Monitoring the activity patterns of a large population of neurons over many days in awake animals is a valuable technique in the field of systems neuroscience. One key component of this technique consists of the precise placement of multiple electrodes into desired brain regions and the maintenance of their stability. Here, we describe a protocol for the construction of a 3D-printable hyperdrive, which includes eighteen independently adjustable tetrodes, and is specifically designed for in vivo extracellular neural recording in freely behaving rats. The tetrodes attached to the microdrives can either be individually advanced into multiple brain regions along the track, or can be used to place an array of electrodes into a smaller area. The multiple tetrodes allow for simultaneous examination of action potentials from dozens of individual neurons, as well as local field potentials from populations of neurons in the brain during active behavior. In addition, the design provides for simpler 3D drafting software that can easily be modified for differing experimental needs.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101149 | PMC |
http://dx.doi.org/10.3791/57388 | DOI Listing |
Prog Neurobiol
January 2025
Department of Biomedicine, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland. Electronic address:
The brain faces the challenging task of preserving a consistent portrayal of the external world in the face of disruptive sensory inputs. What alterations occur in sensory representation amidst noise, and how does brain activity adapt to it? Although it has previously been shown that background white noise (WN) decreases responses to salient sounds, a mechanistic understanding of the brain processes responsible for such changes is lacking. We investigated the effect of background WN on neuronal spiking activity, membrane potential, and network oscillations in the mouse central auditory system.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Cyber Science and Engineering, Xi'an Jiaotong University, China. Electronic address:
Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose Eagle, a deep framework based on bipartitE grAph learninG for anomaLy dEtection.
View Article and Find Full Text PDFJ Med Syst
January 2025
Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), C/ Mare de Déu de Guadalupe, 2, Mataró, 08303, Barcelona, Spain.
Predicting health-related outcomes can help with proactive healthcare planning and resource management. This is especially important on the older population, an age group growing in the coming decades. Considering longitudinal rather than cross-sectional information from primary care electronic health records (EHRs) can contribute to more informed predictions.
View Article and Find Full Text PDFIn the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
View Article and Find Full Text PDFJ Integr Neurosci
January 2025
Laboratory for the Study of Tactile Communication, Pushkin State Russian Language Institute, 117485 Moscow, Russia.
Background: The significance of tactile stimulation in human social development and personal interaction is well documented; however, the underlying cerebral processes remain under-researched. This study employed functional magnetic resonance imaging (fMRI) to investigate the neural correlates of social touch processing, with a particular focus on the functional connectivity associated with the aftereffects of touch.
Methods: A total of 27 experimental subjects were recruited for the study, all of whom underwent a 5-minute calf and foot massage prior to undergoing resting-state fMRI.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!