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Background And Purpose: The human visual system responds asymmetrically to visual motion stimuli in opposite directions due to the involvement of the same brain areas but different operating processes. The expansion mode is thought to invoke a vigilance mechanism, whereas the contraction mode does not.
Methods: To investigate discrepancies between these modes, we produced dynamic connectivity maps based on mutual information between visual-evoked dipole sources of magnetoencephalography, which were steered by visual activity patterns in functional magnetic resonance imaging under two motion-stimulus modes.
Results: In the expansion mode, information was conveyed from V1 at 50-75 ms after motion onset, fed forward to V3A and then V5. Top-down connectivity paths were evident after a latency of 100 ms. Many of these interactions occurred within 200 ms. However, in the contraction mode, information was conveyed from V3A to V5, followed by feedback, but regained from V1 after a latency of 250 ms. Although these interactions were delayed by about 250 ms, they were completed within 500 ms.
Conclusions: These findings show that detect spatiotemporal differences between expansion and contraction modes can be readily detected using time-flow charts. Moreover, delay interactions could be insensitive to object motion away from the observer.
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http://dx.doi.org/10.1111/j.1552-6569.2011.00623.x | DOI Listing |
Sci Adv
September 2025
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
The locus coeruleus-norepinephrine (LC-NE) system regulates arousal and awakening; however, it remains unclear whether the LC does this in a global or circuit-specific manner. We hypothesized that sensory-evoked awakenings are predominantly regulated by specific LC-NE efferent pathways. Anatomical, physiological, and functional modularities of LC-NE pathways involving the mouse basal forebrain (BF) and pontine reticular nucleus (PRN) were tested.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Faculty of Science, Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.
Predictive coding (PC) proposes that our brains work as an inference machine, generating an internal model of the world and minimizing predictions errors (i.e., differences between external sensory evidence and internal prediction signals).
View Article and Find Full Text PDFAdv Physiol Educ
September 2025
Department of Biochemistry, All India Institute of Medical Sciences, Deoghar, India. Email id:
In this article, "Cosmosis" introduces a newly coined metaphorical term that illustrates conceptual parallels between the physiological process of osmosis and the expansive dynamics of the cosmos. Designed as an interdisciplinary teaching framework, Cosmosis provides a novel way to link cellular homeostasis with cosmological principles such as entropy, spacetime curvature, and dark energy. By drawing on core physiological terms such as concentration gradients, osmotic pressure, aquaporins, and membrane selectivity, Cosmosis offers an analogy that may spark curiosity, support integrative thinking, and encourage cross-disciplinary dialogue in physiology and biochemistry education.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma 43125, Italy.
Typically, people perform actions in a valenced-positive or negative-way, depending on their attitudes or desires. These forms of action are named vitality forms (VFs). While it is well established that action goals are mediated by a parieto-frontal network, less is known about the processing of VFs.
View Article and Find Full Text PDFChaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
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