Theory on The Vision-based Mechanisms of Subjective Time Within The Context of The Neural and Psychological Models of Time Perception

Temporal processing is crucial for interval, duration and motion discrimination, as well as the ability to order events. Humans process temporal information over a large scale ranging from microseconds to daily circadian rhythms. The basic questions in the time perception literature include whether timing is centralized or distributed in the brain and whether different time scales or modalities (such as sensory or motor) are processed by different neural mechanisms. In this review, focus will be on visual timing in the millisecond range and the underlying temporal mechanisms.The classical model of a supramodal centralised clock, in which scaling between real and apparent time is accomplished by a change in the arousal level, has been challenged by our evidence, following Johnston et al. (2006), that the apparent duration can be manipulated in a local region of visual field by adaptation to motion or flicker and that the effects of temporal frequency adaptation on perceived duration and perceived temporal frequency are dissociable. The relationship between time, motion and space supports the idea that time is an attribute of a visual stimulus like any other low level features such as color or motion, which we suggest may imply a time pathway in the brain. Keywords: Visual perception, time perception, visual brain

Luminescence Properties and Judd–Ofelt Analysis of Various ErF3 Concentration-Doped BaF2 Crystals

The influence of erbium ion concentration on the optical properties of BaF2:ErF3 crystals was investigated. Four ErF3 concentration (0.05, 0.08, 0.15 and 0.5 mol% ErF3)-doped BaF2 crystals were obtained using the Bridgman technique. Room temperature optical absorption in the 250–850 nm spectral range was measured, and the photoluminescence (PL) and decay times were also investigated. The Judd–Ofelt (JO) approximation was used, taking into account four absorption peaks (at 377, 519, 653 and 802 nm). The JO intensity parameters, Ωt (t = 2, 4, 6), were calculated. The influence of the ErF3 concentration on the JO parameters, branching ratio, radiative transition probability and radiative lifetime were studied. The obtained results were compared with measured values and with those reported in the literature. Under excitation at 380 nm, the well-known green (539 nm) and red (668 nm) emissions were obtained. The calculated and experimental radiative lifetimes were in millisecond range for green and red emissions. The intensity of the PL spectra varied with the Er3+ ion concentration. The emission intensity increased linearly or exponentially, depending on the ErF3 concentration. Under excitation at 290 nm, separate to the green and red emissions, a new UV emission band (at 321 nm) was obtained. Other research has not reported the UV emission or the influence of ErF3 concentration on emission behavior.

Automated long-term two-photon imaging in head-fixed walking Drosophila

The brain of Drosophila shows dynamics at multiple timescales, from the millisecond range of fast voltage or calcium transients to functional and structural changes occurring over multiple days. To relate such dynamics to behavior requires monitoring neural circuits across these multiple timescales in behaving animals. Here, we develop a technique for automated long-term two-photon imaging in fruit flies, during wakefulness and sleep, navigating in virtual reality over up to seven days. The method is enabled by laser surgery, a microrobotic arm for controlling forceps for dissection assistance, an automated feeding robot, as well as volumetric, simultaneous multiplane imaging. The approach is validated in the fly's head direction system. Imaging in behaving flies over multiple timescales will be useful for understanding circadian activity, learning and long-term memory, or sleep.

DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection

In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Most of the available current technologies focus on offline pose estimation with high spatiotemporal resolution. However, to correlate behavior with neuronal activity it is often necessary to detect and react online to behavioral expressions. Here we present DeepLabStream, a versatile closed-loop tool providing real-time pose estimation to deliver posture dependent stimulations. DeepLabStream has a temporal resolution in the millisecond range, can utilize different input, as well as output devices and can be tailored to multiple experimental designs. We employ DeepLabStream to semi-autonomously run a second-order olfactory conditioning task with freely moving mice and optogenetically label neuronal ensembles active during specific head directions.

Slow Transition Path Times Reveal a Complex Folding Barrier in a Designed Protein

De-novo designed proteins have received wide interest as potential platforms for nano-engineering and biomedicine. While much work is being done in the design of thermodynamically stable proteins, the folding process of artificially designed proteins is not well-studied. Here we used single-molecule force spectroscopy by optical tweezers to study the folding of ROSS, a de-novo designed 2x2 Rossmann fold. We measured a barrier crossing time in the millisecond range, much slower than what has been reported for other systems. While long transition times can be explained by barrier roughness or slow diffusion, we show that isotropic roughness cannot explain the measured transition path time distribution. Instead, this study shows that the slow barrier crossing of ROSS is caused by the population of three short-lived high-energy intermediates. In addition, we identify incomplete and off-pathway folding events with different barrier crossing dynamics. Our results hint at the presence of a complex transition barrier that may be a common feature of many artificially designed proteins.

Efficient SMA Actuation—Design and Control Concepts

The versatility of the form factors of thermal shape memory alloys (SMA) in combination with their unique actuation and sensing abilities allow for the design and construction of innovative multifunctional systems. Despite the considerable number of advantages, such as their exceptional energy density, only a few SMA-based actuator systems are commercially available. One of the main reasons for this is their inefficient thermal activation and the resulting high energy consumption. The efficiency of SMA-based actuator systems can be improved by innovative design and control approaches. In the first part of this paper, the intelligent combination of SMA actuator wires with bi-stable, nonlinear spring elements is described. This combination eliminates the commonly quoted disadvantages of SMAs—slow actuation and energy inefficiency—for a wide range of applications. In particular, two energy-free actuator configurations are realized, which can be applied to any non-proportional actuation tasks. The second approach for the realization of high-speed actuation and energy efficiency is the activation of SMA wires with high voltage pulses, which leads to actuation times in the millisecond range and energy savings of up to 80% in comparison to the suppliers’ recommendations. It is shown that even high AC voltages such as typical mains supplies can be directly used for highly efficient SMA activation.

Parallel Neural Multiprocessing with Gamma Frequency Latencies

The Poisson variability in cortical neural responses has been typically modeled using spike averaging techniques, such as trial averaging and rate coding, since such methods can produce reliable correlates of behavior. However, mechanisms that rely on counting spikes could be slow and inefficient and thus might not be useful in the brain for computations at timescales in the 10 millisecond range. This issue has motivated a search for alternative spike codes that take advantage of spike timing and has resulted in many studies that use synchronized neural networks for communication. Here we focus on recent studies that suggest that the gamma frequency may provide a reference that allows local spike phase representations that could result in much faster information transmission. We have developed a unified model (gamma spike multiplexing) that takes advantage of a single cycle of a cell's somatic gamma frequency to modulate the generation of its action potentials. An important consequence of this coding mechanism is that it allows multiple independent neural processes to run in parallel, thereby greatly increasing the processing capability of the cortex. System-level simulations and preliminary analysis of mouse cortical cell data are presented as support for the proposed theoretical model.

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FCEEG to second range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

DeepLabStream: Closing the loop using deep learning-based markerless, real-time posture detection

In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Current technologies enable offline pose estimation with high spatio-temporal resolution, however to understand complex behaviors, it is necessary to correlate the behavior with neuronal activity in real-time. Here we present DeepLabStream, a highly versatile, closed-loop solution for freely moving mice that can autonomously conduct behavioral experiments ranging from behavior-based learning tasks to posture-dependent optogenetic stimulation. DeepLabStream has a temporal resolution in the millisecond range, can operate with multiple devices and can be easily tailored to a wide range of species and experimental designs. We employ DeepLabStream to autonomously run a second-order olfactory conditioning task for freely moving mice and to deliver optogenetic stimuli based on mouse head-direction.