Learning complex nonlinear dynamics of a 3D translational parallel manipulator using neural network.

This study investigates modeling the dynamics of a 3D translational parallel manipulator with closed chains using feedforward neural networks (FFNNs). The dataset exceeds 50,000 samples, incorporating experimental data collected from a robot prototype using MATLAB® real-time workshop and the National InstrumentsTM DAQ toolbox, as well as CAD simulation data from MSC ADAMS software. While achieving satisfactory mean squared error (MSE), some predictions did not fully capture the manipulator's dynamics, with small overfitting observed. A Deep Neural Network (DNN) was tested but faced overfitting and high computational costs, despite being trained on a subset of the dataset. This highlighted the limitations of DNNs for modeling such complicated parallel robots with closed chains and parallelograms. FFNNs were preferred for their simplicity and lower overfitting risk. L2 regularization and k-fold validation were applied to improve performance. Transfer learning (TL) was also employed, fine-tuning a new network with weights from pre-trained FFNNs using a smaller, unseen dataset. This approach significantly reduced MSE and completely eliminated overfitting, demonstrating the effectiveness of TL in refining model performance for forward and inverse dynamics. These findings suggest that FFNNs, combined with TL, L2 regularization, and k-fold validation, offer a robust method for accurately modeling complex robotic dynamics, enhancing control and optimization strategies for complicated robotic systems. Training for all networks was conducted within the MATLAB® environment.

Application of a novel numerical simulation to biochemical reaction systems.

Recent advancements in omics and single-cell analysis highlight the necessity of numerical methods for managing the complexity of biological data. This paper introduces a simulation program for biochemical reaction systems based on the natural number simulation (NNS) method. This novel approach ensures the equitable treatment of all molecular entities, such as DNA, proteins, H2O, and hydrogen ions (H+), in biological systems. Central to NNS is its use of stoichiometric formulas, simplifying the modeling process and facilitating efficient and accurate simulations of diverse biochemical reactions. The advantage of this method is its ability to manage all molecules uniformly, ensuring a balanced representation in simulations. Detailed in Python, NNS is adept at simulating various reactions, ranging from water ionization to Michaelis-Menten kinetics and complex gene-based systems, making it an effective tool for scientific and engineering research.

Grasp context-dependent uncertainty alters the relative contribution of anticipatory and feedback-based mechanisms in object manipulation.

Predictive control within dexterous object manipulation while allowing for the choice of contact points has been shown to employ a predominantly feedback-based force modulation. The anticipation is thought to be facilitated through the internal representation of the object dynamics being integrated and updated on a trial-to-trial basis with the feedback of contact locations on the object. This is as opposed to the classically studied memory representation-based fingertip force control for grasping with pre-selected contact locations. We designed a study to examine this grasp context-dependent asymmetry in sensorimotor integration by introducing binary uncertainty about the grasp type before movement initiation within the framework of motor planning. An inverted T-shaped instrumented object was presented to 24 participants as the manipulandum, and they were asked to reach, grasp, and lift it while minimising the peak roll. We dissociated the planning and the execution phases by pseudo-randomly manipulating the availability of visual contact cues on the object after movement onset. We analysed both derived as well as direct kinetic and kinematic measures of the grasp during the loading phase to understand the anticipatory coordination. Our findings suggest that uncertainty about the grasp context during movement preparation resulted in a shift towards feedback-based mechanisms for grasp force modulation despite the persistence of visual cues.

A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

A Smith Predictor Modified with a Pseudo Feedforward Control for the Charge-Coupled Device-Based Optoelectronic Tracking System.

In the high-precision optoelectronic tracking system (OTS) based on a charge-coupled device (CCD), the boresight error extracted from the tracking image contains an undeniable delay, which directly limits the control bandwidth of visual tracking. High bandwidth means high response speed and tracking accuracy. Generally, a model-based delay compensation control method called the Smith predictor is utilized to separate time delay from the closed loop to promote the control bandwidth. However, due to the existence of errors between the established model and the real object, the improvement in the bandwidth is still limited to ensure system stability, resulting in insufficient tracking performance. In this paper, to solve the problem, a Smith predictor modified with pseudo feedforward control for the OTS is proposed. The experimental results demonstrate that the proposed method achieves significant improvements in tracking performance, reducing the maximum residual error at 1 Hz from 365 arcseconds (using the classic Smith predictor) to 283 arcseconds, a 22.5% improvement. Across the main frequency band (0.2 Hz to 2 Hz), the residual errors were consistently lower using the proposed method.

