Mouse hippocampal CA1 VIP interneurons detect novelty in the environment and support recognition memory.

In the CA1 hippocampus, vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) play a prominent role in disinhibitory circuit motifs. However, the specific behavioral conditions that lead to circuit disinhibition remain uncertain. To investigate the behavioral relevance of VIP-IN activity, we employed wireless technologies allowing us to monitor and manipulate their function in freely behaving mice. Our findings reveal that, during spatial exploration in new environments, VIP-INs in the CA1 hippocampal region become highly active, facilitating the rapid encoding of novel spatial information. Remarkably, both VIP-INs and pyramidal neurons (PNs) exhibit increased activity when encountering novel changes in the environment, including context- and object-related alterations. Concurrently, somatostatin- and parvalbumin-expressing inhibitory populations show an inverse relationship with VIP-IN and PN activity, revealing circuit disinhibition that occurs on a timescale of seconds. Thus, VIP-IN-mediated disinhibition may constitute a crucial element in the rapid encoding of novelty and the acquisition of recognition memory.

Customizing spatial remapping of letters to aid reading in the presence of a simulated central field loss.

Reading is a primary concern of patients with central field loss (CFL) because it is typically performed with foveal vision. Spatial remapping offers one potential avenue to aid in reading; it entails shifting occluded letters to retinal areas where vision is functional. Here, we introduce a method of creating and testing different remapping strategies-ways to remap text-customized for CFL of different shapes. By simulating CFL in typically-sighted individuals, we tested the customization hypothesis-that the benefits of different remapping strategies will depend on the properties of the CFL. That is, remapping strategies will aid reading differentially in the presence of differently shaped CFL. In Experiment 1, letter recognition in the presence of differently shaped CFL was assessed in and around central vision. Using these letter recognition "maps" different spatial remappings were created and tested in Experiment 2 using a word recognition task. Results showed that the horizontal gap remapping, which did not remap any letters vertically, resulted in the best word recognition. Results were also consistent with the customization hypothesis; the benefits of different remappings on word recognition depended on the different CFL shapes. Although the horizontal gap remapping resulted in very good word recognition, tailoring remapping strategies to the shape of patients' CFL may aid reading with the wide range of sizes and shapes encountered by patients with CFL.

Beyond visual integration: sensitivity of the temporal-parietal junction for objects, places, and faces.

One important role of the TPJ is the contribution to perception of the global gist in hierarchically organized stimuli where individual elements create a global visual percept. However, the link between clinical findings in simultanagnosia and neuroimaging in healthy subjects is missing for real-world global stimuli, like visual scenes. It is well-known that hierarchical, global stimuli activate TPJ regions and that simultanagnosia patients show deficits during the recognition of hierarchical stimuli and real-world visual scenes. However, the role of the TPJ in real-world scene processing is entirely unexplored. In the present study, we first localized TPJ regions significantly responding to the global gist of hierarchical stimuli and then investigated the responses to visual scenes, as well as single objects and faces as control stimuli. All three stimulus classes evoked significantly positive univariate responses in the previously localized TPJ regions. In a multivariate analysis, we were able to demonstrate that voxel patterns of the TPJ were classified significantly above chance level for all three stimulus classes. These results demonstrate a significant involvement of the TPJ in processing of complex visual stimuli that is not restricted to visual scenes and that the TPJ is sensitive to different classes of visual stimuli with a specific signature of neuronal activations.

An analysis of information segregation in parallel streams of a multi-stream convolutional neural network.

Visual information is processed in hierarchically organized parallel streams in the primate brain. In the present study, information segregation in parallel streams was examined by constructing a convolutional neural network with parallel architecture in all of the convolutional layers. Although filter weights for convolution were initially set to random values, color information was segregated from shape information in most model instances after training. Deletion of the color-related stream decreased recognition accuracy of animate images, whereas deletion of the shape-related stream decreased recognition accuracy of both animate and inanimate images. The results suggest that properties of filters and functions of a stream are spontaneously segregated in parallel streams of neural networks.

Brain areas interconnected to ventral pathway circuits are independently able to induce enhancement in object recognition memory and cause reversal in object recognition memory deficit.

