N eural Population code for fine perceptual decisions in area MT

1. Neural Population codefor fine perceptual decisions in area MT Gopathy Prushothaman & David C Bradley Sang-il, Kim VNI LAB

2. 요약 • 질문: 자극에 의해 활동하는 모든 neuron이 perception에 기여하는가? 아니면 그 중 특별한 subgroup이 기여하는가? • Population-coding scheme vs. Lower-envelope scheme • 대상: Macaca Mulatta 원숭이(2마리)의 MT영역의 neuron 240개 • 방법: electrophysiology • Task: fine direction-discrimination task • 분석: neural activity와 decision의 상관관계를 계산해서 활동하는 각 neuron의 기여도 측정 • Choice probability & mutual information

3. 과제 설명 1sec 0.5sec 1sec response • 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

4. Ideal observer analysis • Quantifying discrimination performance using ideal observer analysis • 한 neuron의 preferred direction이 CW • FR for CW test > FR for reference > FR for CCW test • Ideal observer는 neuron의 FR을 보고 test stimuli가 어떤 것이었는지 예상가능 Ideal observer가 CW대답을 할 확률 = test interval의 FR이 REF에서의 FR보다 클 확률

6. 229 neurons average 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.

7. Sharp change in the tuning curve -> large difference between test & reference , low discrimination threshold • Slope가 가파르게 변하면 precision도 증가 • Regression : r=0.46

8. Covariation of neural response and Monkey’s choice • Q. neural population 반응의 어떤 part가 decision과 유의미하게 상관있는가? • Scenario 1 : population coding scheme • Decision covary w/ activities of each neurons to the same degree • Scenario 2 : lower-envelope scheme • Small group of neurons exhibit larger degree of covariation • Using “choice probability” & “mutual information”

9. 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...

10. Test FR only • For each neuron -> 2 histogram(CW choice & CCW choice) • average CP is 0.55(more than chance) • implying these neurons were associated with monkey’s discrimination decision • CP of neurons within 30 degree of reference direction is 0.51(chance level) • Reference FR only • average CP is 0.52(more than chance) • Both • average CP is 0.52 • Regression • Subtle but significant (+) correlation • Higher precision neuron -> greater predictive power

11. Reference only Test only Test & reference regression Neural precision이 증가할수록 CP도 증가 Neural precision은 tuning curve의 slope가 증가할수록 증가 Tuning curve의 slope는 reference에 비해 상대적인 preferred direction이 67도 근처일 때 최고

12. 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)

13. 0.34 bits Mutual info. histogram calculated from reference & test firing rate from the ambiguous trials only • 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. -> MT에 있는 다른 뉴런들이 보내는 정보의 양에 근본적인 차이가 있다. 자극과 판단 사이의 MI

14. comparison • A generic discrimination model (CW, CCW pool) • vi : Scale factor, weighting • a : pooling non-linearity • 3 weighting scheme used (exponential, Cauchy-like, Gaussian-like) • Varying pool size • vi=1 for all i (broad, non-selective) • vi=1, vj=0 (lower-envelope principle) • 모든 neural response는 0.12의 correlation으로 sampling • 각 direction difference에 대해 15회 시행 simulation • 이걸 100번 반복 CW CCW

15. (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

16. 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 neuron은 monkey보다 10배 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.

17. 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