Transmitting shocks to the economy: The contribution of interest and exchange rates and the credit channel

Understanding the transmission channels of shocks is critical for successful policy response. This paper develops a dynamic general equilibrium model to assess the relative importance of the interest rate, the exchange rate and the credit channels in transmitting shocks in an open economy. The relative contribution of each channel is determined by comparing the impulse responses when the relevant channel is suppressed with the impulse responses when all three channels are operating. The results suggest that all three channels contribute to business cycle fluctuations and the transmission of shocks to the economy. But the magnitude of the impact of the interest rate channel crucially depends on the inflation process and the structure of the economy.Transmission channels, open economy, general equilibrium model

The coming decade of digital brain research: a vision for neuroscience at the intersection of technology and computing

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales— from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, to identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research

The Effects of Context and Attention on Spiking Activity in Human Early Visual Cortex

<div><p>Here we report the first quantitative analysis of spiking activity in human early visual cortex. We recorded multi-unit activity from two electrodes in area V2/V3 of a human patient implanted with depth electrodes as part of her treatment for epilepsy. We observed well-localized multi-unit receptive fields with tunings for contrast, orientation, spatial frequency, and size, similar to those reported in the macaque. We also observed pronounced gamma oscillations in the local-field potential that could be used to estimate the underlying spiking response properties. Spiking responses were modulated by visual context and attention. We observed orientation-tuned surround suppression: responses were suppressed by image regions with a uniform orientation and enhanced by orientation contrast. Additionally, responses were enhanced on regions that perceptually segregated from the background, indicating that neurons in the human visual cortex are sensitive to figure-ground structure. Spiking responses were also modulated by object-based attention. When the patient mentally traced a curve through the neurons’ receptive fields, the accompanying shift of attention enhanced neuronal activity. These results demonstrate that the tuning properties of cells in the human early visual cortex are similar to those in the macaque and that responses can be modulated by both contextual factors and behavioral relevance. Our results, therefore, imply that the macaque visual system is an excellent model for the human visual cortex.</p></div

Receptive field location and size estimates from E6 and E7.

<p>(A) MUA receptive field (activity averaged between 50 and 300 ms after stimulus onset) from E6 to a 1° x 1° checkerboard briefly presented at each location of an 11 x 11 grid (left panel). The center of the RF was estimated by fitting a 2D Gaussian (right panel) to the data and the RF size as the width of the Gaussian at half of the maximum (FWHM), averaged across the <i>x</i> and <i>y</i> directions. (B) Responses from the 10% of locations closest to the center of the RF (red line) and the 10% of locations farthest from the center of the RF (blue line). The gray bars indicate samples with significant differences between these conditions (<i>t</i> test, <i>p</i> < 0.05). The latency of the response was estimated as the first significant sample that was followed by ten contiguous significant tests (arrow). (C) The change in gamma power (40–120 Hz) in a window from 100–250 ms after stimulus onset at each stimulus location relative to the average power across all locations (left panel). We also fit a 2D Gaussian to the spatial profile of the gamma power increase (right panel). (D) The relative increase in power when a flash was presented within the RF (10% closest locations, red line) compared to outside the RF (10% farthest locations, blue line). The shaded region depicts +/- 1 standard error of the mean (S.E.M) estimated by bootstrapping. (E–H) Data from electrode E7, same format as A–D. Data is available from doi:<a href="http://dx.doi.org/10.17605/OSF.IO/BRCZY" target="_blank">10.17605/OSF.IO/BRCZY</a></p

Orientation tuning in V2/V3.

<p>(A) Upper panel, MUA responses at electrode E7 to sine-wave gratings drifting in one of 24 directions. In this and subsequent figures, error-bars indicate +/- 1 S.E.M. The red line is the fit of a wrapped double Gaussian. The lower panel shows the same data in polar form, averaged across opposite movement directions with the same orientation and normalized to maximum response across orientations. The gray arrow is the preferred orientation (vector average). (B) Time course of MUA at E7 elicited by gratings of the preferred (averaged across 165° and 180°) and orthogonal (averaged across 75° and 90°) orientations. Orientation tuning arises very rapidly. (C) Upper panels: Gamma power (30–100 Hz) evoked by the 24 different directions at electrode E6 (green) and E7 (red) expressed as a percentage of the pre-stimulus baseline. The gamma power was significantly tuned for orientation but did not depend on direction. Lower panels: the same data in polar form, normalized to the maximum change in power across orientations. (D) The center frequency of the gamma peak at E6 and E7 at each of the 24 directions. (E) Change in power relative to pre-stimulus baseline in response to the preferred orientation (dark red) and the orthogonal orientation (pink) for E7. Note the presence of a clear peak in the power spectrum. The solid lines show a Gaussian fit, used to estimate the central frequency of the gamma peak. (F) Spatial frequency tuning of MUA at electrodes E6 (green) and E7 (red). (G) Gamma power (30–100 Hz) changes relative to the pre-stimulus baseline at different spatial frequencies. (H) Change in power relative to pre-stimulus baseline from E7 for each of the five spatial frequencies. Data is available from doi:<a href="http://dx.doi.org/10.17605/OSF.IO/BRCZY" target="_blank">10.17605/OSF.IO/BRCZY</a></p

Attentional modulation of spiking activity in human V2/V3.