Regulation of an RNA toxin-antitoxin system, SdsR-RyeA, by a small RNA GcvB.

The toxin-antitoxin (TA) system regulates many physiological processes in free-living bacteria. One such TA system in Escherichia coli comprises an RNA toxin SdsR and an antitoxin RyeA. An overabundance of SdsR is toxic to the cells. RyeA normalizes SdsR abundance and helps the cells to adapt to altered conditions. The current study showed that a novel small RNA (sRNA) regulator GcvB directly interacts with RyeA to maintain its abundance in the cells under normal or low pH conditions. The deletion of the gcvB allele in the E. coli chromosome resulted in a ∼3-fold decrease in intrabacterial RyeA accumulation. An ectopic expression of GcvB in ΔgcvB strain reinstated RyeA abundance to its normal level. Induction of GcvB in the cells upon exposure to low pH resulted in a simultaneous increase in intracellular RyeA. While GcvB increases RyeA abundance in the cells, SdsR accumulation is divergently regulated by GcvB. The absence of the gcvB gene in E. coli leads to upregulation of SdsR and vice versa. The GcvB-mediated decrease of SdsR accumulation stems from the increased RyeA-driven normalization of SdsR. This study delineates a novel mechanism for the regulation of the expression of an RNA toxin SdsR by another sRNA regulator GcvB through a feed-forward control.

Fuzzy inference system enabled neural network feedforward compensation for position leap control of DC servo motor.

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

Homoharringtonine promotes non-small-cell lung cancer cell death via modulating HIF-1α/ERß/E2F1 feedforward loop.

OBJECTIVES: Hypoxia conditions promote the adaptation and progression of non-small-cell lung cancer (NSCLC) via hypoxia-inducible factors (HIF). HIF-1α may regulate estrogen receptor ß (ERß) and promote the progression of NSCLC. The phytochemical homoharringtonine (HHT) exerts strong inhibitory potency on NSCLC, with molecular mechanism under hypoxia being elusive. METHODS: The effects of HHT on NSCLC growth were determined by cell viability assay, colony formation, flow cytometry, and H460 xenograft models. Western blotting, molecular docking program, site-directed mutagenesis assay, immunohistochemical assay, and immunofluorescence assay were performed to explore the underlying mechanisms of HHT-induced growth inhibition in NSCLC. KEY FINDINGS: HIF-1α/ERß signaling-related E2F1 is highly expressed and contributes to unfavorable survival and tumor growth. The findings in hypoxic cells, HIF-1α overexpressing cells, as well as ERß- or E2F1-overexpressed and knockdown cells suggest that the HIF-1α/ERß/E2F1 feedforward loop promotes NSCLC cell growth. HHT suppresses HIF-1α/ERß/E2F1 signaling via the ubiquitin-proteasome pathway, which is dependent on the inhibition of the protein expression of HIF-1α and ERß. Molecular docking and site-directed mutagenesis revealed that HHT binds to the GLU305 site of ERß. HHT inhibits cell proliferation and colony formation and promotes apoptosis in both NSCLC cells and xenograft models. CONCLUSION: The formation of the HIF-1α/ERß/E2F1 feedforward loop promotes NSCLC growth and reveals a novel molecular mechanism by which HHT induces cell death in NSCLC.

Study the hydrotropic behaviour of butyl stearate using ANN tools.

This study investigates the prediction of the thermophysical properties of butyl stearate in solutions with citric acid, urea, and nicotinamide using Artificial Neural Networks (ANNs). The ANN model uses hydrotropic concentration and temperature to predict these properties. The study focuses on binary mixtures at various temperatures (303, 313, 323, and 333 K). To achieve accurate predictions, researchers trained a committee of ANNs using experimental data. This iterative process optimizes the network architecture and avoids overfitting, a common problem in machine learning. The trained ANN can then predict thermophysical properties for intermediate hydrotropic concentrations without additional experiments. Besides, the study demonstrates the versatility of ANNs by implementing a successful model for multi-pass turning operations in MATLAB, showing superior accuracy compared to other methods. Visualizations like magnitude response curves, FFT spectrums, contour plots, and 3D surface plots helped to identify the optimal hydrotropic concentration with a remarkable 2% margin of error.

