo calcium imaging and a custom 3D image registration method, we found that the activity of the population of MB DANs encodes innate valence information of an odor or taste as well as the physiological state of the animal. Furthermore, DAN population activity is strongly correlated with movement, consistent with a role of dopamine in conveying behavioral state to the MB. Altogether, our data and analysis suggest that DAN population activities encode innate odor and taste valence, movement, and physiological state in a MB-compartment-specific manner. We propose that dopamine shapes innate perception through combinatorial population coding of sensory valence, physiological, and behavioral context.

View-Action Representation Learning for Active First-Person Vision

In visual navigation, a moving agent equipped with a camera is traditionally controlled by an input action and the estimation of the features from a sensory state (i.e. the camera view) is treated as a pre-processing step to perform high-level vision tasks. In this paper, we present a representation learning approach that, instead, considers both state and action as inputs. We condition the encoded feature from the state transition network on the action that changes the view of the camera, thus describing the scene more effectively. Specifically, we introduce an action representation module that generates decoded higher dimensional representations from an input action to increase the representational power. We then fuse the output from the action representation module with the intermediate response of the state transition network that predicts the future state. To enhance the discrimination capability among predictions from different input actions, we further introduce triplet ranking loss and $N$ -tuplet loss functions, which in turn can be integrated with the regression loss. We demonstrate the proposed representation learning approach in reinforcement and imitation learning-based mapless navigation tasks, where the camera agent learns to navigate only through the view of the camera and the performed action, without external information.

Rapid and transient enhancement of thalamic information transmission induced by vagus nerve stimulation

Objective. Vagus nerve stimulation (VNS) has been FDA-approved as a long-term, therapeutic treatment for multiple disorders, including pharmacoresistant epilepsy and depression. Here we elucidate the short-term effects of VNS on sensory processing. Approach. We employed an information theoretic approach to examine the effects of VNS on thalamocortical transmission of sensory-related information along the somatosensory pathway. Main results. We found that VNS enhanced the selectivity of the response of thalamic neurons to specific kinetic features in the stimuli, resulting in a significant increase in the efficiency and rate of stimulus-related information conveyed by thalamic spikes. VNS-induced improvements in thalamic sensory processing coincided with a decrease in thalamic burst firing. Importantly, we found VNS-induced enhancement of sensory processing had a rapid onset and offset, completely disappearing one minute after cessation of VNS. The timescales of these effects indicate against an underlying mechanism involving long-term neuroplasticity. We found several patterns of VNS (tonic, standard duty-cycle, and fast duty-cycle) all induced similar improvements in sensory processing. Under closer inspection we noticed that due to the fast timescale of VNS effects on sensory processing, standard duty-cycle VNS induced a fluctuating sensory processing state which may be sub-optimal for perceptual behavior. Fast duty-cycle VNS and continuous, tonic VNS induced quantitatively similar improvements in thalamic information transmission as standard duty-cycle VNS without inducing a fluctuating thalamic state. Further, we found the strength of VNS-induced improvements in sensory processing increased monotonically with amplitude and frequency of VNS. Significance. These results demonstrate, for the first time, the feasibility of utilizing specific patterns of VNS to rapidly improve sensory processing and confirm fast duty-cycle and tonic patterns as optimal for this purpose, while showing standard duty-cycle VNS causes non-optimal fluctuations in thalamic state.

Astrocytes shape the plastic response of adult cortical neurons to vision loss.

Astrocytes are vital for preserving correct brain functioning by continuously sustaining neuronal activity and survival. They are in contact with multiple synapses at once allowing the expansion of local synaptic events into activity changes in neuronal networks. Furthermore, cortical astrocytes integrate local sensory inputs and behavioral state. From an anatomical, molecular, and functional perspective, astrocytes are thus ideal candidates to influence complex large-scale brain mechanisms such as plasticity. We collected evidence for the astrocytic potential for plasticity modulation, using the monocular enucleation (ME) mouse model of visual cortex plasticity. The impact of one-eyed vision involves the functional recruitment of the deprived visual cortex by the spared senses within a 7-week time frame, reflecting a substantial change in sensory information processing. In visually deprived cortex, a swift upregulation in Aldh1l1-positive astrocyte density lasts until maximal functional recovery is reached. Transient metabolic silencing of visual cortex astrocytes at the time of ME induction, through intracranial fluorocitrate injections, reveals that astrocytes are required on site to achieve adequate long-term neuronal reactivation. In addition, chronic stimulation by Gi but not Gq G-protein coupled receptor activation of local astrocytes boosts the cortical plasticity phenomenon. Hence, functional manipulation of protoplasmic astrocytes has long-lasting effects on the functional recovery of cortical neurons upon sensory loss, possibly by influencing the neuronal threshold to reactivate. Together, our results highlight an integral role for astrocytes in mediating adult cortical plasticity and unmask astrocyte specific Gi signaling as an interesting therapeutic pathway for brain plasticity regulation.

How does the non-conscious become conscious?

