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?

David C. Bradley

I approve this

journal club.

bradley@uchicago.edu

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

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11

CounterClockwise

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11

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22

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22

Run Trial

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Results

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-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

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0 1 2-3 -2 -1 3

PsychoMetric

M

% C

lock

wis

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hoic

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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°

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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

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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

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nce

(sum

-squ

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erro

r)

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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