Corticothalamic input derived from corticospinal neurons contributes to chronic neuropathic pain after spinal cord injury.

Neuropathic pain is a significant and persistent issue for individuals with spinal cord injuries (SCI), severely impacting their quality of life. While changes at the peripheral and spinal levels are known to contribute to SCI-related pain, whether and how supraspinal centers contribute to post SCI chronic neuropathic pain is poorly understood. Here, we first validated delayed development of chronic neuropathic pain in mice with moderate contusion SCI. To identify supraspinal regions involved in the pathology of neuropathic pain after SCI, we next performed an activity dependent genetic screening and identified multiple cortical and subcortical regions that were activated by innocuous tactile stimuli at a late stage following contusion SCI. Notably, chemogenetic inactivation of pain trapped neurons in the lateral thalamus alleviated neuropathic pain and reduced tactile stimuli evoked cortical overactivation. Retrograde tracing showed that contusion SCI led to enhanced corticothalamic axonal sprouting and over-activation of corticospinal neurons. Mechanistically, ablation or silencing of corticospinal neurons prevented the establishment or maintenance of chronic neuropathic pain following contusion SCI. These results highlighted a corticospinal-lateral thalamic feed-forward loop whose activation is required for the development and maintenance of chronic neuropathic pain after SCI. Our data thus shed lights into the central mechanisms underlying chronic neuropathic pain associated with SCI and the development of novel therapeutic avenues to treat refractory pain caused by traumatic brain or spinal cord injuries.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit.

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

An incoherent feed-forward loop involving bHLH transcription factors, Auxin and CYCLIN-Ds regulates style radial symmetry establishment in Arabidopsis.

The bilateral-to-radial symmetry transition occurring during the development of the Arabidopsis thaliana female reproductive organ (gynoecium) is a crucial biological process linked to plant fertilization and seed production. Despite its significance, the cellular mechanisms governing the establishment and breaking of radial symmetry at the gynoecium apex (style) remain unknown. To fill this gap, we employed quantitative confocal imaging coupled with MorphoGraphX analysis, in vivo and in vitro transcriptional experiments, and genetic analysis encompassing mutants in two bHLH transcription factors necessary and sufficient to promote transition to radial symmetry, SPATULA (SPT) and INDEHISCENT (IND). Here, we show that defects in style morphogenesis correlate with defects in cell-division orientation and rate. We showed that the SPT-mediated accumulation of auxin in the medial-apical cells undergoing symmetry transition is required to maintain cell-division-oriented perpendicular to the direction of organ growth (anticlinal, transversal cell division). In addition, SPT and IND promote the expression of specific core cell-cycle regulators, CYCLIN-D1;1 (CYC-D1;1) and CYC-D3;3, to support progression through the G1 phase of the cell cycle. This transcriptional regulation is repressed by auxin, thus forming an incoherent feed-forward loop mechanism. We propose that this mechanism fine-tunes cell division rate and orientation with the morphogenic signal provided by auxin, during patterning of radial symmetry at the style.

Optimal sway motion reduction in forestry cranes.

Introduction: The paper introduces a novel optimal feedforward controller for Hydraulic manipulators equipped with a passive grapple, addressing the issue of sway during and after movement. The controller is specifically applied to a forwarder machine used in forestry for log-loading tasks. Methods: The controller is designed for smooth operation, low computational demands, and efficient sway damping. Customizable parameters allow adjustments to suit operator preferences. The implementation was carried out using the Amesim model of a forwarder. Results: Simulation results indicate a significant reduction in sway motions, averaging a decrease of more than 60%. This performance was achieved without the need for additional sway-detection sensors, which simplifies the system design and reduces costs. Discussion: The proposed method demonstrates versatility and broad applicability, offering a new framework for anti-sway controllers in various fields such as construction cranes, forestry vehicles, aerial drones, and other robotic manipulators with passive end-effectors. This adaptability could lead to significant advances in safety and efficiency.

A Novel Friction Compensation Method for Machine Tool Drive Systems in Insufficient Lubrication.

