Temporal Information Processing Research Articles

Evaluation and Time Constraint: Impact of Time Processing on Mathematical Performance

This study investigated the impact of a temporal assessment context on children’s mathematical performance and anxiety. Children aged 7- to 9-years-old were given mathematics tasks under three different assessment conditions: no time constraint (NoT), time constraint with an invisible timer (IT), and time constraint with visible timer (VT). Results showed that children performed worse under the time constraint condition when the timer was not visible to them. This supports the resource-based theory of time perception, which suggests that the processing of temporal information is a demanding cognitive task that consumes limited attention resources. Of greater interest, the use of a visible timer improved children’s cognitive efficiency without compromising performance compared to the unconstrained time condition. Additionally, the study found that anxiety levels were lower in the situation with a time constraint with a visible timer compared to the two other assessment contexts. These results suggest that a situation with a pre-established time constraint where children can manage their time could alleviate their anxiety and better cater to their needs during the assessment. Overall, these results underline the benefits of a visible timer in providing an organizational framework for children and helping them process time, thus avoiding draining their attentional resources.

Writing direction influences the spatial representations of past- and future-tense forms: Evidence from eye tracking.

The present study tests the hypothesis that the directionality of reading habits (left-to-right or right-to-left) impacts individuals' representation of nonspatial events. Using the blank screen paradigm, we examine whether eye movements reflect culture-specific spatial biases in processing temporal information, specifically, grammatical tense in Russian and Hebrew. Sixty-two native speakers of Russian (a language with a left-to-right reading and writing system) and 62 native speakers of Hebrew (a language with a right-to-left reading and writing system) listened to verbs in the past or future tense while their spontaneous gaze positions were recorded. Following the verb, a visual spatial probe appeared in one of the five locations of the screen, and participants responded manually to indicate its position. While participants' response latencies to the spatial probe revealed no significant effects, their gaze positions along the horizontal axis for past- and future-tensed verbs aligned with the reading and writing direction in their language. These results provide novel evidence that eye movements during auditory processing of grammatical tense are influenced by culturally specific reading and writing conventions, shifting leftward or rightward on the horizontal plane depending on the stimuli's time reference (past or future) and the participants' language (Russian or Hebrew). This spatial bias indicates a common underlying cognitive mechanism that uses spatial dimensions to represent temporal constructs.

Sunspot Group Detection and Classification by Dual Stream Convolutional Neural Network method

Abstract The automatic detection and analysis of sunspots play a crucial role in understanding solar dynamics and predicting space weather events. This paper proposes a novel method for sunspot group detection and classification called the Dual Stream Convolutional Neural Network with Attention Mechanism (DSCNN-AM) . The network consists of two parallel streams each processing different input data allowing for joint processing of spatial and temporal information while classifying sunspots. It takes in the white light images as well as the corresponding magnetic images that reveal both the optical and magnetic features of sunspots. The extracted features are then fused and processed by fully connected layers to perform detection and classification.The attention mechanism is further integrated to address the ``edge dimming" problem which improves the model's ability to handle sunspots near the edge of the solar disk. The network is trained and tested on the SOLAR-STORM1 dataset. The results demonstrate that the DSCNN with the attention mechanism achieves superior performance compared to existing methods, with a total accuracy exceeding 90%.

Keywords:
Sunspots, Mount Wilson, DSCNN, Deep Learning

Temporal context modulates cross-modality time discrimination: Electrophysiological evidence for supramodal temporal representation

Although the peripheral nervous system lacks a dedicated receptor, the brain processes temporal information through different sensory channels. A critical question is whether temporal information from different sensory modalities at different times forms modality-specific representations or is integrated into a common representation in a supramodal manner. Behavioral studies on temporal memory mixing and the central tendency effect have provided evidence for supramodal temporal representations. We aimed to provide electrophysiological evidence for this proposal by employing a cross-modality time discrimination task combined with electroencephalogram (EEG) recordings. The task maintained a fixed auditory standard duration, whereas the visual comparison duration was randomly selected from the short and long ranges, creating two different audio–visual temporal contexts. The behavioral results showed that the point of subjective equality (PSE) in the short context was significantly lower than that in the long context. The EEG results revealed that the amplitude of the contingent negative variation (CNV) in the short context was significantly higher (more negative) than in the long context in the early stage, while it was lower (more positive) in the later stage. These results suggest that the audiovisual temporal context is integrated with the auditory standard duration to generate a subjective time criterion. Compared with the long context, the subjective time criterion in the short context was shorter, resulting in earlier decision-making and a preceding decrease in CNV. Our study provides electrophysiological evidence that temporal information from different modalities inputted into the brain at different times can form a supramodal temporal representation.