AIMS: Ventral pathway circuits are constituted by the interconnected brain areas that are distributed throughout the brain. These brain circuits are primarily involved in processing of object related information in brain. However, their role in object recognition memory (ORM) enhancement remains unknown. Here, we have studied on the implication of these circuits in ORM enhancement and in reversal of ORM deficit in aging. METHODS: The brain areas interconnected to ventral pathway circuits in rat brain were activated by an expression of a protein called regulator of G-protein signaling 14 of 414 amino acids (RGS14414). RGS14414 is an ORM enhancer and therefore used here as a gain-in-function tool. ORM test and immunohistochemistry, lesions, neuronal arborization, and knockdown studies were performed to uncover the novel function of ventral pathway circuits. RESULTS: An activation of each of the brain areas interconnected to ventral pathway circuits individually induced enhancement in ORM; however, same treatment in brain areas not interconnected to ventral pathway circuits produced no effect. Further study in perirhinal cortex (PRh), area V2 of visual cortex and frontal cortex (FrC), which are brain areas that have been shown to be involved in ORM and are interconnected to ventral pathway circuits, revealed that ORM enhancement seen after the activation of any one of the three brain areas was unaffected by the lesions in other two brain areas either individually in each area or even concurrently in both areas. This ORM enhancement in all three brain areas was associated to increase in structural plasticity of pyramidal neurons where more than 2-fold higher dendritic spines were observed. Additionally, we found that an activation of either PRh, area V2, or FrC not only was adequate but also was sufficient for the reversal of ORM deficit in aging rats, and the blockade of RGS14414 activity led to loss in increase in dendritic spine density and failure in reversal of ORM deficit. CONCLUSIONS: These results suggest that brain areas interconnected to ventral pathway circuits facilitate ORM enhancement by an increase in synaptic connectivity between the local brain area circuits and the passing by ventral pathway circuits and an upregulation in activity of ventral pathway circuits. In addition, the finding of the reversal of ORM deficit through activation of an interconnected brain area might serve as a platform for developing not only therapy against memory deficits but also strategies for other brain diseases in which neuronal circuits are compromised.

A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain-Computer Interfaces.

Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.

Linguistic based emotion analysis using softmax over time attention mechanism.

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions. Even a video-based emotion detection system can be fooled by false facial expressions for various emotions. To rectify this issue, we need to make a parallel model that will train on textual data and make predictions based on the words present in the text. The model will then classify the type of emotions using more comprehensive information, thus making it a more robust model. To address this issue, we have tested four text-based classification models to classify the emotions of a customer. We examined the text-based models and compared their results which showed that the modified Encoder decoder model with attention mechanism trained on textual data achieved an accuracy of 93.5%. This research highlights the pressing need for more robust emotion recognition systems and underscores the potential of transfer models with attention mechanisms to significantly improve feedback management processes and the medical applications.

Call it a conspiracy: How conspiracy belief predicts recognition of conspiracy theories.

While conspiracy theories are treated as irrational fringe beliefs in popular culture, conspiracy belief is quite common. Given the disconnect between stereotypes about conspiracy belief and its prevalence, I tested whether people have difficulty recognizing the conspiracy theories that they believe as conspiracy theories. Across two studies I demonstrate that people have considerable difficulty identifying conspiracy theories they believe as conspiracy theories, particularly when they do not take much time to consider whether their beliefs might be conspiracy theories. This is consistent with the notion that people experience "conspiracy blindness." People have trouble recognizing the conspiracy theories they believe as conspiracy theories because they do not take the time to consider whether their beliefs might be conspiracy theories. In Study 2, I demonstrate that people can overcome their conspiracy blindness and recognize the conspiracy theories they believe as conspiracy theories when they are given a definition for "conspiracy theory" and asked to consider their answer. This suggests that people are typically ignorant of their own conspiracy beliefs, but capable of recognizing them when given the tools and motivation to do so. However, recognizing their beliefs as conspiracy theories does not reduce their adherence to those beliefs.

Chronic phase advances reduces recognition memory and increases vascular cognitive dementia-like impairments in aged mice.

Disrupted or atypical light-dark cycles disrupts synchronization of endogenous circadian clocks to the external environment; extensive circadian rhythm desynchrony promotes adverse health outcomes. Previous studies suggest that disrupted circadian rhythms promote neuroinflammation and neuronal damage post-ischemia in otherwise healthy mice, however, few studies to date have evaluated these health risks with aging. Because most strokes occur in aged individuals, we sought to identify whether, in addition to being a risk factor for poor ischemic outcome, circadian rhythm disruption can increase risk for vascular cognitive impairment and dementia (VCID). We hypothesized that repeated 6 h phase advances (chronic jet lag; CJL) for 8 weeks alters cerebrovascular architecture leading to increased cognitive impairments in aged mice. Female CJL mice displayed impaired spatial processing during a spontaneous alternation task and reduced acquisition during auditory-cued associative learning. Male CJL mice displayed impaired retention of the auditory-cued associative learning task 24 h following acquisition. CJL increased vascular tortuosity in the isocortex, associated with increased risk for vascular disease. These results demonstrate that CJL increased sex-specific cognitive impairments coinciding with structural changes to vasculature in the brain. We highlight that CJL may accelerate aged-related functional decline and could be a crucial target against disease progression.

