Decoding Time Research Articles

The representation of stimulus features during stable fixation and active vision.

Predictive updating of an object's spatial coordinates from pre-saccade to post-saccade contributes to stable visual perception. Whether object features are predictively remapped remains contested. We set out to characterise the spatiotemporal dynamics of feature processing during stable fixation and active vision. To do so, we applied multivariate decoding methods to electroencephalography (EEG) data collected while human participants (male and female) viewed brief visual stimuli. Stimuli appeared at different locations across the visual field at either high or low spatial frequency (SF). During fixation, classifiers were trained to decode SF presented at one parafoveal location and cross-tested on SF from either the same, adjacent or more peripheral locations. When training and testing on the same location, SF was classified shortly after stimulus onset (∼79 ms). Decoding of SF at locations farther from the trained location emerged later (∼144 - 295 ms), with decoding latency modulated by eccentricity. This analysis provides a detailed time course for the spread of feature information across the visual field. Next, we investigated how active vision impacts the emergence of SF information. In the presence of a saccade, the decoding time of peripheral SF at parafoveal locations was earlier, indicating predictive anticipation of SF due to the saccade. Crucially however, this predictive effect was not limited to the specific remapped location. Rather, peripheral SF was correctly classified, at an accelerated time course, at all parafoveal positions. This indicates spatially coarse, predictive anticipation of stimulus features during active vision, likely enabling a smooth transition on saccade landing.Significance Statement Maintaining a continuous representation of object features across saccades is vital for stable vision. In order to characterise the spatiotemporal dynamics of stimulus feature representation in the brain, we presented stimuli at a high and low spatial frequency at multiple locations across the visual field. Applying EEG-decoding methods we tracked the neural representation of spatial frequency during both stable fixation and active vision. Using this approach, we provide a detailed time course for the spread of feature information across the visual field during fixation. In addition, when a saccade is imminent, we show that peripheral spatial frequency is predictively represented in anticipation of the post-saccadic input.

Lossless Image Compression Using Context-Dependent Linear Prediction Based on Mean Absolute Error Minimization.

This paper presents a method for lossless compression of images with fast decoding time and the option to select encoder parameters for individual image characteristics to increase compression efficiency. The data modeling stage was based on linear and nonlinear prediction, which was complemented by a simple block for removing the context-dependent constant component. The prediction was based on the Iterative Reweighted Least Squares (IRLS) method which allowed the minimization of mean absolute error. Two-stage compression was used to encode prediction errors: an adaptive Golomb and a binary arithmetic coding. High compression efficiency was achieved by using an author's context-switching algorithm, which allows several prediction models tailored to the individual characteristics of each image area. In addition, an analysis of the impact of individual encoder parameters on efficiency and encoding time was conducted, and the efficiency of the proposed solution was shown against competing solutions, showing a 9.1% improvement in the bit average of files for the entire test base compared to JPEG-LS.

Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI

Reactive Brain-Computer Interfaces (rBCIs) typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small, randomly-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction.

Methods: In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditional Plain flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-class Code Visually Evoked Potentials (c-VEP) paradigm featuring low-frequency, short, and aperiodic visual flashes.

Results: Subjective ratings revealed that Gabor and Ricker stimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings in naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 seconds.

Conclusion: This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low-density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.

Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding

Batched network coding (BNC) is a practical realization of random linear network coding (RLNC) designed for reliable network transmission in multi-hop networks with packet loss. By grouping coded packets into batches and restricting the use of RLNC within the same batch, BNC resolves the issue of RLNC that has high computational and storage costs at the intermediate nodes. A simple and common way to apply BNC is to fire and forget the recoded packets at the intermediate nodes, as BNC can act as an erasure code for data recovery. Due to the finiteness of batch size, the recoding strategy is a critical design that affects the throughput, the storage requirements, and the computational cost of BNC. The gain of the recoding strategy can be enhanced with the aid of a feedback mechanism, however the utilization and development of this mechanism is not yet standardized. In this paper, we investigate a multi-phase recoding mechanism for BNC. In each phase, recoding depends on the amount of innovative information remained at the current node after the transmission of the previous phases was completed. Relevant information can be obtained via hop-by-hop feedback; then, a more precise recoding scheme that allocates networking resources can be established. Unlike hop-by-hop retransmission schemes, the reception status of individual packets does not need to be known and packets to be sent in the next phase may not be the lost packets in the previous phase. Further, due to the loss-tolerance feature of BNC, it is unnecessary to pass all innovative information to the next node. This study illustrates that multi-phase recoding can significantly boost the throughput and reduce the decoding time as compared with the traditional single-phase recoding approach This opens a new window in developing better strategies for designing BNC rather than sending more batches in a blind manner.

