Encoding Of Information Research Articles

Efficient recognition of composite vortex beams with multi-mode in atmospheric turbulence based on convolutional neural network

In recent years, vortex beams have been widely used in optical communication because of their characteristics. However, it is noted that the information encoding of a single-mode vortex beam is limited by the size of physical device. To resolve the information limitation problem associated with single-mode vortex beams, an idea of multi-beam switching superposition can be applied to composite vortex beam with multi-mode. Here, a convolutional neural network is combined to simulate and recognize the specific modes of composite vortex beams. Moreover, taking into account the atmospheric turbulence environment, the recognition effect is numerically simulated under three different levels of turbulence intensity. Finally, a compensation scheme is proposed for strong turbulence, whose recognition rate is upto 97.82% in harsh environments. This finding attests to the efficiency of our proposed technique. These results are expected to generate novel concepts for enhancing optical communication capacity and quality in the future.

Fixational eye movements as active sensation for high visual acuity

Perception and action are inherently entangled: our world view is shaped by how we explore our environment through complex and variable self-motion. Even when fixating stable stimuli, our eyes undergo small, involuntary movements. Fixational eye movements (FEM) render a stable world jittery on our retinae, which can be expected to harm neural coding. Yet, empirical evidence suggests that FEM help rather than harm human perception of fine detail. Here, we elucidate this paradox by uncovering under which conditions FEM improve or impair retinal coding and human acuity. We combine theory and experiment: model accuracy is directly compared to that of healthy human subjects in a visual acuity task. Acuity is modeled by applying an ideal Bayesian classifier to simulations of retinal spiking activity in the presence of FEM. In addition, empirical FEM are monitored using high-resolution eye-tracking by an adaptive optics scanning laser ophthalmoscope. FEM introduce variability in retinal ganglion cell activity, but they also effectively preprocess inputs to facilitate retinal information encoding. Based on an interplay of these mechanisms, our model predicts a relation between visual acuity, FEM amplitude, and single-trial stimulus size that quantitatively accounts for experimental observations and captures the beneficial effect of FEM. Moreover, we observe that while human subjects' FEM statistics vary with stimulus size, our model suggests that subjects' FEM amplitude remains within a near-optimal range, where acuity is enhanced compared to much larger or smaller amplitudes. Overall, our findings indicate that perception benefits from action even at the fine spatiotemporal scale of FEM.

Degeneracy-breaking and long-lived multimode microwave electromechanical systems enabled by cubic silicon-carbide membrane crystals

Cubic silicon-carbide crystals (3C-SiC), known for their high thermal conductivity and in-plane stress, hold significant promise for the development of high-quality (Q) mechanical oscillators. We reveal degeneracy-breaking phenomena in 3C-phase crystalline silicon-carbide membrane and present high-Q mechanical modes in pairs or clusters. The 3C-SiC material demonstrates excellent microwave compatibility with superconducting circuits. Thus, we can establish a coherent electromechanical interface, enabling precise control over 21 high-Q mechanical modes from a single 3C-SiC square membrane. Benefiting from extremely high mechanical frequency stability, this interface enables tunable light slowing with group delays extending up to an impressive duration of an hour. Coherent energy transfer between distinct mechanical modes are also presented. In this work, the studied 3C-SiC membrane crystal with their significant properties of multiple acoustic modes and high-quality factors, provide unique opportunities for the encoding, storage, and transmission of quantum information via bosonic phonon channels.

End-to-end hybrid infrared imaging system design with thermal analysis

The hybrid refractive-diffractive optical system exhibits strong capabilities in achromatic and athermal imaging, as well as in information encoding. This paper presents a novel end-to-end design framework for refractive-diffractive hybrid optical imaging systems. Utilizing a differential hybrid ray-tracing model, the framework simultaneously optimizes optical and neural network parameters. It allows for the design of diffractive optical elements (DOE) on aspheric substrates, enhancing flexibility and enabling applications in infrared optics. The integrated thermal analysis facilitates the development of athermal hybrid optical systems by combining them with an advanced restoration network. When applied to a single-lens short-wave infrared (SWIR) system, this approach outperforms traditional discrete design methods in both simulations and experiments, demonstrating its significant potential for future optical applications.

