No abstract

Synchronizing neural processes, mental activities, and social interactions is considered to be fundamental for the creation of temporal order on the personal and interpersonal level. Several different types of synchronization are distinguished, and for each of them examples are given: self-organized synchronizations on the neural level giving rise to pre-semantically defined time windows of some tens of milliseconds and of approximately 3 s; time windows that are created by synchronizing different neural representations, as for instance in aesthetic appreciations or moral judgments; and synchronization of biological rhythms with geophysical cycles, like the circadian clock with the 24-hr rhythm of day and night. For the latter type of synchronization, an experiment is described that shows the importance of social interactions for sharing or avoiding common time. In a group study with four subjects being completely isolated together for 3 weeks from the external world, social interactions resulted both in intra- and interindividual circadian synchronization and desynchronization. A unique phenomenon in circadian regulation is described, the "beat phenomenon," which has been made visible by the interaction of two circadian rhythms with different frequencies in one body. The separation of the two physiological rhythms was the consequence of social interactions, that is, by the desire of a subject to share and to escape common time during different phases of the long-term experiment. The theoretical arguments on synchronization are summarized with the general statement: "Nothing in cognitive science makes sense except in the light of time windows." The hypothesis is forwarded that time windows that express discrete timing mechanisms in behavioral control and on the level of conscious experiences are the necessary bases to create cognitive order, and it is suggested that time windows are implemented by neural oscillations in different frequency domains.

What could be a unifying principle for the manifold of temporal experiences: the simultaneity or temporal order of events, the subjective present, the duration of experiences, or the impression of a continuity of time? Furthermore, we time travel to the past visiting in imagination previous experiences in episodic memory, and we also time travel to the future anticipating actions or plans. For such time traveling we divide time into three domains: past, present, and future. What could be an escape out of this “jungle of time” characterized by many different perceptual and conceptual phenomena? The key concept we want to propose is “identity” which is derived from homeostasis as a fundamental biological principle. Within this conceptual frame two modes of identity are distinguished: individual or self-identity required because of homeostatic demands, and object-related identity necessary for the reliability and efficiency of neuro-cognitive processing. With this concept of self- and object-identity, the different temporal experiences can be conceptualized within a common frame. Thus, we propose a fundamental biological principle to conceptually unify temporal phenomena on the psychological level.

No abstract

Given a large-scale rhythmic time series containing mostly normal data segments (or `beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For example, how can we detect anomalous beats from electrocardiogram (ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs explainable results to pinpoint the anomalous time ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error using an adversarial generation approach, as well as data augmentation using time series warping. Experiments show that BeatGAN accurately and efficiently detects anomalous beats in ECG time series, and routes doctors' attention to anomalous time ticks, achieving accuracy of nearly 0.95 AUC, and very fast inference (2.6 ms per beat). In addition, we show that BeatGAN accurately detects unusual motions from multivariate motion-capture time series data, illustrating its generality.

Attention is intrinsic to our perceptual representations of sensory inputs. Best characterized in the visual domain, it is typically depicted as a spotlight moving over a saliency map that topographically encodes strengths of visual features and feedback modulations over the visual scene. By introducing smells to two well-established attentional paradigms, the dot-probe and the visual-search paradigms, we find that a smell reflexively directs attention to the congruent visual image and facilitates visual search of that image without the mediation of visual imagery. Furthermore, such effect is independent of, and can override, top-down bias. We thus propose that smell quality acts as an object feature whose presence enhances the perceptual saliency of that object, thereby guiding the spotlight of visual attention. Our discoveries provide robust empirical evidence for a multimodal saliency map that weighs not only visual but also olfactory inputs.

Event timing engages a distributed neural network including cortical and subcortical structures. However, it remains unclear whether the early visual cortex contributes to event timing. Here we showed that the processes of nontemporal visual features such as orientation and spatial location, which are coded by the early visual cortex, contribute to the temporal representation of a visual stimulus. Participants were presented with 2 successive Gabor patches (a prime and a target) with different orientations or spatial locations. The subjective duration of the target was significantly reduced when it was preceded by the prime compared with when presented alone. More important, this duration-compression effect varied systematically as a function of orientation similarity or spatial proximity between the prime and the target and was influenced by how the prime and the target were perceptually grouped. Our results suggest that repetition suppression of neural activity in response to orientation may contribute to the observed duration distortion and that neurons in the early visual cortex with small receptive fields and orientation selectivity may be involved in visual temporal perception. Our findings help to understand the functional role of early visual cortex in event timing in humans.

This paper presents optimized implementations of two different pipeline FFT processors on Xilinx Spartan-3 and Virtex-4 FPGAs. Different optimization techniques and rounding schemes were explored. The implementation results achieved better performance with lower resource usage than prior art. The 16-bit 1024-point FFT with the R2 2 SDF architecture had a maximum clock frequency of 95.2 MHz and used 2802 slices on the Spartan-3, a throughput per area ratio of 0.034 Msamples/s/slice. The R4SDC architecture ran at 123.8 MHz and used 4409 slices on the Spartan-3, a throughput per area ratio of 0.028 Msamples/s/slice. On Virtex-4, the 16-bit 1024-point R2 2 SDF architecture ran at 235.6 MHz and used 2256 slice, giving a 0.104 Msamples/s/slice ratio; the 16-bit 1024-point R4SDC architecture ran at 219.2 MHz and used 3064 slices, giving a 0.072 Msamples/s/slice ratio. The R2 2 SDF was more efficient than the R4SDC in terms of throughput per area due to a simpler controller and an easier balanced rounding scheme. This paper also shows that balanced stage rounding is an appropriate rounding scheme for pipeline FFT processors.