This study investigated how changes in nutritional motivation modulate odour-related oscillatory activities at several levels of the olfactory pathway in non-trained rats. Local field potential recordings were obtained in freely moving animals in the olfactory bulb (OB), anterior and posterior parts of the piriform cortex (APC and PPC respectively) and lateral entorhinal cortex (EC). Dynamic signal analysis detected changes in power during odour presentation for several frequency bands The results showed that in most cases odour presentation was associated with changes in a wide 15-90 Hz frequency band of activity in each olfactory structure. However, nutritional state modulated initial responses to food odour (FO) in the OB and EC selectively in the 15-30 Hz frequency band. Changes in nutritional state also modulated responses to repeated FO stimuli. Habituation was expressed differentially across structures with a clear dissociation between the two parts of the piriform cortex. Finally, systemic injections of scopolamine (0.125 mg/kg) selectively blocked expression of the nutritional modulation in the OB found in the beta band. These results suggest that internal state can differentially modulate odour processing among different olfactory areas and point to a cholinergic-sensitive beta band oscillation during presentation of a behaviourally meaningful odorant.
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http://dx.doi.org/10.1093/chemse/25.5.561 | DOI Listing |
Chaos
January 2025
Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille 13005, France.
PLoS Comput Biol
January 2025
Kavli Institute for Systems Neuroscience and Centre for Algorithms in the Cortex, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Persistent homology applied to the activity of grid cells in the Medial Entorhinal Cortex suggests that this activity lies on a toroidal manifold. By analyzing real data and a simple model, we show that neural oscillations play a key role in the appearance of this toroidal topology. To quantitatively monitor how changes in spike trains influence the topology of the data, we first define a robust measure for the degree of toroidality of a dataset.
View Article and Find Full Text PDFJ Transl Int Med
February 2024
Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China.
Background And Objective: Hemodynamic changes that lead to increased blood pressure represent the main drivers of organ damage in hypertension. Prolonged increases to blood pressure can lead to vascular remodeling, which also affects vascular hemodynamics during the pathogenesis of hypertension. Exercise is beneficial for relieving hypertension, however the mechanistic link between exercise training and how it influences hemodynamics in the context of hypertension is not well understood.
View Article and Find Full Text PDFSci Rep
January 2025
RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Forskningsveien 3A, Oslo, 0373, Norway.
Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Mathematics, Southern Methodist University, Dallas, 75275, TX, USA. Electronic address:
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spectral bias of fully connected deep neural networks in approximating oscillatory functions. The diffusion models are obtained from the spectral form of the error equation of the MscaleDNN, derived with a neural tangent kernel approach and gradient descent training and a sine activation function, assuming a vanishing learning rate and infinite network width and domain size.
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