Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Neural interfaces are evolving at a rapid pace owing to advances in material science and fabrication, reduced cost of scalable complementary metal oxide semiconductor (CMOS) technologies, and highly interdisciplinary teams of researchers and engineers that span a large range from basic to applied and clinical sciences. This study outlines currently established technologies, defined as instruments and biological study systems that are routinely used in neuroscientific research. After identifying the shortcomings of current technologies, such as a lack of biocompatibility, topological optimization, low bandwidth, and lack of transparency, it maps out promising directions along which progress should be made to achieve the next generation of symbiotic and intelligent neural interfaces. Lastly, it proposes novel applications that can be achieved by these developments, ranging from the understanding and reproduction of synaptic learning to live-long multimodal measurements to monitor and treat various neuronal disorders.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468917 | PMC |
http://dx.doi.org/10.1002/adhm.202301055 | DOI Listing |
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