Previous behavioural studies in our laboratory have demonstrated that the visual input is both necessary and sufficient for the acquisition of the acutely compensated state following hemilabyrinthectomy (HL) in the goldfish. Here we examine the role of the visual input in the maintenance of the compensated state. Exposure of acutely compensated animals to illumination from below (IFB) or infra-red illumination (IRI) elicited a decompensation: whereas IRI was no longer effective 4 days after HL, the susceptibility to IFB disappeared slowly over a number of weeks. Exposure of acutely compensated animals to unilateral illumination (UI) induced a highly asymmetrical dorsal light response 1 day after HL: tilt towards the ipsilateral side was extreme, whilst tilt towards the contralateral side was similar to preoperative values. This pronounced side difference decreased rapidly over the next 3 days and then more slowly over the following weeks and months. The findings show (1) that the maintenance of the acutely compensated state is temporarily dependent not only on the presence of light but also on its direction of incidence; and (2) that the visual-vestibular integration governing posture and locomotion is strongly biased in favour of the visual input to the lesioned side during the early postoperative period and subsequently returns to near preoperative values. The present results are compatible with the hypothesis that acute vestibular compensation in the goldfish is based on a visual substitution process. The latter is not permanent, however, the chronic course of compensation being characterized by a progressive decrease in reliance on visual cues. The observed changes in visual-vestibular integration with time are assumed to reflect modifications in inter- and/or extra-vestibular commissural systems by which the intact labyrinth gradually strengthens its control over the deafferented nuclear complex.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/s0166-4328(05)80012-9 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Unlabelled: The basal ganglia play a crucial role in action selection by facilitating desired movements and suppressing unwanted ones. The substantia nigra pars reticulata (SNr), a key output nucleus, facilitates movement through disinhibition of the superior colliculus (SC). However, its role in action suppression, particularly in primates, remains less clear.
View Article and Find Full Text PDFSparse coding enables cortical populations to represent sensory inputs efficiently, yet its temporal dynamics remain poorly understood. Consistent with theoretical predictions, we show that stimulus onset triggers broad cortical activation, initially reducing sparseness and increasing mutual information. Subsequently, competitive interactions sustain mutual information as activity declines and sparseness increases.
View Article and Find Full Text PDFqPRF: A system to accelerate population receptive field modeling.
Neuroimage
January 2025
Division of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New District, 200124, Shanghai, China; Center for Neural Science, New York University, 4 Washington Place, NY, 10003, NY, USA; NYU-ECNU Institute of Brain and Cognitive Science, 3663 Zhongshan Road North, Putuo District, 200062, Shanghai, China. Electronic address:
BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF ("quick PRF"), a system for accelerated PRF modeling that reduced the computation time by a factor ¿1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al.
View Article and Find Full Text PDFClassification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks.
Front Neuroinform
December 2024
Institute of Theoretical Physics, Jagiellonian University, Kraków, Poland.
Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging.
View Article and Find Full Text PDFSpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis.
Mach Learn Clin Neuroimaging (2024)
December 2024
Stanford University, Stanford, CA 94305, USA.
Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!