Author
Ashena, Narges, Papadourakis, V., Raos, V., Öztop, Erhan
Publication Date
2017
Publication Place
-
Springer International Publishing
Subject
Arm kinematics, Grasping Image processing, Neural decoding, Ventral premotor cortex (F5)
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-331959071-4
Record ID
1c7d6251-10b7-42dc-afca-429e28ede61d
Library Location
Computer Science
Date
2017
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
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
