Browsing by Author "Ashena, Narges"

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Real-time decoding of arm kinematics during grasping based on F5 neural spike data
(Springer International Publishing, 2017) Ashena, Narges; Papadourakis, V.; Raos, V.; Öztop, Erhan; Computer Science; ÖZTOP, Erhan; Ashena, Narges
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.
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Real-time decoding of arm kinematics during grasping based on f5 neural spike data
(2017-05) Ashena, Narges; Öztop, Erhan; Öztop, Erhan; Aktunç, Emrah; Uğur, E.; Department of Computer Science; Ashena, Narges
Extending our knowledge about brain mechanisms and behavior can lead to many advantages and inspiration in the diagnosis of nervous system diseases and robotics and artificial intelligence. Ventral premotor cortex, i.e. area F5, in a macaque monkey's brain is one of the areas of interest in the literature. Studies have shown that F5 area in monkeys is involved in arm movements and hand configuration, enabling the animal to grasp objects with different shapes (different grip types). Furthermore, it is shown in the studies that F5 area contains neurons called mirror neurons which are active not only during the period the animal moves his arm and hand but also while the animal is observing another monkey or person performing the same action. In this study, we aim to investigate whether, by using F5 area neural activity, monkey's arm kinematics can be decoded in real-time or not. Furthermore, how the decoding capacity of mirror and non-mirror neurons can be differentiated. To this end, the neural behavior of 32 neurons (including both mirror and non-mirror neurons) in the stated area was recorded while a monkey was performing grasping tasks on different objects. Also, monkey's motion was video captured simultaneously. Using image processing techniques and tools, kinematics data was extracted from the videos. Later, the possibility of single neuron's decoding of the kinematics data was investigated. Results reveal that although single neuron real-time decoding of the kinematics is not always ideal, reasonable performance is achievable with selected neurons from both groups. Based on the results of this study non-mirror neurons seem to act as better single-neuron decoders. Although it seems obvious that population-level activity is required for more robust decoding, the single-neuron decoding accuracy can be considered as possible criteria to categorize neurons in the F5 area.