<p>(A) Example stimulus of the curve-tracing experiment. The RF of MUA at E7 is also shown. There were two distractor curves, which were connected to two other circles near and far from the RF. The right panels illustrate the two alternative stimulus configurations. (B) The stimulus paradigm: The patient initially had to fixate for 300 ms. One of nine possible stimulus configurations was shown, the three curve configurations of stimulus set 1 are shown here. The patient had to mentally trace the target curve attached to the fixation point. After a delay of 500 ms the fixation point turned green and the patient was required to make a saccade towards the red target circle connected to the fixation dot. (C) The MUA response averaged across E6 and E7 when the RFs fell on the target curve (red line) or on one of the two distractor curves (blue line, averaged across near and far distractor). The attentional modulation is the difference between the two responses (gray shading). (D) The average response at E6 and E7 in a window from 200 to 500 ms elicited by the target curve (red bar) and the distractors (blue bars). The asterisks indicate the significance of the difference in MUA between the target and distractor conditions as assessed by a <i>t</i> test (* = <i>p</i> < 0.05, ** = <i>p</i> < 0.01). Error-bars indicate 1 S.E.M. Data is available from doi:<a href="http://dx.doi.org/10.17605/OSF.IO/BRCZY" target="_blank">10.17605/OSF.IO/BRCZY</a></p

Size tuning of MUA and LFP.

<p>(A) MUA responses at E7 evoked by drifting gratings of various sizes. The peak response increases up to sizes around 3°–4° and activity is suppressed at larger sizes. (B) The average response between 0–500 ms at each size for E6 and E7. Error-bars show +/- 1 S.E.M. The red line is the best fit of the ratio-of-Gaussians model. (C) The traces show the raw LFP from 20 trials from E7. The red vertical lines mark the onset and offset of the stimulus (a 10° diameter grating). Note the low-frequency oscillations before the appearance of the stimulus, which are replaced with high-frequency gamma oscillations after stimulus onset. (D) The relative increase in the amplitude spectrum in the gamma range as function of stimulus size averaged across E6 and E7 (compared to a pre-stimulus baseline). The non-normalized (log) amplitude spectra for E6 and E7 are shown on the right. (E) Center frequency of the gamma peak, as estimated by fitting a Gaussian function. The colors of the dots indicate the size of the grating; conventions as in panel D. (F) Increase in gamma-band amplitude as a function of the MUA response. Note the absence of a clear relationship. (G) Dependence of the change in gamma amplitude on stimulus size. There was a strong correlation (r = 0.98, <i>p</i> < 0.001). Data is available from doi:<a href="http://dx.doi.org/10.17605/OSF.IO/BRCZY" target="_blank">10.17605/OSF.IO/BRCZY</a></p

Contextual modulation of neuronal responses in human V2/V3.

<p>(A) The stimuli used to study contextual modulation. In the center-only condition, a 6° diameter grating was centered on the RF. We used two different orientations so that, on average, the orientation of the center was the same across conditions. In the Iso, Iso90, and Cross conditions, a full-screen surround was added. These conditions allowed extraction of signals related to two contextual effects: orientation-tuned surround suppression (upper panels) and figure-ground modulation (lower panels). (B) The MUA time courses averaged across E6 and E7. The inset shows responses during the early phase. These early responses were suppressed when the surround had the same orientation as the center (Iso, Iso90). Later activity depended on whether the central grating could be segmented from the background (Iso90, Cross) or not (Iso). (C) The average level of OTSS and FGM in an early (50–100 ms) and late (100–500 ms) time window for E6 and E7. Error bars represent 1 S.E.M. as estimated by a bootstrap procedure. Asterisks mark significant effects (** = <i>p</i> < 0.01, *** = <i>p</i> < 0.001, two-sample <i>t</i> test). (D) The time course of the modulatory effects. The black line illustrates OTSS (Cross-Iso90) averaged across E6 and E7. The gray line represents FGM (Iso90-Iso). The red lines show best-fitting Gaussian functions. Latencies were estimated as the point at which the Gaussians reached 50% of their maximum and are marked by the arrows. (E) Results of the latency analysis for OTSS and FGM. Error bars are 1 S.E.M. estimated by a bootstrap procedure. The black dashed line indicates the latency of the visual response estimated using the center-only condition. The latency was shorter than in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002420#pbio.1002420.g002" target="_blank">Fig 2</a> because the effective contrast of the stimulus within the RF was higher. Data is available from doi:<a href="http://dx.doi.org/10.17605/OSF.IO/BRCZY" target="_blank">10.17605/OSF.IO/BRCZY</a></p