Friction is the dominant factor restricting tracking accuracy and machining surface quality in mechanical systems such as machine tool feed-drive. Hence, friction modeling and compensation is an important method in accurate tracking control of CNC machine tools used for welding, 3D printing, and milling, etc. Many static and dynamic friction models have been proposed to compensate for frictional effects to reduce the tracking error in the desired trajectory and to improve the surface quality. However, most of them focus on the friction characteristics of the pre-sliding zone and low-speed sliding regions. These models do not fully describe friction in the case of insufficient lubrication or high acceleration and deceleration in machine tool systems. This paper presents a new nonlinear friction model that includes the typical Coulomb-Viscous friction, a nonlinear periodic harmonic friction term for describing the lead screw property in insufficient lubrication, and a functional component of acceleration for describing the friction lag caused by the acceleration and deceleration of the system. Experiments were conducted to compare the friction compensation performance between the proposed and the conventional friction models. Experimental results indicate that the root mean square and maximum absolute tracking error can be significantly reduced after applying the proposed friction model.

The effects of combining sensorimotor training with transcranial direct current stimulation on the anticipatory and compensatory postural adjustments in patients with chronic low back pain.

PURPOSE: To investigate the effects of concurrent sensorimotor training (SMT) and transcranial direct current stimulation (tDCS) on the anticipatory and compensatory postural adjustments (APAs and CPAs) in patients with chronic low back pain (CLBP). METHOD: The interventions included (1) SMT plus tDCS and (2) SMT plus sham tDCS. Outcome measures were the normalized integrals of electromyography activity (NIEMG) during the phases of anticipatory and compensatory, and muscle onset latency. The investigated muscles were ipsilateral and contralateral multifidus (MF), transversus abdominus/internal oblique (TrA/IO), and gluteus medius (GM). RESULTS: Between-group comparisons demonstrated that ipsilateral TrA/IO NIEMG during CPA1 (p = 0.010) and ipsilateral GM NIEMG during CPA1 (p = 0.002) and CPA2 (p = 0.025) were significantly lower in the SMT combined with tDCS than in the control group. Furthermore, this group had greater NIEMG for contralateral GM during APA1 than the control group (p = 0.032). Moreover, the onset latency of contralateral TrA/IO was significantly earlier after SMT combined with tDCS (p = 0.011). CONCLUSIONS: Both groups that received SMT showed positive effects, but anodal tDCS had an added value over sham stimulation for improving postural control strategies in patients with CLBP. Indeed, SMT combined with tDCS leads to stronger APA and less demand for CPA. RCT REGISTRATION NUMBER: IRCT20220228054149N1. REGISTRATION DATE: 2022-04-04.

Evidence suggests that reduced excitability in the sensory and motor cortex is linked to chronic and recurring lower back pain.Increasing the excitability of these two areas using anodal transcranial direct current stimulation (tDCS), in conjunction with sensorimotor training (SMT), may improve anticipatory and compensatory postural control strategies.This study showed that the combination of SMT with tDCS targeting the sensory and motor cortex notably enhances motor preparation and refines postural control strategies in patients with chronic unilateral lumbar radiculopathy.Rehabilitation professionals are encouraged to integrate SMT with tDCS into treatment protocols to enhance the ability of individuals with back pain to handle postural disturbances in daily life, thereby potentially alleviating the persistence of their symptoms.Incorporating brain stimulation enhances the effectiveness of SMT for patients with chronic unilateral lumbar radiculopathy.

The Homburg-Adelaide toric IOL nomogram: How to predict corneal power vectors from preoperative IOLMaster 700 keratometry and total corneal power in toric IOL implantation.

PURPOSE: The purpose of this study is to compare the reconstructed corneal power (RCP) by working backwards from the post-implantation spectacle refraction and toric intraocular lens power and to develop the models for mapping preoperative keratometry and total corneal power to RCP. METHODS: Retrospective single-centre study involving 442 eyes treated with a monofocal and trifocal toric IOL (Zeiss TORBI and LISA). Keratometry and total corneal power were measured preoperatively and postoperatively using IOLMaster 700. Feedforward neural network and multilinear regression models were derived to map keratometry and total corneal power vector components (equivalent power EQ and astigmatism components C0 and C45) to the respective RCP components. RESULTS: Mean preoperative/postoperative C0 for keratometry and total corneal power was -0.14/-0.08 dioptres and -0.30/-0.24 dioptres. All mean C45 components ranged between -0.11 and -0.20 dioptres. With crossvalidation, the neural network and regression models showed comparable results on the test data with a mean squared prediction error of 0.20/0.18 and 0.22/0.22 dioptres2 and on the training data the neural network models outperformed the regression models with 0.11/0.12 and 0.22/0.22 dioptres2 for predicting RCP from preoperative keratometry/total corneal power. CONCLUSIONS: Based on our dataset, both the feedforward neural network and multilinear regression models showed good precision in predicting the power vector components of RCP from preoperative keratometry or total corneal power. With a similar performance in crossvalidation and a simple implementation in consumer software, we recommend implementation of regression models in clinical practice.

