Neuroscience 2005 Abstract
| Presentation Number: | 619.14 |
|---|---|
| Abstract Title: | A computational relationship between the distributed response of MT neurons and fine direction discrimination. |
| Authors: |
Xu, H.*1
; Purushothaman, G.1
; Bradley, D.1
1Dept Psychol, Univ. of Chicago, Chicago, IL |
| Primary Theme and Topics |
Sensory and Motor Systems - Vision -- Processing of visual motion |
| Secondary Theme and Topics | Sensory and Motor Systems<br />- Vision<br />-- Neural coding |
| Session: |
619. Motion Processing III Poster |
| Presentation Time: | Tuesday, November 15, 2005 9:00 AM-10:00 AM |
| Location: | Washington Convention Center - Hall A-C, Board # Q3 |
| Keywords: | Choice probability, Decisions, Motion, Model |
Based on measurements of responses of a distributed population of middle temporal (MT) neurons made in macaques practicing a fine direction discrimination task, we previously reported that behavioral choices in that task had a stronger association with high precision neurons than with those of lower precision. Consistent with this observation, pooling schemes that up weighted the activities of high precision neurons were found necessary to account for psychophysical thresholds (SFN, 2003). We have now tried to understand how both the psychophysical performance and the distributed pattern of neuron-choice association observed in our experiment can be accounted for in a computational model. We used the recorded activities to construct a continuum of models with various pool sizes, different patterns of inter-neuronal correlations, and read-out mechanisms. We used both uniform read-outs that assume diffuse neural circuits unchanged by learning (e.g., Shadlen et al 1996) as well as models that adaptively learned the read-out weights through a physiologically and behaviorally constrained perceptual learning mechanism. The points of equilibrium for the learning mechanism are shown to consist of the optimal (Fisher) discriminant function for the weight pattern. The learned read-outs closely approximated the Fisher discriminant. Models that accounted for all the experimental data within 95% confidence intervals consisted of about 60-80 neurons with the activities of the high-precision neurons up-weighted. These models also accounted for human direction discrimination performance in an adaptation paradigm (Hol & Treue, 2001). Using these models, we predict hitherto unknown effects of adaptation on direction discrimination. We suggest that fine direction discrimination is supported by an efficient read-out of MT activities perhaps due to the mechanistic strengthening of connections between informative MT neurons and decision networks during the perceptual learning of the task.
Supported by NIH R01-EY013138
Sample Citation:
[Authors]. [Abstract Title]. Program No. XXX.XX. 2005 Neuroscience Meeting Planner. Washington, DC: Society for Neuroscience, 2005. Online.
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