A neural data structure for novelty detection.
Ontology highlight
ABSTRACT:
SUBMITTER: Dasgupta S
PROVIDER: S-EPMC6304992 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Attention impairments are common symptoms of posttraumatic stress disorder (PTSD); however, the nature of these impairments remains elusive. Attention impairment may arise as the result of either excessive response to task-irrelevant stimuli or reduced response to task-relevant information. To test the association between PTSD and response to task-relevant and task-irrelevant stimuli, we used a 3-tone novelty auditory oddball task (AOD). We hypothesized that participants with PTSD relative to trauma controls would have less response during novelty processing in the dorsolateral prefrontal cortex (dlPFC) and the anterior cingulate cortex, as well as less response in the dlPFC and the orbitofrontal cortex during target detection. Thirty-one male veterans completed a 3-tone novelty AOD task during functional magnetic resonance imaging. Compared to trauma controls, the PTSD group had reduced response during novelty processing in ventromedial prefrontal cortex, superior/middle frontal gyrus (dlPFC), supplementary motor area/caudate, and in posterior regions including bilateral posterior cingulate cortex. The current results suggest PTSD is associated with a pattern of reduced response to novel stimuli. A disturbed orienting response in these brain regions could theoretically underlie PTSD attention-related symptoms.
| S-EPMC5962434 | biostudies-other
Project description:Huber and O'Reilly (2003) proposed that neural habituation aids perceptual processing, separating neural responses to currently viewed objects from recently viewed objects. However, synaptic depression has costs, producing repetition deficits. Prior work confirmed the transition from repetition benefits to deficits with increasing duration of a prime object, but the prediction of enhanced novelty detection was not tested. The current study examined this prediction with a same/different word priming task, using support vector machine (SVM) classification of EEG data, ERP analyses focused on the N400, and dynamic neural network simulations fit to behavioral data to provide a priori predictions of the ERP effects. Subjects made same/different judgements to a response word in relation to an immediately preceding brief target word; prime durations were short (50ms) or long (400ms), and long durations decreased P100/N170 responses to the target word, suggesting that this manipulation increased habituation. Following long duration primes, correct "different" judgments of primed response words increased, evidencing enhanced novelty detection. An SVM classifier predicted trial-by-trial behavior with 66.34% accuracy on held-out data, with greatest predictive power at a time pattern consistent with the N400. The habituation model was augmented with a maintained semantics layer (i.e., working memory) to generate behavior and N400 predictions. A second experiment used response-locked ERPs, confirming the model's assumption that residual activation in working memory is the basis of novelty decisions. These results support the theory that neural habituation enhances novelty detection, and the model assumption that the N400 reflects updating of semantic information in working memory.
| S-EPMC7447193 | biostudies-literature
Project description:ProTarget is a Web-based tool for the automatic prediction of fold novelty. It offers the structural genomics community a method for target selection by providing an online analysis of any new or pre-existing sequence for its relationship to any previously solved three-dimensional structure. ProTarget takes as input an amino acid sequence. Regions of this sequence that exhibit high similarity to an existing PDB (Protein Data Bank) sequence are removed, leaving one or more subsequences. Each of these subsequences is then analyzed against a clustering of the protein space to determine the likelihood of its representing a new structural superfamily. This likelihood is derived from the distance in the clustering between the (sub)sequence and sequences that have known structures. The output of ProTarget is a graphical visualization of the protein of interest together with the likelihood that a protein sequence represents a novel structural superfamily. ProTarget is updated regularly and currently covers over 160 000 protein sequences from the SwissProt and PDB databases. ProTarget is available at http://www.protarget.cs.huji.ac.il.
| S-EPMC1160150 | biostudies-literature
Project description:Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).
| S-EPMC5104774 | biostudies-literature
Project description:Novelty detection is a core feature of behavioral adaptation and involves cascades of neuronal responses-from initial evaluation of the stimulus to the encoding of new representations-resulting in the behavioral ability to respond to unexpected inputs. In the past decade, a new important novelty detection feature, beta2 (~20-30 Hz) oscillations, has been described in the hippocampus (HC). However, the interactions between beta2 and the hippocampal network are unknown, as well as the role-or even the presence-of beta2 in other areas involved with novelty detection. In this work, we combined multisite local field potential (LFP) recordings with novelty-related behavioral tasks in mice to describe the oscillatory dynamics associated with novelty detection in the CA1 region of the HC, parietal cortex, and mid-prefrontal cortex. We found that transient beta2 power increases were observed only during interaction with novel contexts and objects, but not with familiar contexts and objects. Also, robust theta-gamma phase-amplitude coupling was observed during the exploration of novel environments. Surprisingly, bursts of beta2 power had strong coupling with the phase of delta-range oscillations. Finally, the parietal and mid-frontal cortices had strong coherence with the HC in both theta and beta2. These results highlight the importance of beta2 oscillations in a larger hippocampal-cortical circuit, suggesting that beta2 plays a role in the mechanism for detecting and modulating behavioral adaptation to novelty.