Toward an endophenotype for ADHD: Exploring the duration mismatch negativity in drug-free children with ADHD

Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent disorders in children that is considered to affect early stages of information processes. Inefficient processing of temporal information, which is a vital auditory processing skill suggests itself as a potential candidate for investigating ADHD deficits. The Research Domain Criteria (RDoC), a neuroscience-based research framework, has been introduced to study mental illness without relying on pre-established diagnostic categories. In this regard, Mismatch Negativity (MMN) has been considered an ideal electrophysiological marker for investigating ADHD deficits. This study investigates alterations in the amplitude and latency of the MMN component in response to changes in the duration and Inter-Stimulus Interval (ISI) of basic sound stimuli within an oddball task. The MMN paradigm was employed to examine duration deviations in ADHD (n = 25, 84% male, mean age: 7.3 years, SD = 2.01) compared to Control group of typically developing (TD) children (n = 25, 72% male, mean age: 7.2 years, SD = 1.92). Participants with ADHD were introduced from an accredited psychiatrist. TD children were recruited from social media and online forms. Both groups were matched in terms of gender, age and IQ. The psychological tests conducted in this study included Conners’ Parent Rating Scale (CPRS), Gilliam Autism Rating Scale|Third Edition (Gars3), Sensory profile questionnaire and Edinburgh Handedness inventory. Our findings revealed reduced MMN amplitudes in response to two blocks of duration and ISI-based deviations in ADHD children. To elaborate in greater detail, at Fz, in Duration and ISI block, respectively, the ADHD group showed an amplitude of −1.2097 ± 0.2938 and −0.8553 ± 0.4423, while the normal group showed an amplitude of −1.8325 ± 0.3689 and −2.0855 ± 0.3802. Additionally, at Cz, the ADHD group exhibited a shorter amplitude (-1.2515 ± 0.3261 and −0.9367 ± 0.3432) compared to the normal group (−2.1319 ± 0.4445 and −2.7561 ± 0.4883), in the duration and ISI blocks, respectively. Furthermore, children with ADHD display longer MMN latencies in both experimental blocks, suggesting atypical responses. To provide more detail, at Fz, the ADHD group displayed MMN latencies of 239.68 ± 5.059 and 226.88 ± 4.885 in the Duration and ISI blocks, respectively, whereas the normal group showed MMN latencies of 228.56 ± 6.584 and 213.56 ± 4.153. Similarly, at Cz, the ADHD group exhibited longer MMN latencies (234.40 ± 5.741 and 231.44 ± 5.464) compared to the normal group (227.52 ± 6.710 and 218.00 ± 5.261) in the Duration and ISI blocks, respectively. Our findings were interpreted in the context of the internal clock model, which involves the pace of an internal pacemaker regulated by dopamine (DA) levels. The convergence of MMN and auditory timing abnormalities within the RDoC framework suggests their potential as endophenotypes for ADHD, highlighting the significance of sensory processing in understanding the disorder.

BOLD fMRI responses to amplitude-modulated sounds across age in adult listeners

Abstract Age-related alterations in the auditory system have been suggested to affect the processing of temporal envelope amplitude modulations (AM) at different levels of the auditory hierarchy, yet few studies have used functional magnetic resonance imaging (fMRI) to study this noninvasively in humans with high spatial resolution. In this study, we utilized sparse-sampling fMRI at 3 Tesla (3T) to investigate regional blood oxygenation level-dependent (BOLD) responses to AM noise stimuli in 65 individuals ranging in age from 19 to 77 years. We contrasted BOLD responses to AM noise stimuli modulated at 4 Hz or 80 Hz with responses to unmodulated stimuli. This allowed us to derive functional measures of regional neural sensitivity to the imposed AM. Compared with unmodulated noise, slowly varying 4 Hz AM noise stimuli elicited significantly greater BOLD responses in the left and right auditory cortex along the Heschl’s gyrus (HG). BOLD responses to the 80 Hz AM stimuli were significantly greater than responses to unmodulated stimuli in putatively primary auditory cortical regions in the lateral HG. BOLD responses to 4 Hz AM stimuli were significantly greater in magnitude than responses to 80 Hz AM stimuli in auditory cortical regions. We find no discernible effects of age on the functional recruitment of the auditory cortex by AM stimuli. While the results affirm the involvement of the auditory cortex in processing temporal envelope rate information, they provide no support for age-related effects on these measures. We discuss potential caveats in assessing age-related changes in responses to AM stimuli in the auditory pathway.

