Journal article
Brain-Machine Interfaces beyond Neuroprosthetics
Neuron (Cambridge, Mass.), v 86(1)
08 Apr 2015
PMID: 25856486
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The field of invasive brain-machine interfaces (BMIs) is typically associated with neuroprosthetic applications aiming to recover loss of motor function. However, BMIs also represent a powerful tool to address fundamental questions in neuroscience. The observed subjects of BMI experiments can also be considered as indirect observers of their own neurophysiological activity, and the relationship between observed neurons and (artificial) behavior can be genuinely causal rather than indirectly correlative. These two characteristics defy the classical object-observer duality, making BMIs particularly appealing for investigating how information is encoded and decoded by neural circuits in real time, how this coding changes with physiological learning and plasticity, and how it is altered in pathological conditions. Within neuroengineering, BMI is like a tree that opens its branches into many traditional engineering fields, but also extends deep roots into basic neuroscience beyond neuroprosthetics.
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Details
- Title
- Brain-Machine Interfaces beyond Neuroprosthetics
- Creators
- Karen A. Moxon - Drexel UniversityGuglielmo Foffani - CEU San Pablo University
- Publication Details
- Neuron (Cambridge, Mass.), v 86(1)
- Publisher
- Elsevier
- Number of pages
- 13
- Grant note
- FEDER; European Commission PI11/02451 / Fondo de Investigacion Sanitaria, Instituto de Salud Carlos III (Spain); Instituto de Salud Carlos III 9205 / Michael J. Fox Foundation (USA) CBET-1402984 / National Science Foundation (USA); National Science Foundation (NSF) 1402984 / Div Of Chem, Bioeng, Env, & Transp Sys; National Science Foundation (NSF); NSF - Directorate for Engineering (ENG) 89500 / Shriners Hospital for Children (USA)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000352552900013
- Scopus ID
- 2-s2.0-84930370551
- Other Identifier
- 991019168186104721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Neurosciences