Bank of Japan policymakers said it was necessary to monitor the negative economic impact of continuing monetary stimulus amid stubbornly weak inflation, minutes of their June meeting showed Friday. The minutes suggest members of the central bank's Policy Board recognized a need to make policy more flexible, setting the stage for their decision earlier this week to allow long-term yields to rise higher and tweak the bank's asset purchases. Many board members pointed out that it was "important to continue to conduct a multifaceted monitoring and assessment of the positive effects and side effects that could arise from the continuation of powerful monetary easing" at the meeting held on June 14 and 15. One person said the BOJ should make sure its policy does not cause "severe distortions" to economic and financial conditions, while another said that it was necessary to "consider the possible countermeasures against the side effects before they materialized." The minutes are released after approval by board members at the following policy meeting and do not attribute comments to individual speakers.
As part of WIRED's exclusive look at Breaking2, Nike's attempt to break the two-hour marathon mark next month in Monza, Italy, our writer is using the same training regime, apparel, and expertise as Nike's three elite athletes to try to achieve his own personal milestone: a sub-90-minute half-marathon. This is the fourth in a series of monthly updates on his progress. The trip came at a bad time for my upcoming attempt to complete a sub-90-minute half-marathon in Monza, Italy. I should have been in the meat of my training regimen, doing a high-mileage week. Instead, I was in Bangui, chasing interviews in an unstable, poor, and fiendishly hot country in crisis.
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity.
How meaning is represented in the brain is still one of the big open questions in neuroscience. Does a word (e.g., bird) always have the same representation, or does the task under which the word is processed alter its representation (answering "can you eat it?" versus "can it fly?")? The brain activity of subjects who read the same word while performing different semantic tasks has been shown to differ across tasks. However, it is still not understood how the task itself contributes to this difference. In the current work, we study Magnetoencephalography (MEG) brain recordings of participants tasked with answering questions about concrete nouns. We investigate the effect of the task (i.e. the question being asked) on the processing of the concrete noun by predicting the millisecond-resolution MEG recordings as a function of both the semantics of the noun and the task. Using this approach, we test several hypotheses about the task-stimulus interactions by comparing the zero-shot predictions made by these hypotheses for novel tasks and nouns not seen during training. We find that incorporating the task semantics significantly improves the prediction of MEG recordings, across participants. The improvement occurs 475-550ms after the participants first see the word, which corresponds to what is considered to be the ending time of semantic processing for a word. These results suggest that only the end of semantic processing of a word is task-dependent, and pose a challenge for future research to formulate new hypotheses for earlier task effects as a function of the task and stimuli.