Real-time decoding of arm kinematics during grasping based on F5 neural spike data

Name: Real-time decoding of arm kinematics during grasping based on F5 neural spike data
Author: Ashena, Narges, Papadourakis, V., Raos, V., Öztop, Erhan

İsim	Real-time decoding of arm kinematics during grasping based on F5 neural spike data
Yazar	Ashena, Narges, Papadourakis, V., Raos, V., Öztop, Erhan
Basım Tarihi:	2017
Basım Yeri	- Springer International Publishing
Konu	Arm kinematics, Grasping Image processing, Neural decoding, Ventral premotor cortex (F5)
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-331959071-4
Kayıt Numarası	1c7d6251-10b7-42dc-afca-429e28ede61d
Lokasyon	Computer Science
Tarih	2017
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Several studies have shown that the information related to grip type, object identity and kinematics of monkey grasping actions is available in macaque cortical areas of F5, MI, and AIP. In particular, these studies show that the neural discharge patterns of the neuron populations from the aforementioned areas can be used for accurate decoding of action parameters. In this study, we focus on single neuron decoding capacity of neurons in a given region, F5, considering their functional classification, i.e. as to whether they show the mirror property or not. To this end, we recorded neural spike data and arm kinematics from a monkey that performed grasping actions. The spikes were then used as a regressor to predict the kinematic parameters. Results show that single neuron real-time decoding of the kinematics is not perfect, but reasonable performance can be achieved with selected neurons from both populations. Considering the neurons that we have studied (N:32), non-mirror neurons seem to act as better single-neuron decoders. Although it is clear that population-level activity is needed for robust decoding, single-neuron decoding capacity may be used as a quantitative means to classify neurons in a given region.
DOI	10.1007/978-3-319-59072-1_31
Cilt	10261

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Real-time decoding of arm kinematics during grasping based on F5 neural spike data

Yazar Ashena, Narges, Papadourakis, V., Raos, V., Öztop, Erhan

Basım Tarihi 2017

Basım Yeri - Springer International Publishing

Konu Arm kinematics, Grasping Image processing, Neural decoding, Ventral premotor cortex (F5)

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-331959071-4

Kayıt Numarası 1c7d6251-10b7-42dc-afca-429e28ede61d

Lokasyon Computer Science

Tarih 2017

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Several studies have shown that the information related to grip type, object identity and kinematics of monkey grasping actions is available in macaque cortical areas of F5, MI, and AIP. In particular, these studies show that the neural discharge patterns of the neuron populations from the aforementioned areas can be used for accurate decoding of action parameters. In this study, we focus on single neuron decoding capacity of neurons in a given region, F5, considering their functional classification, i.e. as to whether they show the mirror property or not. To this end, we recorded neural spike data and arm kinematics from a monkey that performed grasping actions. The spikes were then used as a regressor to predict the kinematic parameters. Results show that single neuron real-time decoding of the kinematics is not perfect, but reasonable performance can be achieved with selected neurons from both populations. Considering the neurons that we have studied (N:32), non-mirror neurons seem to act as better single-neuron decoders. Although it is clear that population-level activity is needed for robust decoding, single-neuron decoding capacity may be used as a quantitative means to classify neurons in a given region.

DOI 10.1007/978-3-319-59072-1_31

Cilt 10261