Neural Population code for fine perceptual decisions in area MT

Sang-il, KimVNI LAB

Gopathy Prushothaman & David C Bradley

요약• 질문 : 자극에 의해 활동하는 모든 neuron 이 perception

에 기여하는가 ? 아니면 그 중 특별한 subgroup 이 기여하는가 ? – Population-coding scheme vs. Lower-envelope scheme

• 대상 : Macaca Mulatta 원숭이 (2 마리 ) 의 MT 영역의 neuron 240 개

• 방법 : electrophysiology• Task: fine direction-discrimination task• 분석 : neural activity 와 decision 의 상관관계를 계산해서

활동하는 각 neuron 의 기여도 측정– Choice probability & mutual information

과제 설명

• stimulus: random dot kinematogram(RDK)

• reference stimuli : moving upward

• test stimuli : -3 degree(CCW) ~ +3 degree(CW) relative to reference stimuli

• average threshold : 1.7 degree

• neurometric vs. psychometric

1sec 1sec0.5sec response

Ideal observer analysis

• Quantifying discrimination performance using ideal observer analysis– 한 neuron 의 preferred direction 이 CW– FR for CW test > FR for reference > FR for CCW test– Ideal observer 는 neuron 의 FR 을 보고 test stimuli 가 어떤

것이었는지 예상가능

0

)/()/( drREFrpdsCWspr

Ideal observer 가 CW 대답을 할 확률

= test interval 의 FR 이 REF 에서의 FR 보다 클 확률

229 neuronsaverage ratio : 26+-2.2 • a. Just Upward component

• b. Relative precision as a function of the neuron’s preferred direction relative to the reference

• relative precision

= Psychometric / neurometric

• Precisions(60~80 degree) significantly differ from the rest

C. The average neuron whose preferred direction was about 67 degree CW or CCW away from the stimulus direction had one of steepest parts of its tuning curve near the stimulus direction.

• Sharp change in the tuning curve -> large difference between test & reference , low discrimination threshold

• Slope 가 가파르게 변하면 precision 도 증가• Regression : r=0.46

Covariation of neural response and Monkey’s choice

• Q. neural population 반응의 어떤 part 가 decision 과 유의미하게 상관있는가 ?

• Scenario 1 : population coding scheme– Decision covary w/ activities of each neurons to the s

ame degree

• Scenario 2 : lower-envelope scheme– Small group of neurons exhibit larger degree of covaria

tion

• Using “choice probability” & “mutual information”

Choice probability

• is the average accuracy with which ideal observer can predict the monkey’s choice in trial using neuron’s response– 0.5 -> no predictive power – 1.0 -> accurately predict monkey’s choice

• Example– CW preference, direction difference is small ->

reference and test response of this neuron must be about the same. But fluctuation exist.

– Random Reference FR ↓ or test FR↑ -> CW answer

• Compute CP in 3 ways...

1. Test FR only• For each neuron -> 2 histogram(CW choice & CCW c

hoice)• average CP is 0.55(more than chance)• implying these neurons were associated with monke

y’s discrimination decision• CP of neurons within 30 degree of reference directio

n is 0.51(chance level)2. Reference FR only

• average CP is 0.52(more than chance)3. Both

• average CP is 0.524. Regression

• Subtle but significant (+) correlation• Higher precision neuron -> greater predictive power

Test only

regression

Test & reference

Reference only

Neural precision 이 증가할수록 CP 도 증가

Neural precision 은 tuning curve 의 slope 가 증가할수록 증가

Tuning curve 의 slope 는 reference 에 비해 상대적인 preferred direction 이 67도 근처일 때 최고

Mutual information

• H(C)=1bit: entropy of choice, H(C/FR)=0: the conditional entropy of the choice given FR

• Small differences in FR convey little info.• is the average info. gained about a monkey’s

choice from knowledge of the FR• I(FR,C)=H(C)-H(C/FR), I(C,FR)=H(FR)-H(FR/C) : • Population-average mutual information

=0.34+-0.01bit (significantly greater than 0)

Mutual info. histogram calculated from reference & test firing rate from the ambiguous trials only

0.34 bits

• Calculated only from direction difference 0

• Direction difference 0.125 ->

info conveyed by stimuli < info conveyed by neuron

• neuron 과 판단의 상관관계가 자극과 판단의 상관관계에서 나온게 아니다 . -> MT 에 있는 다른 뉴런들이 보내는 정보의 양에 근본적인 차이가 있다 .

자극과 판단 사이의 MI

comparison

• A generic discrimination model (CW, CCW pool)

• vi : Scale factor, weighting• a : pooling non-linearity• 3 weighting scheme used (expone

ntial, Cauchy-like, Gaussian-like)• Varying pool size• vi=1 for all i (broad, non-selective)• vi=1, vj=0 (lower-envelope principl

e)• 모든 neural response 는 0.12 의

correlation 으로 sampling• 각 direction difference 에 대해 1

5 회 시행 simulation • 이걸 100 번 반복

/12/

1

/12/

1

)()(

N

iii

N

iii CCWvCWvD

CW CCW

• (B, C) – large population, low non-linearity, equal-weight , BUT 3-times less precise than psychophysical performance

• high non-linearity, asymptotically approach to psychophysical performance, BUT residual error increase

• (D, E) – assign higher weights to activities of neurons at the peak(70 degree)

• high non-linearity, residual error NOT increase

Discussions• Single neuron precision for fine

discrimination– Poor ideal observer performance -> due to

sampling procedure– average threshold ratio 26 vs. 1.0(coarse

discrimination task)– Most sensitive neuron 은 monkey 보다 10 배

sensitive -> not only sampling procedure– Other factors: explanation of small part of

difference -> Maybe, MT neuron have inherently poor capacity for fine discrimination, broad width of MT direction-tuning curve

– V1(40, 1~2) vs. MT(100)– Direction-discrimination threshold : 7.5, mean

threshold : 35 vs.

• average CP for test response(0.55) > average CP for reference response(0.52) ->due to noise during storing & retrieval

• Pooling efficiency of decision network– Modeling : 4~8 neuron sufficient– Low CP(0.55) : 70~100 neuron involving to

decide– 3 explanation proposed– Single MT neuron have lower precision than

monkey -> pooling is necessary (reaction time motion detection task)

• result show – Selective pooling is necessary– Decision are associated the activities of high-

precision MT neurons than those of low-precision neurons