Two-Phase Globally Coupled Low-Density Parity Check Decoding Aided with Early Termination and Forced Convergence.

To enhance the decoding efficiency of Globally Coupled (GC) LDPC codes, we incorporated Early Termination (ET) and Forced Convergence (FC) into the local/global two-phase decoding algorithm to expedite the decoding process. The two-phase decoding scheme integrates the ET technique to halt unnecessary iterations in the local decoding phase while employing the FC technique to accelerate convergence in the global phase decoding. The application of ET technology in the local decoding of GC-LDPC codes will not cause performance loss as in traditional block codes and will cause considerable complexity gains. For a longer code length and larger convergence differences between nodes' global codes, the FC technique operates more efficiently in global code than local code. Two variants are proposed for the ET scheme in the local decoding, namely ET-1 and ET-2. The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. In the case of ET-2, the saved local iterations are transformed to global decoding equally. The results show that ET-1 saves considerable decoding time complexity and ET-2 improves the performance of the GC-LDPC code with the same decoding time complexity. The combined approach of ET-1 with FC reduces the decoding time complexity up to 42% at a low Signal Noise Rate region while maintaining its performance; ET-2-FC two-phase decoding saves approximately 25% decoding time complexity while improving the BER by about 0.18 dB and FER by about 0.23 dB.

Real-Time Decoding of Snapshot Compressive Imaging Using Tensor FISTA-Net.

Snapshot compressive imaging (SCI) cameras compress high-speed videos or hyperspectral images into measurement frames. However, decoding the data frames from measurement frames is compute-intensive. Existing state-of-the-art decoding algorithms suffer from low decoding quality or heavy running time or both, which are not practical for real-time applications. In this article, we exploit the powerful learning ability of deep neural networks (DNN) and propose a novel tensor fast iterative shrinkage-thresholding algorithm net (Tensor FISTA-Net) as a real-time decoder for SCI cameras. Since SCI cameras have an accurate physical model, we can trade training time for the decoding time by generating abundant synthetic data and training a decoder on the cloud. Tensor FISTA-Net not only learns a sparse representation of the frames through convolution layers but also reduces the decoding time and memory consumption significantly through tensor operations, which makes Tensor FISTA-Net an appropriate approach for a real-time decoder. Our proposed Tensor FISTA-Net obtains an average PSNR improvement of 0.79-2.84 dB (video images) and 2.61-4.43 dB (hyperspectral images) over the state-of-the-art algorithms, along with more clear and detailed visual results on real SCI datasets, Hammer and Wheel, respectively. Our Tensor FISTA-Net reaches 45 frames per second in video datasets and 70 frames per second in hyperspectral datasets, meeting the real-time requirement. Besides, the trained model occupies only a 12 -MB memory footprint, making it applicable to real-time Internet of Things (IoT) applications.

Abnormal state prediction of flotation process based on dual attention mechanism and multivariate information fusion

AbstractPredicting abnormal conditions in flotation processes is vital for safety, efficiency, and product quality. However, existing studies lack predictions of abnormal working conditions in flotation processes and neglect temporal information in data. To address this, this paper proposes a novel approach for predicting abnormal work conditions in flotation processes. It utilizes a dual attention mechanism and multivariate information fusion. Features are extracted from froth images using the Xception model, a pre‐trained convolutional neural network. These features are combined with flotation process monitoring variables, creating fused data. An encoder and decoder time feature seq2seq (EDTF‐seq2seq) model with time and feature attention modules enables end‐to‐end information fusion and work condition prediction. The attention modules assign weights to each feature point, capturing the time–feature relationship and improving prediction accuracy. Four sets of experiments using real flotation process data validate the effectiveness of the proposed method, achieving favourable prediction accuracy.

Adaptively hybrid fractal image coding

AbstractAn adaptively hybrid method was proposed to improve the performance of fractal coding methods. First, it is found that the range blocks with large variances (RBLVs) play a crucial role in degrading decoded images, and the effect of the remaining range blocks with small variances (RBSVs) can be ignored. Then, an adaptive method was proposed to divide the range blocks into the above two categories: RBLVs and RBSVs. Second, RBLVs were designed to be encoded in an extended domain block pool (EDBP). Then, better block‐matching effect can be obtained, which will result in better decoded image quality. Further, the no‐search fractal encoding method is adopted for RBSVs to achieve faster encoding speed and fewer bits per pixel (bpp). Finally, four fractal coding methods were adopted to assess the performance of the proposed method. Experimental results show that compared with the previous methods, the PSNR quality of decoded images in the proposed method can be improved by about 0.15–0.4 dB, about 20%–35% of the total computations in encoding process can be saved, and about 0.2 bpp can be saved. Moreover, under the same decoding time, the proposed method can achieve comparable or smaller deviations regarding the decoded image.