Learning sequence attractors in recurrent networks with hidden neurons

The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store predefined pattern sequences and retrieve them robustly. We show that to store arbitrary pattern sequences, it is necessary for the network to include hidden neurons even though their role in displaying sequence memories is indirect. We develop a local learning algorithm to learn sequence attractors in the networks with hidden neurons. The algorithm is proven to converge and lead to sequence attractors. We demonstrate that the network model can store and retrieve sequences robustly on synthetic and real-world datasets. We hope that this study provides new insights in understanding sequence memory and temporal information processing in the brain.

Duration, numerosity and length processing in healthy ageing and Parkinson’s disease

People constantly process temporal, numerical, and length information in everyday activities and interactions with the environment. However, it is unclear whether quantity perception changes during ageing. Previous studies have provided heterogeneous results, sometimes showing an age-related effect on a particular quantity, and other times reporting no differences between young and elderly samples. However, three dimensions were never compared within the same study. Here, we conducted two experiments with the aim of investigating the processing of duration, numerosity and length in both healthy and pathological ageing. The experimental paradigm consisted of three bisection tasks in which participants were asked to judge whether the presented stimulus (i.e. a time interval, a group of dots, or a line) was more similar to the short/few or long/many standards. The first study recruited healthy young and elderly participants, while the second recruited healthy elderly participants and patients with Parkinson’s disease, a clinical condition commonly associated with temporal impairments. The results of both experiments showed that discrimination precision differed between domains in all groups, with higher precision in the numerosity task and lower sensitivity in judging duration. Furthermore, while discrimination abilities were affected in healthy elderly and, even more so, in Parkinson’s disease group, no domain-specific impairments emerged. According to our research, reduced discrimination precision might be explained by an alteration of a single system for all quantities or by an age-related general cognitive decline.

ALI: The adaptive levels of interval method for processing laser speckle images with superior activity extraction and discrimination capabilities

This paper describes the development of a new laser speckle imaging method for extracting speckle patterns' temporal activity information with superior extraction and discrimination capabilities. It is based on a novel mechanism which uses the speckle images, in an unconventional manner, through adapting the levels of their interval, to deal with the scattering medium that contains regions with different activity scales. This mechanism overcomes the discrimination inability of the other processing methods when they deal with such a medium and speeds up the process of activity map calculation. The developed speckle processing method was evaluated using numerical and graphical evaluation tests, the results showing the efficacy of using the adapting mechanism for the levels of interval. Experimental tests were also undertaken on the proposed method to validate its ability with real speckle images, obtained through a series of experiments performed using a laser speckle imaging system. The experimental results show the high discrimination and activity extraction capabilities of this method against the speckle processing methods and validate its ability to deal with multi-spectral real speckle images. The developed method achieved a faster processing performance in comparison with traditional methods when processing temporal information for the speckle images and calculating the map of their activity.

Switching Dynamics in Anti‐Ferroelectric Transistor for Multimodal Reservoir Computing

AbstractSpatial‐temporal time series analysis and forecasting are crucial for understanding dynamic systems and making informed decisions. Recurrent neural networks (RNNs) have paved the way for reservoir computing (RC), a method enabling effective temporal information processing at low training costs. While software‐based RC performs well, physical RC systems face challenges like slow processing speed and limited state richness, leading to high hardware costs. This study introduces an innovative approach, i.e., the antiferroelectric field effect transistor‐based RC (AFeFET‐based RC) system for efficient temporal data processing. By exploiting the fading memory property inherent in hafnium oxide‐based antiferroelectric material, this system demonstrates promise for physical RC implementation. Moreover, it leverages the light sensitivity of 2D molybdenum disulfide (MoS2) channels for controllable temporal dynamics under electrical and optical stimuli. This dual‐mode modulation significantly enriches the reservoir state, boosting overall system performance. Experimental tests on standard benchmarking tasks using the AFeFET‐based RC system yielded impressive accuracy results (95.4%) in spoken‐digit recognition and a remarkable normalized root mean square error (NRMSE) of 0.015 in Mackey–Glass time series prediction.

Construction and improvement of English vocabulary learning model integrating spiking neural network and convolutional long short-term memory algorithm.

To help non-native English speakers quickly master English vocabulary, and improve reading, writing, listening and speaking skills, and communication skills, this study designs, constructs, and improves an English vocabulary learning model that integrates Spiking Neural Network (SNN) and Convolutional Long Short-Term Memory (Conv LSTM) algorithms. The fusion of SNN and Conv LSTM algorithm can fully utilize the advantages of SNN in processing temporal information and Conv LSTM in sequence data modeling, and implement a fusion model that performs well in English vocabulary learning. By adding information transfer and interaction modules, the feature learning and the timing information processing are optimized to improve the vocabulary learning ability of the model in different text contents. The training set used in this study is an open data set from the WordNet and Oxford English Corpus data corpora. The model is presented as a computer program and applied to an English learning application program, an online vocabulary learning platform, or a language education software. The experiment will use the open data set to generate a test set with text volume ranging from 100 to 4000. The performance indicators of the proposed fusion model are compared with those of five traditional models and applied to the latest vocabulary exercises. From the perspective of learners, 10 kinds of model accuracy, loss, polysemy processing accuracy, training time, syntactic structure capturing accuracy, vocabulary coverage, F1-score, context understanding accuracy, word sense disambiguation accuracy, and word order relation processing accuracy are considered. The experimental results reveal that the performance of the fusion model is better under different text sizes. In the range of 100-400 text volume, the accuracy is 0.75-0.77, the loss is less than 0.45, the F1-score is greater than 0.75, the training time is within 300s, and the other performance indicators are more than 65%; In the range of 500-1000 text volume, the accuracy is 0.81-0.83, the loss is not more than 0.40, the F1-score is not less than 0.78, the training time is within 400s, and the other performance indicators are above 70%; In the range of 1500-3000 text volume, the accuracy is 0.82-0.84, the loss is less than 0.28, the F1-score is not less than 0.78, the training time is within 600s, and the remaining performance indicators are higher than 70%. The fusion model can adapt to various types of questions in practical application. After the evaluation of professional teachers, the average scores of the choice, filling-in-the-blank, spelling, matching, exercises, and synonyms are 85.72, 89.45, 80.31, 92.15, 87.62, and 78.94, which are much higher than other traditional models. This shows that as text volume increases, the performance of the fusion model is gradually improved, indicating higher accuracy and lower loss. At the same time, in practical application, the fusion model proposed in this study has a good effect on English learning tasks and offers greater benefits for people unfamiliar with English vocabulary structure, grammar, and question types. This study aims to provide efficient and accurate natural language processing tools to help non-native English speakers understand and apply language more easily, and improve English vocabulary learning and comprehension.

Tunable Neuromorphic Switching Dynamics via Porosity Control in Mesoporous Silica Diffusive Memristors.

In response to the growing need for efficient processing of temporal information, neuromorphic computing systems are placing increased emphasis on the switching dynamics of memristors. While the switching dynamics can be regulated by the properties of input signals, the ability of controlling it via electrolyte properties of a memristor is essential to further enrich the switching states and improve data processing capability. This study presents the synthesis of mesoporous silica (mSiO2) films using a sol-gel process, which enables the creation of films with controllable porosities. These films can serve as electrolyte layers in the diffusive memristors and lead to tunable neuromorphic switching dynamics. The mSiO2 memristors demonstrate short-term plasticity, which is essential for temporal signal processing. As porosity increases, discernible changes in operating currents, facilitation ratios, and relaxation times are observed. The underlying mechanism of such systematic control was investigated and attributed to the modulation of hydrogen-bonded networks within the porous structure of the silica layer, which significantly influences both anodic oxidation and ion migration processes during switching events. The result of this work presents mesoporous silica as a unique platform for precise control of neuromorphic switching dynamics in diffusive memristors.

Neuromorphic auditory classification based on a single dynamical electrochemical memristor

Designing compact computing hardware and systems is highly desired for resource-restricted edge computing applications. Utilizing the rich dynamics in a physical device for computing is a unique approach in creating complex functionalities with miniaturized footprint. In this work, we developed a dynamical electrochemical memristor from a static memristor by replacing the gate material. The dynamical device possessed short-term fading dynamics and exhibited distinct frequency-dependent responses to varying input signals, enabling its use as a single device-based frequency classifier. Simulation showed that the device responses to different frequency components in a mixed-frequency signal were additive with nonlinear attenuation at higher frequency, providing a guideline in designing the system to process complex signals. We used a rate-coding scheme to convert real world auditory recordings into fixed amplitude spike trains to decouple amplitude-based information and frequency-based information and was able to demonstrate auditory classification of different animals. The work provides a new building block for temporal information processing.

Temporal and Spatial Information Elicit Different Power and Connectivity Profiles during Working Memory Maintenance.

Working memory (WM) is the cognitive ability to store and manipulate information necessary for ongoing tasks. Although frontoparietal areas are involved in the retention of visually presented information, oscillatory neural activity differs for temporal and spatial WM processing. In this study, we corroborated previous findings describing the modulation of neural oscillations and expanded our investigation to the network organization underlying the cognitive processing of temporal and spatial information. We utilized MEG recordings during a Sternberg visual WM task. The spectral oscillatory activity in the maintenance phase revealed increased frontal theta (4-8 Hz) and parietal beta (13-30 Hz) in the temporal condition. Source level coherence analysis delineated the prominent role of parietal areas in all frequency bands during the maintenance of temporal information, whereas frontal and central areas showed major contributions in theta and beta ranges during the maintenance of spatial information. Our study revealed distinct spectral profiles of neural oscillations for separate cognitive subdomains of WM processing. The delineation of specific functional networks might have important implications for clinical applications, enabling the development of stimulation protocols targeting cognitive disabilities associated with WM impairments.

Training multi-layer spiking neural networks with plastic synaptic weights and delays.

Spiking neural networks are usually considered as the third generation of neural networks, which hold the potential of ultra-low power consumption on corresponding hardware platforms and are very suitable for temporal information processing. However, how to efficiently train the spiking neural networks remains an open question, and most existing learning methods only consider the plasticity of synaptic weights. In this paper, we proposed a new supervised learning algorithm for multiple-layer spiking neural networks based on the typical SpikeProp method. In the proposed method, both the synaptic weights and delays are considered as adjustable parameters to improve both the biological plausibility and the learning performance. In addition, the proposed method inherits the advantages of SpikeProp, which can make full use of the temporal information of spikes. Various experiments are conducted to verify the performance of the proposed method, and the results demonstrate that the proposed method achieves a competitive learning performance compared with the existing related works. Finally, the differences between the proposed method and the existing mainstream multi-layer training algorithms are discussed.

Oxide‐Based Electrolyte‐Gated Transistors with Stable and Tunable Relaxation Responses for Deep Time‐Delayed Reservoir Computing

AbstractTime‐delayed reservoir computing with marked strengths of friendly hardware implementation and low training cost is regarded as a promising solution to realize time and energy‐efficient time series information processing and thus receives growing attention. However, achieving a sufficient number of reproducible reservoir states remains a significant challenge, which severely limits its computing performance. Here, an electric‐double‐layer‐coupled oxide‐based electrolyte‐gated transistor with a shared gate and varying channel lengths is developed to construct a deep time‐delayed reservoir computing system. A variety of short‐term synaptic responses related to inherent ion‐electron‐coupled dynamics at the electrolyte/channel interface are demonstrated, reflecting a flexibly regulable channel current. Different stable and tunable relaxation responses corresponding to varying channel lengths are obtained to enrich reservoir states combined with virtual nodes ways. The spoken‐digit classification and Hénon map prediction tasks are implemented with high accuracy (≈92.2%) and ultralow normalized root mean square error (≈0.013), respectively, validating the significant improvement of the computing performance by introducing additional relaxation responses. This work opens a promising pathway in exploiting oxide‐based electrolyte‐gated transistors for realizing temporal information processing hardware systems.

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics

It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Temporal Information Processing in the Cerebellum and Basal Ganglia.

Temporal information processing in the range of a few hundred milliseconds to seconds involves the cerebellum and basal ganglia. In this chapter, we present recent studies on nonhuman primates. In the studies presented in the first half of the chapter, monkeys were trained to make eye movements when a certain amount of time had elapsed since the onset of the visual cue (time production task). The animals had to report time lapses ranging from several hundred milliseconds to a few seconds based on the color of the fixation point. In this task, the saccade latency varied with the time length to be measured and showedstochastic variability from one trial to the other. Trial-to-trial variability under the same conditions correlated well with pupil diameter and the preparatory activity in the deep cerebellar nuclei and the motor thalamus. Inactivation of these brain regions delayed saccades when asked to report subsecond intervals. These results suggestthat the internal state, which changes with each trial, may cause fluctuations in cerebellar neuronal activity, thereby producing variations in self-timing. When measuring different time intervals, the preparatory activity in the cerebellum always begins approximately 500ms before movements, regardless of the length of the time interval being measured. However, the preparatory activity in the striatum persists throughout the mandatory delay period, which can be up to 2s, with different rate of increasing activity. Furthermore, in the striatum, the visual response and low-frequency oscillatory activity immediately before time measurement were altered by the length of the intended time interval. These results indicate that the state of the network, including the striatum, changes with the intended timing, which lead to different time courses of preparatory activity. Thus, the basal ganglia appear to be responsible for measuring time in the range of several hundred milliseconds to seconds, whereas the cerebellum is responsible for regulating self-timing variability in the subsecond range. The second half of this chapter presents studies related to periodic timing. During eye movements synchronized with alternating targets at regular intervals, different neurons in the cerebellar nuclei exhibit activity related to movement timing, predicted stimulus timing, and the temporal error of synchronization. Among these, the activity associated with target appearance is particularly enhanced during synchronized movements and may represent an internal model of the temporal structure of stimulus sequence. We also considered neural mechanism underlying the perception of periodic timing in the absence of movement. During perception of rhythm, we predict the timing of the next stimulus and focus our attention on that moment. In the missing oddball paradigm, the subjects had to detect the omission of a regularly repeated stimulus. When employed in humans, the results show that the fastest temporal limit for predicting each stimulus timing is about 0.25s (4Hz). In monkeys performing this task, neurons in the cerebellar nuclei, striatum, and motor thalamus exhibit periodic activity, with different time courses depending on the brain region. Since electrical stimulation or inactivation of recording sites changes the reaction time to stimulus omission, these neuronal activities must be involved in periodic temporal processing. Future research is needed to elucidate the mechanism of rhythm perception, which appears to be processed by both cortico-cerebellar and cortico-basal ganglia pathways.

Fast Non-Rigid Radiance Fields from Monocularized Data.

The reconstruction and novel view synthesis of dynamic scenes recently gained increased attention. As reconstruction from large-scale multi-view data involves immense memory and computational requirements, recent benchmark datasets provide collections of single monocular views per timestamp sampled from multiple (virtual) cameras. We refer to this form of inputs as monocularized data. Existing work shows impressive results for synthetic setups and forward-facing real-world data, but is often limited in the training speed and angular range for generating novel views. This paper addresses these limitations and proposes a new method for full 360° inward-facing novel view synthesis of non-rigidly deforming scenes. At the core of our method are: 1) An efficient deformation module that decouples the processing of spatial and temporal information for accelerated training and inference; and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field. In addition to existing synthetic monocularized data, we systematically analyze the performance on real-world inward-facing scenes using a newly recorded challenging dataset sampled from a synchronized large-scale multi-view rig. In both cases, our method is significantly faster than previous methods, converging in less than 7 minutes and achieving real-time framerates at 1K resolution, while obtaining a higher visual accuracy for generated novel views. Our code and dataset are available online: https://github.com/MoritzKappel/MoNeRF.

Dopamine precursor depletion affects performance and confidence judgements when events are timed from an explicit, but not an implicit onset

Dopamine affects processing of temporal information, but most previous work has tested its role in prospective tasks, where participants know in advance when the event to be timed starts. However, we are often exposed to events whose onset we do not know in advance. We can evaluate their duration after they have elapsed, but mechanisms underlying this ability are still elusive. Here we contrasted effects of acute phenylalanine and tyrosine depletion (APTD) on both forms of timing in healthy volunteers, in a within-subject, placebo-controlled design. Participants were presented with a disc moving around a circular path and asked to reproduce the duration of one full revolution and to judge their confidence in performance. The onset of the revolution was either known in advance (explicit onset) or revealed only at the end of the trial (implicit onset). We found that APTD shortened reproduced durations in the explicit onset task but had no effect on temporal performance in the implicit onset task. This dissociation is corroborated by effects of APTD on confidence judgements in the explicit task only. Our findings suggest that dopamine has a specific role in prospective encoding of temporal intervals, rather than the processing of temporal information in general.