Asynchronous decision making in a memorized paddle pressing task.
This paper presents a method for asynchronous decision making using recorded neural data in a binary decision task. This is a demonstration of a technique for developing motor cortical neural prosthetics that do not rely on external cued timing information. The system presented in this paper uses support vector machines and leaky integrate-and-fire elements to predict directional paddle presses. In addition to the traditional metrics of accuracy, asynchronous systems must also optimize the time needed to make a decision. The system presented is able to predict paddle presses with a median accuracy of 88% and all decisions are made before the time of the actual paddle press. An alternative bit rate measure of performance is defined to show that the system proposed here is able to perform the task with the same efficiency as the rats.
Dankert
James R
JR
Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287-5706, USA.
Olson
Byron
B
Si
Jennie
J
D000465:Algorithms; D000818:Animals; D003216:Conditioning, Operant / Q000502:physiology; D003627:Data Interpretation, Statistical; D003658:Decision Making, Computer-Assisted; D004567:Electrodes, Implanted; D008297:Male; D008568:Memory / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D051381:Rats; D017207:Rats, Sprague-Dawley; D012677:Sensation / Q000502:physiology