A Light-Weight Autoregressive CNN-Based Frame Level Transducer Decoder for End-to-End ASR

A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy. The CNN of 177 k parameters and a kernel size of 6 generates the probabilities of the current token at the token level, at the token transition of the output token sequence. Two probabilities of the current token, one from the encoder and the other from the CNN are added to the frame level to reduce the decoding step to the number of input frames. An encoder composed of an 18-layer conformer was combined with the proposed decoder for training with the Librispeech data set. The forward-backward algorithm was used for training. The space and re-appearance tokens are added to the 300-word piece tokens to represent the token string. A space token appears at a frame between two words. A comparison with the autoregressive decoders such as transformer and RNN-T decoders demonstrates that this work provides comparable WERs with much less decoding time. A comparison with non-autoregressive decoders such as CTC indicates that this work enhanced WERs.

Software for assessment and monitoring the decoding skills development of children from the elementary school: validity based on response process.

To continue the validation process of the Decoding Development Monitoring Protocol (PRADE) in software format in the validity evidence stage based on response processes. 250 individuals participated in this study, 125 individuals from private schools and 125 individuals from public schools. The assessment was carried out in person using the software that hosts the instrument's tasks, which are organized into decoding linguistically balanced words and non-words, respecting the decoding rules of Brazilian Portuguese. The software prepares an individual performance report for each participant, counting the decoding time for each stimulus, as well as the number of words decoded correctly. The data is organized considering the correct decoding time of the stimuli, decoding accuracy and percentage of correct answers. All data underwent statistical analysis using SPSS software. The data indicated an important effect of the length of words and non-words on public and private school students. Furthermore, it was possible to observe the evolution of decoding, depending on the school year, in all the variables studied. In both groups, a strong influence of non-words on student performance throughout Elementary School I was observed. The data indicate validity in the analysis of response processes, since it was possible to adequately characterize the performance of school children public and private throughout Elementary School I, characterizing each group, as well as their differences according to the advancement of schooling.

Desempenho em decodificação e escrita de crianças com Transtorno do Desenvolvimento da Linguagem: dados preliminares

ABSTRACT Purpose to verify the performance of children with Developmental Language Disorder in decoding and writing tests in order to better understand their manifestations and the process of acquiring written language skills. Methods The study subjects were 80 children. The Research Group consisted of 16 children diagnosed with Developmental Language Disorder, 13 males and 3 females, mean age of 7.3. The Control Group counted on 64 subjects paired in gender, age, education and socioeconomic level with the Control Group in a 4:1 ratio. The ability to decode words and pseudowords of both groups was evaluated, measuring the time spent to correctly read words and the percentage of correct answers, also considering the length of the word/pseudoword. The writing evaluation was carried out in the control group, which had its spelling errors analyzed and categorized. All data underwent descriptive and inferential statistical analysis. Results The data indicated a longer decoding time and a lower percentage of correct answers for the children from the Research Group. Regarding spelling errors, there was a predominance of arbitrary spelling errors. Conclusion The data showed that children with Developmental Language Disorder tend to have a longer decoding time, greater percentage of errors than their peers and tend to present spelling errors more concentrated in natural orthography.

Performance in decoding and writing of children with Developmental Language Disorder: preliminary data.

to verify the performance of children with Developmental Language Disorder in decoding and writing tests in order to better understand their manifestations and the process of acquiring written language skills. The study subjects were 80 children. The Research Group consisted of 16 children diagnosed with Developmental Language Disorder, 13 males and 3 females, mean age of 7.3. The Control Group counted on 64 subjects paired in gender, age, education and socioeconomic level with the Control Group in a 4:1 ratio. The ability to decode words and pseudowords of both groups was evaluated, measuring the time spent to correctly read words and the percentage of correct answers, also considering the length of the word/pseudoword. The writing evaluation was carried out in the control group, which had its spelling errors analyzed and categorized. All data underwent descriptive and inferential statistical analysis. The data indicated a longer decoding time and a lower percentage of correct answers for the children from the Research Group. Regarding spelling errors, there was a predominance of arbitrary spelling errors. The data showed that children with Developmental Language Disorder tend to have a longer decoding time, greater percentage of errors than their peers and tend to present spelling errors more concentrated in natural orthography.

A Hybrid Algorithms Based on the Aizawa Attractor and Rabbit-Lightweight Cipher for Image Encryption

Social media and networks rely heavily on images. Those images should be distributed in a private manner. Image encryption is therefore one of the most crucial components of cyber security. In the present study, an effective image encryption technique is developed that combines the Rabbit Algorithm, a simple algorithm, with the Attractor of Aizawa, a chaotic map. The lightweight encryption algorithm (Rabbit Algorithm), which is a 3D dynamic system, is made more secure by the Attractor of Aizawa. The process separates color images into blocks by first dividing them into bands of red, green, and blue (RGB). The presented approach generates multiple keys, or sequences, based on the initial parameters and conditions, which are then utilized for encrypting each one of the blocks. The peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE), and structural similarity index measure (SSIM) tests are among performance tests that are used to assess the effectiveness of the approach. With the help of National Institute of Standards and Technology (NIST), the produced keys have been checked for randomness. Additionally, research is being done into how long decryption and encryption take. The encryption approach is particularly secure because of the chaotic attractor's unpredictability. The correlation coefficient, Shannon entropy, and a histogram are used to analyse the encryption technique's performance. The findings demonstrate that the suggested encryption approach dependably operates since it makes use of a mechanism to strengthen security against the attack of known or selected plain-text. The outcomes also demonstrated that both decoding and encoding using the suggested encoding approach require less time. For instance, images 3's decoding time is 0.002000, whereas images 3's encoding time is 0.001200.

Multi-codec rate adaptive point cloud streaming for holographic-type communication

Point cloud videos play a crucial role in immersive applications enabled by holographic-type communication, which has been identified as an important service for 6G and beyond wireless systems and the metaverse. The significant volume of point cloud video demands efficient compression and transmission techniques to support the Quality of Experience (QoE) requirements of real-time immersive applications. A few Point Cloud Compression (PCC) techniques, such as MPEG PCC and Draco, have emerged in recent years, and studies have shown that each technique has its strengths and weaknesses under different system settings. This paper proposes a multi-codec rate adaptive point cloud streaming method to satisfy the QoE requirements of interactive and live applications considering available system resources. The proposed method leverages three common PCC techniques: MPEG V-PCC, MPEG G-PCC, and Draco. The performance of each PCC technique is evaluated under various test conditions, and then estimation models are constructed to predict the bit rate, the decoding time, and the quality of the reconstructed point cloud. Based on the user's quality requirements and available computational and communication resources, the proposed streaming method selects a codec along with appropriate compression parameters that can provide the minimum latency for streaming. Evaluation results demonstrate that the proposed method can provide better QoE than benchmark methods under various bandwidth and computation scenarios.

Corner-to-Center long-range context model for efficient learned image compression

In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel context model has been proposed as an alternative that necessitates only two passes during the decoding phase, thus facilitating efficient image compression in real-world scenarios. However, performance degradation occurs due to its incomplete casual context. To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding. Based on such analysis, we propose the Corner-to-Center transformer-based Context Model (C3M) designed to enhance context and latent predictions and improve rate–distortion performance. Specifically, we leverage the logarithmic-based prediction order to predict more context features from corner to center progressively. In addition, to enlarge the receptive field in the analysis and synthesis transformation, we use the Long-range Crossing Attention Module (LCAM) in the encoder/decoder to capture the long-range semantic information by assigning the different window shapes in different channels. Extensive experimental evaluations show that the proposed method is effective and outperforms the state-of-the-art parallel methods. Finally, according to the subjective analysis, we suggest that improving the detailed representation in transformer-based image compression is a promising direction to be explored.

Tele-proctoring for minimally invasive surgery across Japan: Aninitial step toward a new approach to improving the disparity of surgical care and supporting surgical education.

The aim of this study was to verify the clinical feasibility of tele-proctoring using our ultra-low latency communication system with shared internet access. Connections between two multiple remote locations at various distances were established through the TELEPRO® tele-proctoring system. The server records the latency between the two locations for tele-proctoring using the annotations. Questionnaires were administered to the surgeons, assistants, and medical staff. Respondents rated the quickness and quality of communication in terms of latency and disturbances in the audio, video, and usefulness of the live telestrations with annotation. Seven hospitals tele-proctored with Sapporo Medical University between January 2021 and September 2022. The median latency of annotation between the two locations ranged from 24.5 to 48.5 ms. No major technological problems occurred, such as streaming interruption, loss of video or audio, poor resolution. The video encoding time was 10 ms, and its decoding time was 0.8 ms. The total latency positively correlated with the distance between two locations (R = 0.55, p < 0.01). The quality of communication regarding latency, disturbance, and surgical education with intraoperative annotative instructions showed similar trends, with perfectly fine being the most common response. No significant differences in surgical quality, educational effect, or social impact were observed between the latency ≥30 and <30 ms groups for whether the size of latency affects surgical education. The feasibility of the tele-proctoring system is expected to be a sustainable approach to help education for young surgeons and surgical supports in rural areas, thereby reducing disparities in health care.

ES-net: An Integration Model Based on Encoder–Decoder and Siamese Time Series Difference Network for Grade Monitoring of Zinc Tailings and Concentrate

In froth flotation, the tailings grade and concentrate grade are the two key performance indexes. At present, the monitoring models of these two key grades mostly use the froth image or video from a flotation cell. However, flotation cells are closely related and coupled seriously. It is difficult to use a froth image or video from a flotation cell to represent the concentrate or tailings grade. Therefore, an encoder-decoder and Siamese time series network (ES-net) is proposed. First, an encoder-decoder (ED) model is designed to predict target grade (i.e., the zinc tailings or concentrate grade) by the video feature sequence of the first rougher and the measured target grade sequence. Meanwhile, a Siamese time series and difference network (STS-D net) is constructed to predict the target grade by the video feature sequences of target flotation cell (i.e., the last scavenger or cleaner) at current and previous moments and the previously measured target grade. After that, a multi-task learning strategy is proposed to integrate the ED model and STS-D net. Experiments show that the proposed ES-net can effectively integrate multiple froth visual features from different flotation cells and obtain more accurate concentrate and tailings grades than the existing models.

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding.

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

ELDPC: An Efficient LDPC Coding Scheme for Phase-Change Memory

Low read latency, long lifetime, and high storage density have all been demonstrated in phase-change memory (PCM), making it an attractive contender for main memory. However, due to resistance drift per cell caused by long-term storage, data reliability becomes a major challenge. Low-density parity-check (LDPC) codes with improved error correction capability can be used in PCM to reduce bit error rates and thus improve data reliability. More interestingly, when the raw bit error rates (RBER) of various pages in PCM is compared at the same storage time, a considerable gap appears, resulting in high sensing and decoding latency. We propose eLDPC, an efficient LDPC coding scheme for reducing sensing and decoding latency, in this paper. We start with a preliminary experiment, which reveals that there is a significant variation in resistance drifts between adjacent distributions, resulting in a large RBER gap for different pages. Then, using a submatrix of the parity-check matrix to shorten the codeword length, eLDPC is inspired to encode pages with lower RBER. The original bit sequence is separated into even bit sequence (EBS) and odd bit sequence (OBS) for pages with higher RBER. eLDPC is used to encode EBS and OBS independently. By utilizing optimized soft information, EBS and OBS are eLDPC decoded. eLDPC can significantly improve error correction capability of LDPC hard decoding, effectively eliminating soft decoding processes and lowering decoding latency. The results of simulations show that eLDPC can greatly decrease decoding iterations and time.

Are single-peaked tuning curves tuned for speed rather than accuracy?

According to the efficient coding hypothesis, sensory neurons are adapted to provide maximal information about the environment, given some biophysical constraints. In early visual areas, stimulus-induced modulations of neural activity (or tunings) are predominantly single-peaked. However, periodic tuning, as exhibited by grid cells, has been linked to a significant increase in decoding performance. Does this imply that the tuning curves in early visual areas are sub-optimal? We argue that the time scale at which neurons encode information is imperative to understand the advantages of single-peaked and periodic tuning curves, respectively. Here, we show that the possibility of catastrophic (large) errors creates a trade-off between decoding time and decoding ability. We investigate how decoding time and stimulus dimensionality affect the optimal shape of tuning curves for removing catastrophic errors. In particular, we focus on the spatial periods of the tuning curves for a class of circular tuning curves. We show an overall trend for minimal decoding time to increase with increasing Fisher information, implying a trade-off between accuracy and speed. This trade-off is reinforced whenever the stimulus dimensionality is high, or there is ongoing activity. Thus, given constraints on processing speed, we present normative arguments for the existence of the single-peaked tuning organization observed in early visual areas.