English vowel recognition in multi-talker babbles mixed with different numbers of talkersa).

The present study examined English vowel recognition in multi-talker babbles (MTBs) in 20 normal-hearing, native-English-speaking adult listeners. Twelve vowels, embedded in the h-V-d structure, were presented in MTBs consisting of 1, 2, 4, 6, 8, 10, and 12 talkers (numbers of talkers [N]) and a speech-shaped noise at signal-to-noise ratios of -12, -6, and 0 dB. Results showed that vowel recognition performance was a non-monotonic function of N when signal-to-noise ratios were less favorable. The masking effects of MTBs on vowel recognition were most similar to consonant recognition but less so to word and sentence recognition reported in previous studies.

Increases in pre-stimulus theta and alpha oscillations precede successful encoding of crossmodal associations.

A central aspect of episodic memory is the formation of associations between stimuli from different modalities. Current theoretical approaches assume a functional role of ongoing oscillatory power and phase in the theta band (3-7 Hz) for the encoding of crossmodal associations. Furthermore, ongoing activity in the theta range as well as alpha (8-12 Hz) and low beta activity (13-20 Hz) before the presentation of a stimulus is thought to modulate subsequent cognitive processing, including processes that are related to memory. In this study, we tested the hypothesis that pre-stimulus characteristics of low frequency activity are relevant for the successful formation of crossmodal memory. The experimental design that was used specifically allowed for the investigation of associative memory independent from individual item memory. Participants (n = 51) were required to memorize associations between audiovisual stimulus pairs and distinguish them from newly arranged ones consisting of the same single stimuli in the subsequent recognition task. Our results show significant differences in the state of pre-stimulus theta and alpha power between remembered and not remembered crossmodal associations, clearly relating increased power to successful recognition. These differences were positively correlated with memory performance, suggesting functional relevance for behavioral measures of associative memory. Further analysis revealed similar effects in the low beta frequency ranges, indicating the involvement of different pre-stimulus-related cognitive processes. Phase-based connectivity measures in the theta band did not differ between remembered and not remembered stimulus pairs. The findings support the assumed functional relevance of theta band oscillations for the formation of associative memory and demonstrate that an increase of theta as well as alpha band oscillations in the pre-stimulus period is beneficial for the establishment of crossmodal memory.

A primary sensory cortical interareal feedforward inhibitory circuit for tacto-visual integration.

Tactile sensation and vision are often both utilized for the exploration of objects that are within reach though it is not known whether or how these two distinct sensory systems combine such information. Here in mice, we used a combination of stereo photogrammetry for 3D reconstruction of the whisker array, brain-wide anatomical tracing and functional connectivity analysis to explore the possibility of tacto-visual convergence in sensory space and within the circuitry of the primary visual cortex (VISp). Strikingly, we find that stimulation of the contralateral whisker array suppresses visually evoked activity in a tacto-visual sub-region of VISp whose visual space representation closely overlaps with the whisker search space. This suppression is mediated by local fast-spiking interneurons that receive a direct cortico-cortical input predominantly from layer 6 neurons located in the posterior primary somatosensory barrel cortex (SSp-bfd). These data demonstrate functional convergence within and between two primary sensory cortical areas for multisensory object detection and recognition.

Plant disease recognition using residual convolutional enlightened Swin transformer networks.

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer's life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

The Role of Attention in Category Representation.

Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants' memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion.

Randomness impacts the building of specific priors, visual exploration, and perception in object recognition.

Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack a full understanding of the role visual information sampling plays in this process, and its relation to the individual's priors. To bridge this gap, the eye-movements of 18 adult participants were recorded during a free-viewing object-recognition task using Dots stimuli1. Participants viewed the stimuli in one of three orders: from most visible to least (Descending), least visible to most (Ascending), or in a randomised order (Random). This dictated the strength of their priors along the experiment. Visibility order influenced the participants' recognition performance and visual exploration. In addition, we found that while orders allowing for stronger priors generally led participants to visually sample more informative locations, this was not the case of Random participants. Indeed, they appeared to behave naïvely, and their use of specific object-related priors was fully impaired, while they maintained the ability to use general, task-related priors to guide their exploration. These findings have important implications for our understanding of perception, which appears to be influenced by complex cognitive processes, even at the basic level of visual sampling during object recognition.

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare.

Pupil diameter as an indicator of sound pair familiarity after statistically structured auditory sequence.

