Presentation on theme: "Neural population code for fine perceptual decisions in area MT Gopathy Purushothaman m M David C Bradley Image from: PLoS Journal Club # 4 September 28."— Presentation transcript:
Neural population code for fine perceptual decisions in area MT Gopathy Purushothaman m M David C Bradley Image from: PLoS Journal Club # 4 September 28 2005 Willie Buchser
Figure 3 Questions: Does preferred direction impact threshold? For Individual Neurons, we know: Preferred Direction Neurometric Threshold
0.4 0 0.2 0.1 10203040 50607080 900 Figure 3b Neural Precision Neurons Preferred Direction Moving average: every 4° within a 16° window. Neural Precision and Preferred Direction
0.4 0 0.2 0.1 10203040 50607080 900 Neural Precision Neurons Preferred Direction Figure 3c Direction Tuning Curve -60 -30 0 30 60 -90 90 20 Firing rate (Hz) 40 60 1.0 0.6 Slope (normalized) First Derivative of Tuning Curve
Figure 3 For a Population of Neurons, we know: The neurons with the best precisions had a particular preferred direction ~70° away from reference. Summary We still need to know about which neurons contribute to the decision.
Figure 4 Question: What neurons in the population are correlated with the decision? Choice Probabilities
Figure 4d,e r = 0.042, 99% CI 0.0300.054 F = 50, P < 0.00001 Choice probabilities
Figure 4 Some neurons are better at predicting the decision of the monkeys, even when the stimulus is almost ambiguous. Summary The neurons that are better at predicting decisions are also the most precise. The neurons that are best at predicting decisions have a preferred stimulus ~70° away from reference.
Figure 6a Model network for computing discrimination decisions.
Conclusions Neurons with preferred directions 6070° away from the reference exhibited the highest choice probabilities. They suggest that perception is dependent on the most precise neurons in the population. Nature Neuroscience 8, 12 - 13 (2005)Nature Neuroscience 8, 99 - 106 (2004) Lower-envelope Principle
Figure 5 Questions: Can we confirm the same results with a different computation Mutual Information Mutual information
Figure 5b This test rigorously showed that the correlation between the neuron's activity and decisions did not result spuriously from a correlation between the stimuli and decisions.
Figure 6b,c α = 1 Linear Pooling α = 2 Quadratic Pooling α = 3+ Higher Order Pooling Noise Variance (sum-square error) Threshold ratio (neural-pool/behaviour) Uniform, Non-selective Pooling (all the neurons tuned in all 90° on either side of the reference) Pool Size (Number of Neurons)
Figure 6d,e Emphasize neurons tuned 70° from reference Noise Variance (sum-square error) Threshold ratio (neural-pool/behaviour) Pool Size (Number of Neurons) α = 1 Linear Pooling α = 2 Quadratic Pooling α = 3+ Higher Order Pooling
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