| S-EPMC7921172 | biostudies-literature
Project description:Memory for odors is believed to be longer-lasting than memory for visual stimuli, as is evidenced by flat forgetting curves. However, performance on memory tasks is typically weaker in olfaction than vision. Studies of odor memory that use forced-choice methods confound responses that are a result of a trace memory and responses that can be obtained through process of elimination. Moreover, odor memory is typically measured with common stimuli, which are more familiar and responses may be confounded by verbal memory, and measure memory in intentional learning conditions, which are ecologically questionable. Here we demonstrate the value of using tests of memory in which hit rate and correct rejection rate are evaluated separately (i.e., not using forced-choice methods) and uncommon stimuli are used. This study compared memory for common and uncommon odors and pictures that were learned either intentionally (Exp. 1) or incidentally (Exp. 2) and tested with either a forced-choice or a one-stimulus-at-a-time ("monadic") recognition task after delays of 15 min, 48 h or 1 week. As expected, memory declined with delay in most conditions, but depended upon the particular measure of memory and was better for pictures than odors and for common than uncommon stimuli. For common odors, hit rates decreased with delay but correct rejection rates remained constant with delay. For common pictures, we found the opposite result, constant hit rates and decreased correct rejection rates. Our results support the 'misfit theory of conscious olfactory perception', which highlights the importance of the detection of novelty in olfactory memory and suggests that olfactory memory should be studied using more ecologically valid methods.
| S-EPMC8470488 | biostudies-literature
Project description:Background: To develop a tool for assessing normalcy of the thoracic aorta (TA) by echocardiography, based on either a linear regression model (Z-score), or a machine learning technique, namely one-class support vector machine (OC-SVM) (Q-score). Methods: TA diameters were measured in 1112 prospectively enrolled healthy subjects, aging 5 to 89 years. Considering sex, age and body surface area we developed two calculators based on the traditional Z-score and the novel Q-score. The calculators were compared in 198 adults with TA > 40 mm, and in 466 patients affected by either Marfan syndrome or bicuspid aortic valve (BAV). Results: Q-score attained a better Area Under the Curve (0.989; 95% CI 0.984-0.993, sensitivity = 97.5%, specificity = 95.4%) than Z-score (0.955; 95% CI 0.942-0.967, sensitivity = 81.3%, specificity = 93.3%; p < 0.0001) in patients with TA > 40 mm. The prevalence of TA dilatation in Marfan and BAV patients was higher as Z-score > 2 than as Q-score < 4% (73.4% vs. 50.09%, p < 0.00001). Conclusions: Q-score is a novel tool for assessing TA normalcy based on a model requiring less assumptions about the distribution of the relevant variables. Notably, diameters do not need to depend linearly on anthropometric measurements. Additionally, Q-score can capture the joint distribution of these variables with all four diameters simultaneously, thus accounting for the overall aortic shape. This approach results in a lower rate of predicted TA abnormalcy in patients at risk of TA aneurysm. Further prognostic studies will be necessary for assessing the relative effectiveness of Q-score versus Z-score.
| S-EPMC7912952 | biostudies-literature
Project description:Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neuroscience. Convolutional neural networks, for example, were designed to exploit translation symmetry, and their capabilities triggered the first wave of deep learning successes. However, learning convolutions directly from translation-invariant data with a fully connected network has so far proven elusive. Here we show how initially fully connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs, resulting in localized, space-tiling receptive fields. These receptive fields match the filters of a convolutional network trained on the same task. By carefully designing data models for the visual scene, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs, which has long been recognized as the hallmark of natural images. We provide an analytical and numerical characterization of the pattern formation mechanism responsible for this phenomenon in a simple model and find an unexpected link between receptive field formation and tensor decomposition of higher-order input correlations. These results provide a perspective on the development of low-level feature detectors in various sensory modalities and pave the way for studying the impact of higher-order statistics on learning in neural networks.
| S-EPMC9546588 | biostudies-literature
Project description:The detection of novel events and their identification is a basic prerequisite in a rapidly changing environment. Recently, the processing of novelty has been shown to rely on the hippocampus and to be associated with activity in reward-related areas. The present study investigated the influence of spatial attention on neural processing of novel relative to frequently presented standard and target stimuli. Never-before-seen Mandelbrot-fractals absent of semantic content were employed as stimulus material. Consistent with current theories, novelty activated a widespread network of brain areas including the hippocampus. No activity, however, could be observed in reward-related areas with the novel stimuli absent of a semantic meaning employed here. In the perceptual part of the novelty-processing network a region in the lingual gyrus was found to specifically process novel events when they occurred outside the focus of spatial attention. These findings indicate that the initial detection of unexpected novel events generally occurs in specialized perceptual areas within the ventral visual stream, whereas activation of reward-related areas appears to be restricted to events that do possess a semantic content indicative of the biological relevance of the stimulus.
| S-EPMC6871036 | biostudies-literature
Project description:Previous research indicates that the amygdala and hippocampus are sensitive to novelty; however, two types of novelty can be distinguished - stimuli that are ordinary, but novel in the current context, and stimuli that are unusual. Using functional magnetic resonance imaging, we examined blood oxygen dependent level (BOLD) response of the human amygdala and hippocampus to novel, commonly seen objects versus novel unusual objects. When presented with the novel common stimuli, the BOLD signal increased significantly in both the amygdala and hippocampus. However, for the novel unusual stimuli, only the amygdala showed an increased response compared to the novel common stimuli. These findings suggest that the amygdala is distinctly responsive to novel unusual stimuli, making a unique contribution to the novelty detection circuit.
| S-EPMC2830341 | biostudies-literature