Inspired by recent findings in the visual domain, we investigated whether the stimulus-evoked pupil dilation reflects temporal statistical regularities in sequences of auditory stimuli. We conducted two preregistered pupillometry experiments (experiment 1, n = 30, 21 females; experiment 2, n = 31, 22 females). In both experiments, human participants listened to sequences of spoken vowels in two conditions. In the first condition, the stimuli were presented in a random order and, in the second condition, the same stimuli were presented in a sequence structured in pairs. The second experiment replicated the first experiment with a modified timing and number of stimuli presented and without participants being informed about any sequence structure. The sound-evoked pupil dilation during a subsequent familiarity task indicated that participants learned the auditory vowel pairs of the structured condition. However, pupil diameter during the structured sequence did not differ according to the statistical regularity of the pair structure. This contrasts with similar visual studies, emphasizing the susceptibility of pupil effects during statistically structured sequences to experimental design settings in the auditory domain. In sum, our findings suggest that pupil diameter may serve as an indicator of sound pair familiarity but does not invariably respond to task-irrelevant transition probabilities of auditory sequences.

A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames.

Traditional optical flame detectors (OFDs) in flame detection are susceptible to environmental interference, which will inevitably cause detection errors and miscalculations when confronted with a complex environment. The conventional deep learning-based models can mitigate the interference of complex environments by flame image feature extraction, which significantly improves the precision of flame recognition. However, these models focus on identifying the general profile of the static flame, but neglect to effectively locate the source of the dynamic flame. Therefore, this paper proposes a novel dynamic flame detection method named Dynamic Deformable Adaptive Framework (DDAF) for locating the flame root region dynamically. Specifically, to address limitations in flame feature extraction of existing detection models, the Deformable Convolution Network v2 (DCNv2) is introduced for more flexible adaptation to the deformations and scale variations of target objects. The Context Augmentation Module (CAM) is used to convey flame features into Dynamic Head (DH) to feature extraction from different aspects. Subsequently, the Layer-Adaptive Magnitude-based Pruning (LAMP) where the connection with the smallest LAMP score is pruned sequentially is employed to further enhance the speed of model detection. More importantly, both the coarse- and fine-grained location techniques are designed in the Inductive Modeling (IM) to accurately delineate the flame root region for effective fire control. Additionally, the Temporal Consistency-based Detection (TCD) contributes to improving the robustness of model detection by leveraging the temporal information presented in consecutive frames of a video sequence. Compared with the classical deep learning method, the experimental results on the custom flame dataset demonstrate that the AP0.5 value is improved by 4.4%, while parameters and FLOPs are reduced by 25.3% and 25.9%, respectively. The framework of this research extends applicability to a variety of flame detection scenarios, including industrial safety and combustion process control.

Does culture moderate the encoding and recognition of negative cues? Evidence from an eye-tracking study.

Cross-cultural research has elucidated many important differences between people from Western European and East Asian cultural backgrounds regarding how each group encodes and consolidates the contents of complex visual stimuli. While Western European groups typically demonstrate a perceptual bias towards centralised information, East Asian groups favour a perceptual bias towards background information. However, this research has largely focused on the perception of neutral cues and thus questions remain regarding cultural group differences in both the perception and recognition of negative, emotionally significant cues. The present study therefore compared Western European (n = 42) and East Asian (n = 40) participants on a free-viewing task and a subsequent memory task utilising negative and neutral social cues. Attentional deployment to the centralised versus background components of negative and neutral social cues was indexed via eye-tracking, and memory was assessed with a cued-recognition task two days later. While both groups demonstrated an attentional bias towards the centralised components of the neutral cues, only the Western European group demonstrated this bias in the case of the negative cues. There were no significant differences observed between Western European and East Asian groups in terms of memory accuracy, although the Western European group was unexpectedly less sensitive to the centralised components of the negative cues. These findings suggest that culture modulates low-level attentional deployment to negative information, however not higher-level recognition after a temporal interval. This paper is, to our knowledge, the first to concurrently consider the effect of culture on both attentional outcomes and memory for both negative and neutral cues.

On investigating drivers' attention allocation during partially-automated driving.

The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this pattern is a manifestation of inattentional blindness or, more dangerously, it is also accompanied by a greater attentional processing of the driving scene. Participants drove a simulated vehicle in manual or partially-automated mode. Fixations were recorded by means of a head-mounted eye-tracker. A surprise two-alternative forced-choice recognition task was administered at the end of the data collection whereby participants were quizzed on the presence of roadside billboards that they encountered during the two drives. Data showed that participants were more likely to fixate and recognize billboards when the automated system was operational. Furthermore, whereas fixations toward billboards decreased toward the end of the automated drive, the performance in the recognition task did not suffer. Based on these findings, we hypothesize that the use of the partially-automated driving system may result in an increase in attention allocation toward peripheral objects in the road scene which is detrimental to the drivers' ability to supervise the automated system and resume manual control of the vehicle.