Title :
Encoding and decoding target locations with waves in the turtle visual cortex
Author :
Du, Xiuxia ; Ghosh, Bijoy K. ; Ulinski, Philip
Author_Institution :
Dept. of Electr. & Syst. Eng., Washington Univ., St. Louis, MO, USA
fDate :
4/1/2005 12:00:00 AM
Abstract :
Visual stimuli elicit waves of activity that propagate across the visual cortex of turtles. An earlier study showed that these waves encode information about the positions of stimuli in visual space. This paper addresses the question of how this information can be decoded from the waves. Windowing techniques were used to temporally localize information contained in the wave. Sliding encoding windows were used to represent waves of activity as low dimensional temporal strands in an appropriate space. Expanding detection window (EDW) or sliding detection window (SDW) techniques were combined with statistical hypothesis testing to discriminate input stimuli. Detection based on an EDW was more reliable than detection based on a SDW. Detection performance improved at a very early stage of the cortical response as the length of the detection window is increased. The property of intrinsic noise was explicitly considered. Assuming that the noise is colored provided a more reliable estimate than did the assumption of a white noise in the cortical output.
Keywords :
bioelectric potentials; brain; decoding; encoding; medical signal detection; medical signal processing; decoding; encoding; expanding detection window; low dimensional temporal strands; sliding detection window; target locations; turtle visual cortex; Brain modeling; Cerebral cortex; Colored noise; Encoding; Humans; Information analysis; Maximum likelihood decoding; Systems engineering and theory; Testing; White noise; B space representation; Karhunen–Loeve (KL) decomposition; statistical hypothesis testing; turtle visual cortex; Action Potentials; Algorithms; Animals; Brain Mapping; Computer Simulation; Diagnosis, Computer-Assisted; Evoked Potentials, Visual; Humans; Information Storage and Retrieval; Models, Neurological; Models, Statistical; Nerve Net; Neurons; Synaptic Transmission; Turtles; Vision; Visual Cortex;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2004.841262