Abstract
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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Acknowledgments
This work was supported by the grant OBSERVENEMO within the framework of the bilateral S&T Cooperation Program between the Republic of Turkey and the Hellenic Republic. Grant No. 113S391 funded by TUBITAK and grant 14UR OBSERVENEMO co- Financed by the European Union and the Greek State, MCERA/GSRT.
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Ashena, N., Papadourakis, V., Raos, V., Oztop, E. (2017). Real-Time Decoding of Arm Kinematics During Grasping Based on F5 Neural Spike Data. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_31
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DOI: https://doi.org/10.1007/978-3-319-59072-1_31
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