Long Micro-Channel Electrode Arrays: A Novel Type of Regenerative Peripheral Nerve Interface

Author(s):  
S.P. Lacour ◽  
J.J. Fitzgerald ◽  
N. Lago ◽  
E. Tarte ◽  
S. McMahon ◽  
...  
2019 ◽  
Vol 2 (1) ◽  
pp. e10026
Author(s):  
Xinuo Zhang ◽  
Chaoyang Chen ◽  
Guoxin Ni ◽  
Yong Hai ◽  
Biao Chen ◽  
...  

2021 ◽  
pp. 2004033
Author(s):  
Estelle A. Cuttaz ◽  
Christopher A. R. Chapman ◽  
Omaer Syed ◽  
Josef A. Goding ◽  
Rylie A. Green

2013 ◽  
Vol 109 (1) ◽  
pp. 261-272 ◽  
Author(s):  
Alain de Cheveigné ◽  
Jean-Marc Edeline ◽  
Quentin Gaucher ◽  
Boris Gourévitch

Local field potentials (LFPs) recorded in the auditory cortex of mammals are known to reveal weakly selective and often multimodal spectrotemporal receptive fields in contrast to spiking activity. This may in part reflect the wider “listening sphere” of LFPs relative to spikes due to the greater current spread at low than high frequencies. We recorded LFPs and spikes from auditory cortex of guinea pigs using 16-channel electrode arrays. LFPs were processed by a component analysis technique that produces optimally tuned linear combinations of electrode signals. Linear combinations of LFPs were found to have sharply tuned responses, closer to spike-related tuning. The existence of a sharply tuned component implies that a cortical neuron (or group of neurons) capable of forming a linear combination of its inputs has access to that information. Linear combinations of signals from electrode arrays reveal information latent in the subspace spanned by multichannel LFP recordings and are justified by the fact that the observations themselves are linear combinations of neural sources.


Author(s):  
Shelby R. Svientek ◽  
Justin P. Wisely ◽  
Amir Dehdashtian ◽  
Jarred V. Bratley ◽  
Paul S. Cederna ◽  
...  

2020 ◽  
Author(s):  
Yaxi Hu ◽  
Daniel C. Ursu ◽  
Racquel A. Sohasky ◽  
Ian C. Sando ◽  
Shoshana L. W. Ambani ◽  
...  

Author(s):  
John V. Larson ◽  
Stephanie A. Goretski ◽  
Joshua W. Cockrum ◽  
Melanie G. Urbanchek ◽  
Paul S. Cederna ◽  
...  

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