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Neural population code for fine Neural population code for fine perceptual decisions in area MTperceptual decisions in area MT
Gopathy Purushothaman
m M David C Bradley
Image from: PLoS
Journal Club # 4September 28 2005
Willie BuchserWillie Buchser
Why?
Middle Temporal Area
MiddleTemporal
Area
VentralPosterior
Area
TertiaryVisual
Cortex (V3)
Part of the Primate“Extra-striate Cortex”
Human Brain: Purves Neuroscience: Sereno et al., 1995
Visual Information Flow
Monkey Brain
VisualStimulus
OccipitalLobe
StriateCortex
V1
V2MT
Dorsal Stream
Background – Sensory Neurons
Receptive Field
• 1 Neuron – Small amount of information
• Population Sensory Perception
Preferred Stimuli
Stimulus
Perception
Population-coding Scheme
All active neurons
contribute to perception.
Decision UnitPools all information
(Performs a summation)
Stimulus
Perception
Lower-envelope Principle
Only most sensitive neurons contribute to the
perception.
Question
What is the relationship between neural activity & perception
for the Middle Temporal Area?
Uniform, Non-selective Pooling
Lower-envelope Principle
Methods
Rhesus Monkey: The Early Years
Methods - Stimulus
Run Trial
Trial
11
CounterClockwise
Clockwise
Trial
11
Run Trial
Trial
22
CounterClockwise
Clockwise
Trial
22
Run Trial
Trial
33
CounterClockwise
Clockwise
Trial
33
Run Trial
Trial
44
CounterClockwise
Clockwise
Trial
44
Results
CounterClockwise
Clockwise
-3°
+2°
+5°
+9°11
22
33
44
Figure 1b
PsychoMetric
Question:
What is the behavioral threshold for discriminating fine direction differences?
↓ Threshold = ↑ Precision
Figure 1b
1
0.8
0
0.2
0.4
0.6
0 1 2-3 -2 -1 3
PsychoMetric
M
% C
lock
wis
e C
hoic
e
Degrees from Reference
80% Confidence
Chance
Psychometric Threshold = 1.7°
Fine Direction-Discrimination Task
Figure 2aNeuroMetric
Questions:
• How do these neurons respond to different directions?
• How well does a particular neuron predict direction?
Figure 2a
-60
-300
30
60
-90 90
Direction Tuning Curve NeuroMetric
20 spikes/s
40 60
50% 70%
ref test
Neuron with a preferred direction of about 60°
0.4
0.6
0.8
-3 -2 -1 0 1 2 30
0.2
1
Figure 2b
NeuroMetric
4 5 6 7 8 9
80% Confidence
Finding Neurometric Threshold
Neurometric Threshold = 7.4°
PsychoMetric
Psychometric Threshold = 0.8°
% C
lock
wis
e C
hoic
e
Degrees from Reference
Figure 3
Questions:
• Does preferred direction impact threshold?
For Individual Neurons, we know:• Preferred Direction• Neurometric Threshold
0.4
0
0.2
0.2
0.1
10 20 30 40 50 60 70 80 900
Figure 3b
Neu
ral P
reci
sion
Neuron’s Preferred Direction
Moving average: every 4° within a 16° window.
Neural Precision and Preferred Direction
0.4
0
0.2
0.2
0.1
10 20 30 40 50 60 70 80 900
Neu
ral P
reci
sion
Neuron’s Preferred Direction
Figure 3cDirection Tuning Curve
-60
-300
30
60
-90 9020
Firingrate (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.
Choice Probabilities
Ambiguous Stimulus
1
0.5
0
Neuron Decision ChoiceProbability
Figure 4
Question:
• What neurons in the population are correlated with the decision?
Choice Probabilities
Figure 4d,e
r = 0.042, 99% CI 0.030−0.054 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 60−70° 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
Finished
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 Poolingα = 2 Quadratic Poolingα = 3+ Higher Order Pooling
Noi
se V
aria
nce
(sum
-squ
are
erro
r)
Thr
esho
ld r
atio
(neu
ral-p
ool/b
ehav
iour
)
Uniform, Non-selective Pooling
(all the neurons tuned in all 90° on either side of the reference)
Pool Size (Number of Neurons)
Figure 6d,eEmphasize neurons tuned 70° from reference
Noi
se V
aria
nce
(sum
-squ
are
erro
r)
Thr
esho
ld r
atio
(neu
ral-p
ool/b
ehav
iour
)
Pool Size (Number of Neurons)
α = 1 Linear Poolingα = 2 Quadratic Poolingα = 3+ Higher Order Pooling