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Neural population activity in area MT improves determination of perceptual states

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84063

Maier AV, Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84050

Leopold,  DA
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Wang, Z., Maier AV, Logothetis, N., Leopold, D., & Liang, H. (2006). Neural population activity in area MT improves determination of perceptual states. Poster presented at 36th Annual Meeting of the Society for Neuroscience (Neuroscience 2006), Atlanta, GA, USA.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CFF7-5
Abstract
The relationship between single neuronal activity in area MT and motion perception is a well studied phenomenon. Less is known about how larger fractions of neurons interact to produce a certain perceptual outcome. Here we asked whether pooling the responses of a large population of MT neurons with widely varying properties could improve the predictability of perceptual decisions during ambiguous visual stimulation. Two well trained rhesus monkeys indicated the perceived direction of rotation of bistable structure-from-motion (SFM) stimuli by pushing one of two levers. During this task, multi-channel intracortical recordings including single-unit activity (SUA), multi-unit activity (MUA), and local field potentials (LFP) were collected from area MT. We sorted the neural data according to the monkeys’ behavioral choices and utilized the measure of choice probability (Britten et al., 1996) to quantify the relationship between the different signals and perceptual report. We found that SUA, MUA and LFP all had a rather modest capability of predicting the monkeys’ perceptual report when considered in isolation. We developed optimal predictors for each type of neural signal by selecting the weight for each channel and combining the signals from multiple channels. We found that the combination of simultaneously collected data greatly improved the prediction accuracy of each of the signals. Furthermore, we found that by combining all these three types of neural signals from multiple channels, choice probability increased even further in a systematic way. The accuracy and statistical power of determining the monkeys’ perception increased with the number of channels as well as with the types of neural signals used for analysis. Our results demonstrate that simultaneous collection of multiple neural responses in area MT can be used to reliably determine perceptual states.