Inaccessible time to visual awareness during attentional blinks in macaques and humans

知覚のコマ落ちから、意識の時間を可視化 --意識の進化的側面にもアプローチ--. 京都大学プレスリリース. 2023-11-01.Even when we attend to successive visual events, we often cannot notice an event occurring during a certain temporal window. Such an inaccessible time for visual awareness is known as "attentional blink" (AB). Whether AB is a phenomenon unique to humans or exists also in other animals is unclear. Using a dual-task paradigm shared between macaques and humans, we here demonstrate a nonhuman primate model of AB. Although macaques also showed behavioral signatures of AB, their AB effect lasted longer than that of humans. To map the relation between macaque and human ABs, we introduced a time warping analysis. The analysis revealed a formal structure behind the interspecies difference of AB; the temporal window of macaque AB was scaled from that of human AB. The present study opens the door to combining the approaches of neuroscience, psychophysics, and theoretical models to further identify a scale-invariant biological substrate of visual awareness

Multi-GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional q-state Potts model

We present the multiple GPU computing with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional (2D) q-state Potts model. Extending our algorithm for single GPU computing [Comp. Phys. Comm. 183 (2012) 1155], we realize the GPU computation of the Swendsen-Wang multi-cluster algorithm for multiple GPUs. We implement our code on the large-scale open science supercomputer TSUBAME 2.0, and test the performance and the scalability of the simulation of the 2D Potts model. The performance on Tesla M2050 using 256 GPUs is obtained as 37.3 spin flips per a nano second for the q=2 Potts model (Ising model) at the critical temperature with the linear system size L=65536.Comment: accepted for publication in Comp. Phys. Commun. arXiv admin note: substantial text overlap with arXiv:1202.063

Mechanism of strong quenching of photosystem II chlorophyll fluorescence under drought stress in a lichen, Physciella melanchla, studied by subpicosecond fluorescence spectroscopy

AbstractThe mechanism of the severe quenching of chlorophyll (Chl) fluorescence under drought stress was studied in a lichen Physciella melanchla, which contains a photobiont green alga, Trebouxia sp., using a streak camera and a reflection-mode fluorescence up-conversion system. We detected a large 0.31 ps rise of fluorescence at 715 and 740 nm in the dry lichen suggesting the rapid energy influx to the 715–740 nm bands from the shorter-wavelength Chls with a small contribution from the internal conversion from Soret bands. The fluorescence, then, decayed with time constants of 23 and 112 ps, suggesting the rapid dissipation into heat through the quencher. The result confirms the accelerated 40 ps decay of fluorescence reported in another lichen (Veerman et al., 2007 [36]) and gives a direct evidence for the rapid energy transfer from bulk Chls to the longer-wavelength quencher. We simulated the entire PS II fluorescence kinetics by a global analysis and estimated the 20.2 ns−1 or 55.0 ns−1 energy transfer rate to the quencher that is connected either to the LHC II or to the PS II core antenna. The strong quenching with the 3–12 times higher rate compared to the reported NPQ rate, suggests the operation of a new type of quenching, such as the extreme case of Chl-aggregation in LHCII or a new type of quenching in PS II core antenna in dry lichens

Cerebral hierarchies: predictive processing, precision and the pulvinar

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference—based upon hierarchical predictive coding—and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context—in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature–ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention

GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional classical spin systems

We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling algorithms recently proposed with CUDA for the assignment of the cluster in the Swendsen-Wang algorithm. Starting with the q-state Potts model, we extend our implementation to the system of vector spins, the q-state clock model, with the idea of embedded cluster. We test the performance, and the calculation time on GTX580 is obtained as 2.51 nano sec per a spin flip for the q=2 Potts model (Ising model) and 2.42 nano sec per a spin flip for the q=6 clock model with the linear size L=4096 at the critical temperature, respectively. The computational speed for the q=2 Potts model on GTX580 is 12.4 times as fast as the calculation speed on a current CPU core. That for the q=6 clock model on GTX580 is 35.6 times as fast as the calculation speed on a current CPU core.Comment: accepted for publication in Comp. Phys. Commu