In this My Word, Joseph LeDoux describes how his work as a graduate student got him interested in human consciousness. Although he has not studied this topic since 1970s, he never stopped thinking and writing about it during his four-decade career exploring how non-conscious processes involving the amygdala detect and respond to danger. Here, he tells us what is on his mind about consciousness these days.

Evaluating the neurophysiological evidence for predictive processing as a model of perception.

For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long‐standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

Continuous Representations of Speed by Striatal Medium Spiny Neurons.

The striatum is critical for controlling motor output. However, it remains unclear how striatal output neurons encode and facilitate movement. A prominent theory suggests that striatal units encode movements in bursts of activity near specific events, such as the start or end of actions. These bursts are theorized to gate or permit specific motor actions, thereby encoding and facilitating complex sequences of actions. An alternative theory has suggested that striatal neurons encode continuous changes in sensory or motor information with graded changes in firing rate. Supporting this theory, many striatal neurons exhibit such graded changes without bursting near specific actions. Here, we evaluated these two theories in the same recordings of mice (both male and female). We recorded single-unit and multiunit activity from the dorsomedial striatum of mice as they spontaneously explored an arena. We observed both types of encoding, although continuous encoding was more prevalent than bursting near movement initiation or termination. The majority of recorded units did not exhibit positive linear relationships with speed but instead exhibited nonlinear relationships that peaked at a range of locomotor speeds. Bulk calcium recordings of identified direct and indirect pathway neurons revealed similar speed tuning profiles, indicating that the heterogeneity in response profiles was not due to this genetic distinction. We conclude that continuous encoding of speed is a central component of movement encoding in the striatum.SIGNIFICANCE STATEMENT The striatum is a structure that is linked to volitional movements and is a primary site of pathology in movement disorders. It remains unclear how striatal neurons encode motor parameters and use them to facilitate movement. Here, we evaluated two models for this: a "discrete encoding model" in which striatal neurons facilitate movements with brief burst of activity near the start and end of movements, and a "continuous encoding model," in which striatal neurons encode the sensory or motor state of the animal with continuous changes in firing. We found evidence primarily in support of the continuous encoding model. This may have implications for understanding the striatal control of movement, as well as informing therapeutic approaches for treating movement disorders.

Influence of Pairing Startling Acoustic Stimuli with Postural Responses Induced by Light Touch Displacement

The first exposure to an unexpected, rapid displacement of a light touch reference induces a balance reaction in naïve participants, whereas an arm-tracking behaviour emerges with subsequent exposures. The sudden behaviour change suggests the first trial balance reaction arises from the startling nature of the unexpected stimulus. We investigated how touch-induced balance reactions interact with startling acoustic stimuli. Responses to light touch displacements were tested in 48 participants across six distinct combinations of touch displacement (DISPLACEMENT), acoustic startle (STARTLE), or combined (COMBINED) stimuli. The effect of COMBINED depended, in part, on the history of the preceding stimuli. Participants who received 10 DISPLACEMENT initially, produced facilitated arm-tracking responses with subsequent COMBINED. Participants who received 10 COMBINED initially, produced facilitated balance reactions, with arm-tracking failing to emerge until the acoustic stimuli were discontinued. Participants who received five DISPLACEMENT, after initially habituating to 10 STARTLE, demonstrated re-emergence of the balance reaction with the subsequent COMBINED. Responses evoked by light touch displacements are influenced by the startling nature of the stimulus, suggesting that the selection of a balance reaction to a threatening stimulus is labile and dependent, in part, on the context and sensory state at the time of the disturbance.

Effect of Broken Wall on the Quality of Ganoderma lucidum Spore Powder

Ganoderma lucidum spore powder was used as raw material,and it was broken by microwave and ultrasonic. The time,power and ratio of material to liquid of microwave and ultrasonic were taken as the factors. The content of polysaccharides and triterpenoids of Ganoderma lucidum spore powder were as indicators. The best process were solid-liquid ratio 20∶1 (mL/g),microwave power 280 W,microwave time 90 s.Ganoderma lucidum spore powder breaking by microwave was used as raw material to compare the particle size,looseness,specific surface area,color and angle of repose of the spore powder before and after breaking,and observed the morphology by electron microscope. The total fat,total polysaccharide,total triterpene,ash,protein,polyphenol and other nutrients of the spore powder were determined before and after the wall was broken,and the qualities of the spore powder before and after the wall was broken were compared.The results showed that the sensory state of Ganoderma lucidum spore powder after breakage was significantly different from that of unbroken spore powder:The specific surface area,looseness and chroma of spore powder increased significantly;particle size and fluidity decreased. The content of nutrients such as fat,polysaccharide,triterpenoids,ash and protein in the spore powder after breaking the wall increased to varying degrees. The experiment proves that the microwave breaking treatment is helpful for the dissolution of functional ingredients such as triterpenoids,crude polysaccharides and crude fat in the spore powder of Ganoderma lucidum,which greatly increases the dissolution of nutrients of Ganoderma lucidum spore powder.

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance.

This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporal-parietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity.