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Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

Brain‐computer interfaces (BCIs) enable communication between individuals and computers or other assistive devices by decoding brain activity, thereby reconstructing speech and motor functions for patients with neurological disorders. This study presents a high‐resolution micro‐electrocorticography (µECoG) BCI based on a flexible, high‐density µECoG electrode array, capable of chronically stable and real‐time motor decoding. Leveraging micro‐nano manufacturing technology, the µECoG BCI achieves a 64‐fold increase in electrode density compared to conventional clinical electrode arrays, enhancing spatial resolution while featuring scalability. Over a 203‐day in vivo experiment, high‐resolution µECoG carrying fine spatial specificity information demonstrated the potential to improve decoding performance while reduce implanted devices size. These advancements provide a pathway to overcome the limitations of conventional ECoG BCIs. During awake surgery, the µECoG BCI enabled game control after 7 min of model training. Furthermore, during practice of 19.87 h, the participant achieved cursor control with a bit rate of 1.13 bits per second (BPS) under full volitional control, and the bit rate reached up to 4.15 BPS with enhanced user interface. These results show that the µECoG BCI achieves comparable performance to intracortical electroencephalographic (iEEG) BCIs without intracortical invasiveness, marking a breakthrough in the clinical feasibility of flexible BCIs. A high‐resolution micro‐electrocorticographic (µECoG) brain‐computer interface (BCI) for real‐time motor decoding is reported. The application of flexible, scalable µECoG electrode arrays overcomes the insufficient spatial resolution in conventional ECoG BCIs. The real‐time motor and motor imagery decoding achieved in the long‐term in vivo experiment and clinical practices demonstrates the practical value of flexible µECoG BCIs.

The transmission of sensory information through the visual system takes time. As a result of these delays, the visual information available to the brain always lags behind the timing of events in the present moment. Compensating for these delays is crucial for functioning within dynamic environments, since interacting with a moving object (e.g., catching a ball) requires real-time localization of the object. One way the brain might achieve this is via prediction of anticipated events. Using time-resolved decoding of electroencephalographic (EEG) data, we demonstrate that the visual system represents the anticipated future position of a moving object, showing that predictive mechanisms activate the same neural representations as afferent sensory input. Importantly, this activation is evident before sensory input corresponding to the stimulus position is able to arrive. Finally, we demonstrate that, when predicted events do not eventuate, sensory information arrives too late to prevent the visual system from representing what was expected but never presented. Taken together, we demonstrate how the visual system can implement predictive mechanisms to preactivate sensory representations, and argue that this might allow it to compensate for its own temporal constraints, allowing us to interact with dynamic visual environments in real time.

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.

•Value-related neural patterns differ between risky and conservative decisions.•Neurocomputation in risky decisions may balance risk and benefit values.•Emotions affect conservative decisions but have a weaker influence on risky ones. The substantial variability in people’s risky decision-making constitutes a compelling and interesting topic. It is particularly fascinating to note that even when confronted with identical circumstances, the same individual tends to exhibit variability in their risk decisions, oscillating between a propensity for heightened risk-taking and a more cautious approach. The current study investigated human computational neural mechanisms of risky and conservative decision-making in sequential risk-taking tasks through integrative analysis of dynamic-updating model parameters and EEG signatures of decision processes. The model revealed that, during risky decision-making, subjective benefit values exerted a stronger influence, whereas subjective risk values dominated conservative decisions. Emotional factors, particularly regret-related emotion, primarily modulated conservative decisions rather than risky decisions. By applying time-resolved multivariate pattern analyses to EEG data and identifying the peak decoding accuracy as a marker of stage completion, we dissociated valuation from the selection stage in decision-making. The peak decoding accuracy during the valuation stage demonstrated higher and more stable in risky decision-making compared to conservative decision-making. Further analysis suggested that peak decoding accuracy in risky decision-making may reflect a relatively balanced consideration between risk and benefit values. Notably, emotional factors had less impact on the selection stage of risky decisions, but significantly affected the selection stage of conservative decisions. These findings elucidate the adaptability and dynamic architecture of neurocomputation across risky and conservative decisions.

A number of recent studies have put human subjects in true social interactions, with the aim of better identifying the psychophysiological processes underlying social cognition. Interestingly, this emerging Neuroscience of Social Interactions (NSI) field brings up challenges which resemble important ones in the field of Brain-Computer Interfaces (BCI). Importantly, these challenges go beyond common objectives such as the eventual use of BCI and NSI protocols in the clinical domain or common interests pertaining to the use of online neurophysiological techniques and algorithms. Common fundamental challenges are now apparent and one can argue that a crucial one is to develop computational models of brain processes relevant to human interactions with an adaptive agent, whether human or artificial. Coupled with neuroimaging data, such models have proved promising in revealing the neural basis and mental processes behind social interactions. Similar models could help BCI to move from well-performing but offline static machines to reliable online adaptive agents. This emphasizes a social perspective to BCI, which is not limited to a computational challenge but extends to all questions that arise when studying the brain in interaction with its environment.

•Does stimulus timing impact the processing of predicted visual features?.•We evaluated if expectation suppression effects are modulated by temporal predictability.•Expectation suppression was robust in both visual ERFs and feature decoding accuracy.•Visual responses to predictable stimuli are greater for predictable vs. unpredictable timing.•Sensory evidence is given less weight when timing is uncertain. Contextual information triggers predictions about the content (“what”) of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability (“when”) may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information (“what”) is affected by temporal predictability (“when”). Participants (N = 16) were presented with four consecutive Gabor patches (entrainers) with constant spatial frequency but with variable orientation and temporal onset. A fifth target Gabor was presented after a longer delay and with higher or lower spatial frequency that participants had to judge. We compared the neural responses to entrainers where the Gabor orientation could, or could not be temporally predicted along the entrainer sequence, and with inter-entrainer timing that was constant (predictable), or variable (unpredictable). We observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.

Main objective of the proposed work is to develop an approach for video coding based on Fractal coding using the weighted finite automata (WFA). The proposed work only focuses on reducing the encoding time as this is the basic limitation why the Fractal coding not becomes the practical reality. WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image based on the idea of fractal that the image has self-similarity in itself. The plane WFA (applied on every frame), and Plane FC (applied on every frame) coding approaches are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, Traffic, Paris, Bus, Akiyo, Mobile, Suzie etc. and on the recorded video, namely, Geometry and Circle. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio, compression percentage, bits per pixel and Peak Signal to Noise Ratio (PSNR). Though the initial number of states is 256 for every frame of all the types of videos, but we got the different number of states for different frames and it is quite obvious due to minimality of constructed WFA for respective frame. Based on the obtained results, it is observed that the number of states is more in videos namely, Traffic, Bus, Paris, Mobile, and Akiyo, therefore the reconstructed video quality is good in comparison with other videos namely, Circle, Suzie, and Geometry.

Given an observed low frame rate video, video frame interpolation (VFI) aims to generate a high frame rate video, which has smooth video frames with higher frames per second (FPS). Most existing VFI methods often focus on generating one frame at a specific timestep, e.g., 0.5, between every two frames, thus lacking the flexibility to increase the video’s FPS by an arbitrary scale, e.g., 3. To better address this issue, in this paper, we propose an arbitrary timestep video frame interpolation (ATVFI) network with time-dependent decoding. Generally, the proposed ATVFI is an encoder–decoder architecture, where the interpolation timestep is an extra input added to the decoder network; this enables ATVFI to interpolate frames at arbitrary timesteps between input frames and to increase the video’s FPS at any given scale. Moreover, we propose a data augmentation method, i.e., multi-width window sampling, where video frames can be split into training samples with multiple window widths, to better leverage training frames for arbitrary timestep interpolation. Extensive experiments were conducted to demonstrate the superiority of our model over existing baseline models on several testing datasets. Specifically, our model trained on the GoPro training set achieved 32.50 on the PSNR metric on the commonly used Vimeo90k testing set.

Low-power asymmetric multicore processors (AMPs) have attracted considerable attention due to their appealing performance/power ratio for energy-constrained environments. However, these processors pose a significant programming challenge due to the integration of cores with different performance capabilities, asking for an asymmetry-aware scheduling solution that carefully distributes the workload. The recent HEVC standard, which offers several high-level parallelization strategies, is an important application that can benefit from an implementation tailored for the low-power AMPs present in many current mobile or handheld devices. In this scenario, we present an architecture-aware implementation of an HEVC decoder that embeds a criticality-aware scheduling strategy tuned for a Samsung Exynos 5422 System-on-Chip furnished with an ARM big.LITTLE AMP. The performance and energy efficiency of our solution are further enhanced by exploiting the NEON vector engine available in the ARM big.LITTLE architecture. Our experimental results expose a 1080p real-time HEVC decoding at 24 frames/s and a reduction of energy consumption over 20 %.