Cholinergic dynamics in the septo-hippocampal system provide phasic multiplexed signals for spatial novelty and correlate with behavioral states.

In the hippocampal formation, cholinergic modulation from the medial septum/diagonal band of Broca (MSDB) is known to correlate with the speed of an animal's movements at sub-second timescales and also supports spatial memory formation. Yet, the extent to which sub-second cholinergic dynamics, if at all, align with transient behavioral and cognitive states supporting the encoding of novel spatial information remains unknown. In this study, we used fiber photometry to record the temporal dynamics in the population activity of septo-hippocampal cholinergic neurons at sub-second resolution during a hippocampus-dependent object location memory task using ChAT-Cre mice. Using a general linear model, we quantified the extent to which cholinergic dynamics were explained by changes in movement speed, behavioral states such as locomotion, grooming, and rearing, and hippocampus-dependent cognitive states such as recognizing a novel location of a familiar object. The data show that cholinergic dynamics contain a multiplexed code of fast and slow signals i) coding for the logarithm of movement speed at sub-second timescales, ii) providing a phasic spatial novelty signal during the brief periods of exploring a novel object location, and iii) coding for environmental novelty at a seconds-long timescale. Furthermore, behavioral event-related phasic cholinergic activity around the onset and offset of the behavior demonstrates that fast cholinergic transients help facilitate a switch in cognitive and behavioral state before and during the onset of behavior. These findings enhance understanding of the mechanisms by which cholinergic modulation contributes to the coding of movement speed and encoding of novel spatial information.

Wearable Pressure Sensor Based on Triboelectric Nanogenerator for Information Encoding, Gesture Recognition, and Wireless Real‐Time Robot Control

AbstractWearable sensor has attracted a broad interesting in application prospect of human‐machine interaction (HMI). However, most of sensors are assembled in the shape of gloves to accurately capture complex hand motion information, thereby seriously blocking the hand to complete complex tasks. Herein, a wearable pressure sensor based on drum‐structured triboelectric nanogenerator (DS‐TENG) is developed to capture subtle pressure signals for physiological signal detection, information encoding, gesture recognition, and wireless real‐time robot control. The DS‐TENG enables a limit of detection down to 3.9 Pa pressure, which can sensitively capture human micromotion signals of pulse, throat sounds, and wrist muscles contraction. Especially, combined with a microprocessor and Morse code, the DS‐TENG worn on wrist can detect single‐finger motion signals to translate into regular voltage signals, which is employed to encode information of 26 letters and subsequently decode into the corresponding letters. Furthermore, with an aid of machine learning, the DS‐TENG array (2 × 2) can successfully achieve gesture recognition with high accuracy of 92% to wirelessly perform real‐time robot control. Consequently, the wearable pressure sensor based on DS‐TENG can capture subtle pressure signals for information encoding and wireless real‐time robot control, which demonstrates extreme potential in the field of HMI and artificial intelligence.

Cortical coding of gustatory and thermal signals in active licking mice.

Eating behaviours are influenced by the integration of gustatory, olfactory and somatosensory signals, which all contribute to the perception of flavour. Although extensive research has explored the neural correlates of taste in the gustatory cortex (GC), less is known about its role in encoding thermal information. This study investigates the encoding of oral thermal and chemosensory signals by GC neurons compared to the oral somatosensory cortex. In this study we recorded the spiking activity of more than 900 GC neurons and 500 neurons from the oral somatosensory cortex in mice allowed to freely lick small drops of gustatory stimuli or deionized water at varying non-nociceptive temperatures. We then developed and used a Bayesian-based analysis technique to assess neural classification scores based on spike rate and phase timing within the lick cycle. Our results indicate that GC neurons rely predominantly on rate information, although phase information is needed to achieve maximum accuracy, to effectively encode both chemosensory and thermosensory signals. GC neurons can effectively differentiate between thermal stimuli, excelling in distinguishing both large contrasts (14 vs. 36°C) and, although less effectively, more subtle temperature differences. Finally a direct comparison of the decoding accuracy of thermosensory signals between the two cortices reveals that whereas the somatosensory cortex exhibited higher overall accuracy, the GC still encodes significant thermosensory information. These findings highlight the GC's dual role in processing taste and temperature, emphasizing the importance of considering temperature in future studies of taste processing. KEY POINTS: Flavour perception relies on gustatory, olfactory and somatosensory integration, with the gustatory cortex (GC) central to taste processing. GC neurons also respond to temperature, but the specifics of how the GC processes taste and oral thermal stimuli remain unclear. The focus of this study is on the role of GC neurons in the encoding of oral thermal information, particularly compared to the coding functions of the oral somatosensory cortex. We found that whereas the somatosensory cortex shows a higher classification accuracy for distinguishing water temperature, the GC still encodes a substantial amount of thermosensory information. These results emphasize the importance of including temperature as a key factor in future studies of cortical taste coding.

Dissipation Alters Modes of Information Encoding in Small Quantum Reservoirs near Criticality.

Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet, identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. Here, we investigate a minimal model of a driven-dissipative quantum reservoir described by two coupled Kerr-nonlinear oscillators, an experimentally realizable platform that features controllable coupling, intrinsic nonlinearity, and tunable photon loss. Using Partial Information Decomposition (PID), we examine how different dynamical regimes encode input drive signals in terms of redundancy (information shared by each oscillator) and synergy (information accessible only through their joint observation). Our key results show that, near a critical point marking a dynamical bifurcation, the system transitions from predominantly redundant to synergistic encoding. We further demonstrate that synergy amplifies short-term responsiveness, thereby enhancing immediate memory retention, whereas strong dissipation leads to more redundant encoding that supports long-term memory retention. These findings elucidate how the interplay of instability and dissipation shapes information processing in small quantum systems, providing a fine-grained, information-theoretic perspective for analyzing and designing QRC platforms.

A human single-neuron dataset for object recognition

Object recognition is fundamental to how we interact with and interpret the world around us. The human amygdala and hippocampus play a key role in object recognition, contributing to both the encoding and retrieval of visual information. Here, we recorded single-neuron activity from the human amygdala and hippocampus when neurosurgical epilepsy patients performed a one-back task using naturalistic object stimuli. We employed two sets of naturalistic object images from leading datasets extensively used in primate neural recordings and computer vision models: we recorded 1204 neurons using the ImageNet stimuli, which included broader object categories (10 different images per category for 50 categories), and we recorded 512 neurons using the Microsoft COCO stimuli, which featured a higher number of images per category (50 different images per category for 10 categories). Together, our extensive dataset, offering the highest spatial and temporal resolution currently available in humans, will not only facilitate a comprehensive analysis of the neural correlates of object recognition but also provide valuable opportunities for training and validating computational models.

Goals bias face perception.

Faces-the most common and complex stimuli in our daily lives-contain multidimensional information used to infer social attributes that guide consequential behaviors, such as deciding who to trust. Decades of research illustrates that perceptual information from faces is processed holistically. An open question, however, is whether goals might impact this perceptual process, influencing the encoding and representation of the complex social information embedded in faces. If an individual were able to factorize information so that each dimension is separately represented, it might enable flexibility. Having a goal, for example, might mean that only goal-relevant dimensions are leveraged to inform behavior. Whether people are able to build such factorized representations remains unknown, largely due to natural correlations between social attributes. We overcome these confounds using a new statistical face model that orthogonalizes perceived facial attractiveness and trustworthiness. Across three experiments (N = 249), we observe that only in some contexts can humans successfully factorize multidimensional social information. When there is a clear goal of assessing another's trustworthiness, people successfully decompose these social attributes. The more an individual factorizes, the more they entrust money to others in a subsequent trust game. However, when the goal is to assess attractiveness, irrelevant information about trustworthiness is so potent that it biases how attractive someone is perceived-a trustworthiness "halo effect." In contrast, in goal-agnostic environments, we do not find any evidence of factorization; instead, people encode multidimensional social information in an entwined and holistic fashion that distorts their perceptions of social attributes. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Reactivation and consolidation of memory traces during post-encoding rest across the adult lifespan.

Episodic memory is a critical cognitive function that enables the encoding, storage, and retrieval of new information. Memory consolidation, a key stage of episodic memory, stabilizes this newly encoded information into long-lasting brain "storage." Studies using fMRI to investigate post-encoding awake rest holds promise to shed light on early, immediate consolidation mechanisms. Here, we review fMRI studies during episodic memory to document common methods to investigate post-encoding consolidation, such as multivoxel pattern analysis (MVPA) and functional connectivity, and the current state of the science in both healthy younger and older adults. In young adults, post-encoding reactivation of stimuli-specific neural patterns in the hippocampus and its connectivity with cortical and subcortical areas (e.g., visual-temporal cortex, medial prefrontal, and medial parietal cortex) correlate with subsequent memory performance. Conversely, studies in older adults highlight the importance of large-scale brain networks during post-encoding rest, particularly the default mode network (DMN). Alterations in connectivity between the DMN and task-positive networks may help older adults maintain episodic memory. Furthermore, non-invasive brain stimulation techniques can enhance these post-encoding consolidation processes and improve memory performance in both younger and older adults. Notably, a lack of studies has investigated post-encoding memory consolidation in neurodegenerative disorders. This review underscores the importance of understanding how post-encoding neural reactivation and connectivity evolve with age to partially explain age-related declines in episodic memory performance and how such declines can be restored.

A kaleidoscopic hydrogel canvas for information encoding, encryption, and decryption via chemical-induced phase separation

A kaleidoscopic hydrogel canvas for information encoding, encryption, and decryption via chemical-induced phase separation

Tensor Coupled Learning of Incomplete Longitudinal Features and Labels for Clinical Score Regression.

Longitudinal data with incomplete entries pose a significant challenge for clinical score regression over multiple time points. Although many methods primarily estimate longitudinal scores with complete baseline features (i.e., features collected at the initial time point), such snapshot features may overlook beneficial latent longitudinal traits for generalization. Alternatively, certain completion approaches (e.g., tensor decomposition technology) have been proposed to impute incomplete longitudinal data before score estimation, most of which, however, are transductive and cannot utilize label semantics. This work presents a tensor coupled learning (TCL) paradigm of incomplete longitudinal features and labels for clinical score regression. The TCL enjoys three advantages: 1) It drives semantic-aware factor matrices and collaboratively deals with incomplete longitudinal entries (of features and labels), during which a dynamic regularizer is designed for adaptive attribute selection. 2) It establishes a closed loop connecting baseline features and the coupled factor matrices, which enables inductive inference of longitudinal scores relying on only baseline features. 3) It reinforces the information encoding of baseline data by preserving the local manifold of longitudinal feature space and detecting the temporal alteration across multiple time points. Extensive experiments demonstrate the remarkable performance improvement of our method on clinical score regression with incomplete longitudinal data.

High Molecular Weight Uniform Polymers Encoding Octal Sequences by Passerini Iterative Exponential Growth.

Sequence-defined polymers composed of a large pool of chemically distinct monomers (SDPs) have been pursued to achieve the structural and functional precisions exhibited by biopolymers in nonbiological environments. In contrast to the incremental growth of SDPs by sequential addition of individual monomers, the iterative exponential growth (IEG) method allows the synthesis of high molecular-weight SDPs, but their sequences have been composed mostly of binary monomers. Consequently, achieving high molecular-weight SDPs built with a large pool of monomers remains a challenge. Here we report the Passerini iterative exponential growth (P-IEG) approach that enables efficient synthesis of 128-mer uniform poly(hydroxybutyrate) (PHB), possessing 127 γ-acylamino cyclohexyl side groups (27 kDa, Đ = 1) and a 31-mer SDP, encoding an octal sequence composed of eight chemically distinct repeating units. Taking advantage of the combinatorial character of the Passerini three-component reaction involving an aldehyde, a carboxylic acid, and an isocyanide to form an acyloxy amide linkage, we simultaneously achieved the exponential chain growth through the convergence of bifunctional building blocks and side-chain implementation by selecting appropriate isocyanides as a third component. The P-IEG approach enabled the synthetic encoding of complex information, an octal sequence equivalent to a 93-bit binary code, into a 31-mer SDP. Our proposed P-IEG method could contribute to the synthesis of synthetic macromolecules with absolutely defined sequences of functionalities. These polymers could be used for the development of functional materials with properties not achievable by conventional polymers, including polymers storing digital information at higher density.

Neural sensitivity to frequency changes in song structure in a high-order auditory area reflects tutor song memory in adult songbirds.

Vocal learners, including humans and songbirds, acquire their complex vocalizations by accurately memorizing and imitating the vocal patterns of other individuals. In songbirds, the caudomedial nidopallium (NCM), considered the secondary auditory region, has been suggested to play a critical role in memorizing and recognizing the songs of tutors. However, the mechanisms by which NCM neurons encode the acoustic information of tutor song are not yet fully understood. Here, we investigate the neural representation of tutor song information in NCM neurons by examining their sensitivity to spectral changes in song structure, using electrophysiological recordings in anesthetized male zebra finches. We manipulated the acoustic structures of both tutor songs and unfamiliar conspecific songs by shifting the fundamental frequency (FF) of harmonic syllables by various frequency steps and recorded neural responses to those FF-shifted and original songs. Our results demonstrate that NCM neurons are highly sensitive to FF shifts in tutor song but much less in unfamiliar conspecific song, providing novel evidence for neural encoding of tutor song information in NCM neurons. Moreover, we find that the effects of FF shifts on neural responses depend on the direction of FF shifts. These findings suggest that NCM neurons encode detailed information of tutor song, which can serve as a tutor song template required for song learning.

Working memory updating in the macaque lateral prefrontal cortex.

Working memory updating is an important executive process. Here, we study the single-neuron mechanisms involved in updating versus protecting memory from distractors in the macaque prefrontal cortex. We recorded single-neuron activity from the lateral prefrontal cortex (LPFC) and prearcuate cortex (PAC) while male monkeys performed a task that required them to update their memory of target locations while ignoring distractors. Our findings revealed that neurons in the PAC signaled updated memory locations approximately ∼100 ms after stimulus onset, significantly faster than the ∼400 ms observed in the LPFC. Additionally, PAC neurons exhibited longer encoding of distractor information. Population decoding analyses further indicated that distractor information was maintained in orthogonal subspaces from target information in both regions, minimizing interference. These results demonstrate the distinct temporal dynamics in memory updating processes between the PAC and LPFC and highlight the interplay between robust memory maintenance and updating, suggesting that local neural mechanisms may contribute to these processes.Significance Statement Working memory is a fundamental cognitive function. It stored information in the short term, and this information can be manipulated to allow intelligent behaviors. The lateral prefrontal cortex is involved in this process, but the mechanisms of working memory manipulation remain unclear. Here, we studied one type of manipulation; working memory updating, which refers to the exchange of one memory for another. We found that two adjacent regions in the lateral prefrontal cortex show different updating times: while a posterior region updates memory content very fast, the more anterior region takes significantly longer. These results show that working memory updating may involve multiple operations, such as updating of memory or attention versus updating of motor plans.

A perceptual cue-based mechanism for automatic assignment of thematic agent and patient roles.

