Both the cellular- and population-level properties of involved neurons are essential for unveiling the learning and memory functions of the brain. To give equal attention to these two aspects, neural sensors based on microelectrode arrays (MEAs) have been in the limelight due to their noninvasive detection and regulation capabilities. Here, we fabricated a neural sensor using carboxylated graphene/3,4-ethylenedioxythiophene:polystyrenesulfonate (cGO/PEDOT:PSS), which is effective in sensing and monitoring neuronal electrophysiological activity in vitro for a long time. The cGO/PEDOT:PSS-modified microelectrodes exhibited a lower electrochemical impedance (7.26 ± 0.29 kΩ), higher charge storage capacity (7.53 ± 0.34 mC/cm), and improved charge injection (3.11 ± 0.25 mC/cm). In addition, their performance was maintained after 2 to 4 weeks of long-term cell culture and 50,000 stimulation pulses. During neural network training, the sensors were able to induce learning function in hippocampal neurons through precise electrical stimulation and simultaneously detect changes in neural activity at multiple levels. At the cellular level, not only were three kinds of transient responses to electrical stimulation sensed, but electrical stimulation was also found to affect inhibitory neurons more than excitatory neurons. As for the population level, changes in connectivity and firing synchrony were identified. The cGO/PEDOT:PSS-based neural sensor offers an excellent tool in brain function development and neurological disease treatment.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9312960PMC
http://dx.doi.org/10.3390/bios12070546DOI Listing

Publication Analysis

Top Keywords

neural sensor
12
electrical stimulation
12
hippocampal neurons
8
neural
6
neurons
5
sensor nanocomposite
4
nanocomposite interface
4
interface study
4
study spike
4
spike characteristics
4

Similar Publications

Multi-gate neuron-like transistors based on ensembles of aligned nanowires on flexible substrates.

Nano Converg

January 2025

Bendable Electronics and Sustainable Technologies (BEST) Group, Electrical and Computer Engineering Department, Northeastern University, Boston, MA, 02115, USA.

The intriguing way the receptors in biological skin encode the tactile data has inspired the development of electronic skins (e-skin) with brain-inspired or neuromorphic computing. Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. This is particularly attractive when one considers the huge data produced by large number of sensors expected in a large area e-skin such as the whole-body skin of a robot.

View Article and Find Full Text PDF

Machine-learning for discovery of descriptors for gas-sensing: A case study of doped metal oxides.

Talanta

January 2025

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China. Electronic address:

Conventionally, gas sensors are studied based on functional materials, case by case, using experimental methods. In this study, 872 datasets with 34 features of doped oxides, extracted from the literature, were used to analyze the key features of gas-sensing reactions and understand gas-sensing mechanisms from a global perspective using a genetic algorithm-optimized artificial neural network. Shapley additive explanations were employed to determine the importance and relationships of the features.

View Article and Find Full Text PDF

Background: To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. Available deep learning tools, all of which are based on single sensor modalities, are however still considerably inferior to that of well-trained human assessors.

View Article and Find Full Text PDF

This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models.

View Article and Find Full Text PDF

Principled neuromorphic reservoir computing.

Nat Commun

January 2025

Neuromorphic Computing Lab, Intel, Santa Clara, CA, USA.

Reservoir computing advances the intriguing idea that a nonlinear recurrent neural circuit-the reservoir-can encode spatio-temporal input signals to enable efficient ways to perform tasks like classification or regression. However, recently the idea of a monolithic reservoir network that simultaneously buffers input signals and expands them into nonlinear features has been challenged. A representation scheme in which memory buffer and expansion into higher-order polynomial features can be configured separately has been shown to significantly outperform traditional reservoir computing in prediction of multivariate time-series.

View Article and Find Full Text PDF

Want 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!