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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.

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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:

1 Neural population code for fine perceptual decisions in area MT Gopathy Purushothaman m M David C Bradley Image from: PLoS Journal Club # 4 September Willie Buchser

2 Why?

3 David C. Bradley I approve this journal club.

4 Middle Temporal Area Middle Temporal Area Ventral Posterior Area Tertiary Visual Cortex (V3) Part of the Primate Extra-striate Cortex Human Brain: Purves Neuroscience: Sereno et al., 1995

5 Visual Information Flow Monkey Brain Visual Stimulus Occipital Lobe Striate Cortex V1V1 V2V2 MT Dorsal Stream

6 Background – Sensory Neurons Receptive Field 1 Neuron – Small amount of information Population Sensory Perception Preferred Stimuli

7 Stimulus Perception Population-coding Scheme All active neurons contribute to perception. Decision Unit Pools all information (Performs a summation)

8 Stimulus Perception Lower-envelope Principle Only most sensitive neurons contribute to the perception.

9 Question What is the relationship between neural activity & perception for the Middle Temporal Area? Uniform, Non-selective Pooling Lower-envelope Principle

10 Methods Rhesus Monkey: The Early Years

11 Methods - Stimulus

12 Trial1

13 Counter Clockwise Trial1

14 2

15 Counter Clockwise Trial2

16 3

17 Counter Clockwise Trial3

18 4

19 Counter Clockwise Trial4

20 Results Counter Clockwise -3° +2° +5° +9°

21 Figure 1b PsychoMetric Question: What is the behavioral threshold for discriminating fine direction differences? Threshold = Precision

22 Figure 1b PsychoMetric M % Clockwise Choice Degrees from Reference 80% Confidence Chance Psychometric Threshold = 1.7° Fine Direction-Discrimination Task

23 Figure 2a NeuroMetric Questions: How do these neurons respond to different directions? How well does a particular neuron predict direction?

24 Figure 2a Direction Tuning Curve NeuroMetric 20 spikes/s %70% reftest Neuron with a preferred direction of about 60°

25 Figure 2b NeuroMetric % Confidence Finding Neurometric Threshold Neurometric Threshold = 7.4° PsychoMetric Psychometric Threshold = 0.8° % Clockwise Choice Degrees from Reference

26 Figure 3 Questions: Does preferred direction impact threshold? For Individual Neurons, we know: Preferred Direction Neurometric Threshold

27 Figure 3b Neural Precision Neurons Preferred Direction Moving average: every 4° within a 16° window. Neural Precision and Preferred Direction

28 Neural Precision Neurons Preferred Direction Figure 3c Direction Tuning Curve Firing rate (Hz) Slope (normalized) First Derivative of Tuning Curve

29 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.

30 Choice Probabilities Ambiguous Stimulus NeuronDecision Choice Probability

31 Figure 4 Question: What neurons in the population are correlated with the decision? Choice Probabilities

32 Figure 4d,e r = 0.042, 99% CI F = 50, P < Choice probabilities

33 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.

34 Figure 6a Model network for computing discrimination decisions.

35 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, (2005)Nature Neuroscience 8, (2004) Lower-envelope Principle

36 Finished

37 Figure 5 Questions: Can we confirm the same results with a different computation Mutual Information Mutual information

38 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.

39 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)

40 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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