Understanding social events requires assigning the participating entities to roles such as agent and patient, a mental operation that is reportedly effortless. We investigated whether, in processing visual scenes, role assignment is accomplished automatically (i.e., when the task does not require it), based on visuospatial information, without requiring semantic or linguistic encoding of the stimuli. Human adults saw a series of images featuring the same male and female actors next to each other, one in an agentlike (more dynamic/leaning forward) and the other in a patientlike (static/less dynamic) posture. Participants indicated the side (left/right) of a target actor (i.e., the woman). From trial to trial, body postures changed, but the roles, defined by the type of posture, sometimes changed, sometimes not. We predicted that if participants spontaneously saw the actors as agent and patient, they should be slower to respond when roles switched from trial n-1 to trial n, than when they stayed the same (role switch cost). Results confirmed this hypothesis (Experiments 1-3). A role switch cost was also found when roles were defined by another visual relational cue, the relative positioning (where one actor stands relative to another), but not when actors were presented in isolation (Experiments 4-6). These findings reveal a mechanism for automatic role assignment based on encoding of visual relational information in social (multiple-person) scenes. Since we found that roles in one trial affected the processing of the subsequent trial despite variations in postures and spatial relations, this mechanism must be one that assigns entities in a scene, to the abstract categories of agent and patient. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Amplifying and Reversing the Chiral Bias in Asymmetric Photo-Polymerization Reaction.

Circularly polarized light (CPL) is inherently chiral and is regarded as one possible source for the origin of homochirality. Coincidentally, chiral metal nanoparticles have great prospects in asymmetric photochemical reactions since they can enhance the chiral light-matter interactions. Nonetheless, little is known about how the spin angular momentum of light competes with the chiral electromagnetic field in the vicinity of a chiral nanoparticle during the chiral induction and amplification process. Here, an asymmetric photo-polymerization system is presented where the chiral bias can be selectively regulated by the combination of chiral fields CPL and Ag nanoparticles, either constructively or destructively. Synergistic chiral amplification effect is observed when the handedness of both are matched, while the opposite-handed CPL can overrule the polymer helicity during the chain propagation process when the chiral near-field is weak. There is a bifurcation point in the chiral bias for the orthogonal control of the polymer helicity during the asymmetric photo-polymerization process, favoring for programmable chiroptical micropatterns and multi-channel independent information encoding. This work not only highlights opportunities for selectively regulating the asymmetric photopolymerization but also is highly valuable for fundamental understanding of the symmetry breaking in photochemical reactions.

Plasmon-Enhanced Optoelectronic Graded Neurons for Dual-Waveband Image Fusion and Motion Perception.

Motion recognition based on vision detectors requires the synchronous encoding and processing of temporal and spatial information in wide wavebands. Here, the dual-waveband sensitive optoelectronic synapses performing as graded neurons are reported for high-accuracy motion recognition and perception. Wedge-shaped nanostructures are designed and fabricated on molybdenum disulfide (MoS2) monolayers, leading to plasmon-enhanced wideband absorption across the visible to near-infrared spectral range. Due to the charge trapping and release at shallow trapping centers within the device channel, the optoelectronic graded neurons demonstrate remarkable photo-induced conductance plasticity at both 633 and 980nm wavelengths. A dynamic vision system consisting of 20 × 20 optoelectronic neurons demonstrates remarkable capabilities in the precise detection and perception of various motions. Moreover, neural network computing systems have been built as visual motion perceptron to identify target object movement. The recognition accuracy of dual-wavelength fused images for various motion trajectories has experienced a remarkable enhancement, transcending the previous level of less than 80% to impressive values exceeding 99%.

In Situ Generation of Microwire Heterojunctions with Flexible Optical Waveguide and Hydration‐Mediated Photochromism

AbstractFlexible heterojunctions based on molecular systems are in high demand for applications in photonics, electronics, and smart materials, but fabrication challenges have hindered progress. Herein, we present an in situ approach to creating optical heterojunctions using hydration‐mediated flexible molecular crystals. These hydrated multi‐component molecular solids display strong blue emitting optical waveguides with minimal optical loss (0.005 dB/μm) and excellent flexibility (elastic modulus: 3.87 GPa). The water‐mediated process enables the molecular microwires with tunable elastic and plastic deformation, as well as reversible uptake and release of lattice water, facilitating the formation of flexible heterojunctions. Spectral analysis and theoretical modeling reveal that these microwires exhibit both photochromism and color‐tunable dual emission (fluorescence and phosphorescence), expanding their utility in photonic information encoding. Therefore, this work introduces a hydration‐mediated molecular engineering strategy for fabricating crystalline heterojunctions with on‐demand processability and controllable emission sequences, enabling optical signal manipulation at the micro/nanoscale.