Literature DB >> 22180517

Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex.

Cliff C Kerr1, Samuel A Neymotin, George L Chadderdon, Christopher T Fietkiewicz, Joseph T Francis, William W Lytton.   

Abstract

Damage to a cortical area reduces not only information transmitted to other cortical areas, but also activation of these areas. This phenomenon, whereby the dynamics of a follower area are dramatically altered, is typically manifested as a marked reduction in activity. Ideally, neuroprosthetic stimulation would replace both information and activation. However, replacement of activation alone may be valuable as a means of restoring dynamics and information processing of other signals in this multiplexing system. We used neuroprosthetic stimulation in a computer model of the cortex to repair activation dynamics, using a simple repetitive stimulation to replace the more complex, naturalistic stimulation that had been removed. We found that we were able to restore activity in terms of neuronal firing rates. Additionally, we were able to restore information processing, measured as a restoration of causality between an experimentally recorded signal fed into the in silico brain and a cortical output. These results indicate that even simple neuroprosthetics that do not restore lost information may nonetheless be effective in improving the functionality of surrounding areas of cortex.

Mesh:

Year:  2011        PMID: 22180517     DOI: 10.1109/TNSRE.2011.2178614

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  17 in total

Review 1.  Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

Authors:  Elliot Greenwald; Matthew R Masters; Nitish V Thakor
Journal:  Med Biol Eng Comput       Date:  2016-01-11       Impact factor: 2.602

2.  Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis.

Authors:  S Dura-Bernal; S A Neymotin; C C Kerr; S Sivagnanam; A Majumdar; J T Francis; W W Lytton
Journal:  IBM J Res Dev       Date:  2017-05-23       Impact factor: 1.889

3.  Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex.

Authors:  Samuel A Neymotin; George L Chadderdon; Cliff C Kerr; Joseph T Francis; William W Lytton
Journal:  Neural Comput       Date:  2013-09-18       Impact factor: 2.026

4.  Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm.

Authors:  Salvador Dura-Bernal; George L Chadderdon; Samuel A Neymotin; Joseph T Francis; William W Lytton
Journal:  Pattern Recognit Lett       Date:  2014-01-15       Impact factor: 3.756

5.  Computer modeling of ischemic stroke.

Authors:  Alexandra H Seidenstein; Frank C Barone; William W Lytton
Journal:  Scholarpedia J       Date:  2015

6.  Motor cortex microcircuit simulation based on brain activity mapping.

Authors:  George L Chadderdon; Ashutosh Mohan; Benjamin A Suter; Samuel A Neymotin; Cliff C Kerr; Joseph T Francis; Gordon M G Shepherd; William W Lytton
Journal:  Neural Comput       Date:  2014-04-07       Impact factor: 2.026

7.  Emergence of physiological oscillation frequencies in a computer model of neocortex.

Authors:  Samuel A Neymotin; Heekyung Lee; Eunhye Park; André A Fenton; William W Lytton
Journal:  Front Comput Neurosci       Date:  2011-04-19       Impact factor: 2.380

8.  Cortical information flow in Parkinson's disease: a composite network/field model.

Authors:  Cliff C Kerr; Sacha J Van Albada; Samuel A Neymotin; George L Chadderdon; P A Robinson; William W Lytton
Journal:  Front Comput Neurosci       Date:  2013-04-25       Impact factor: 2.380

9.  Cortical plasticity induced by spike-triggered microstimulation in primate somatosensory cortex.

Authors:  Weiguo Song; Cliff C Kerr; William W Lytton; Joseph T Francis
Journal:  PLoS One       Date:  2013-03-05       Impact factor: 3.240

10.  An attractor-based complexity measurement for Boolean recurrent neural networks.

Authors:  Jérémie Cabessa; Alessandro E P Villa
Journal:  PLoS One       Date:  2014-04-11       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.