Does pre-speech auditory modulation reflect processes related to feedback monitoring or speech movement planning?

Previous studies have revealed that auditory processing is modulated during the planning phase immediately prior to speech onset. To date, the functional relevance of this pre-speech auditory modulation (PSAM) remains unknown. Here, we investigated whether PSAM reflects neuronal processes that are associated with preparing auditory cortex for optimized feedback monitoring as reflected in online speech corrections. Combining electroencephalographic PSAM data from a previous data set with new acoustic measures of the same participants' speech, we asked whether individual speakers' extent of PSAM is correlated with the implementation of within-vowel articulatory adjustments during /b/-vowel-/d/ word productions. Online articulatory adjustments were quantified as the extent of change in inter-trial formant variability from vowel onset to vowel midpoint (a phenomenon known as centering). This approach allowed us to also consider inter-trial variability in formant production and its possible relation to PSAM at vowel onset and midpoint separately. Results showed that inter-trial formant variability was significantly smaller at vowel midpoint than at vowel onset. PSAM was not significantly correlated with this amount of change in variability as an index of within-vowel adjustments. Surprisingly, PSAM was negatively correlated with inter-trial formant variability not only in the middle but also at the very onset of the vowels. Thus, speakers with more PSAM produced formants that were already less variable at vowel onset. Findings suggest that PSAM may reflect processes that influence speech acoustics as early as vowel onset and, thus, that are directly involved in motor command preparation (feedforward control) rather than output monitoring (feedback control).

Positive feed-forward regulation of nitrate uptake by rice roots and its molecular mechanism.

To investigate the positive feed-forward regulatory mechanism of nitrate uptake by rice, its responses to various light and carbohydrates were compared. In order to measure nitrate uptake in real time, the non-invasive method was used. The results showed that net nitrate uptake increased in the light and decreased in the dark, and finally reached a steady state after about 5 h. Based on it, carbohydrates effects could be investigated without considering light effects. After sucrose addition for 2 h, net nitrate uptake increased by about 80% without a lag, while glucose, fructose and raffinose had a slight effect with a lag and other sugars had no effect. It provided an evidence that sucrose was a positive feed-forward signal molecule of nitrate uptake by rice roots. To further analyze the effect of sucrose on the expression of high affinity nitrate transporter genes OsNRT2.1, OsNRT2.2, OsNRT2.3a and OsNRT2.3b, qRT-PCR was used to further verify after treated with 10 mM sucrose. The results revealed that these genes expression was immediately up-regulated, which indicated that these genes were post transcriptionally regulated. Further, 15N exchange dynamics analyzed N transport. It is benefit for increasing nitrate uptake by rice and improving its yield.

Detection of Parkinson disease using multiclass machine learning approach.

Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80-20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson's Disease.

Unveiling the neural dynamics of conscious perception in rapid object recognition.

Our brain excels at recognizing objects, even when they flash by in a rapid sequence. However, the neural processes determining whether a target image in a rapid sequence can be recognized or not remains elusive. We used electroencephalography (EEG) to investigate the temporal dynamics of brain processes that shape perceptual outcomes in these challenging viewing conditions. Using naturalistic images and advanced multivariate pattern analysis (MVPA) techniques, we probed the brain dynamics governing conscious object recognition. Our results show that although initially similar, the processes for when an object can or cannot be recognized diverge around 180 ms post-appearance, coinciding with feedback neural processes. Decoding analyses indicate that gist perception (partial conscious perception) can occur at ∼120 ms through feedforward mechanisms. In contrast, object identification (full conscious perception of the image) is resolved at ∼190 ms after target onset, suggesting involvement of recurrent processing. These findings underscore the importance of recurrent neural connections in object recognition and awareness in rapid visual presentations.