Download presentation

Presentation is loading. Please wait.

Published byEzra Demming Modified over 2 years ago

1
Neural Computation Chapter 3

2
Neural Computation Outline Comparison of behavioral and neural response on a discrimination task –Bayes rule –ROC curves –Neyman Pearson Lemma Population decoding –Cricket cercal system –Monkey M1 motoneurons Optimal decoding –MAP and Bayesian estimates –Relation to population vector –Fisher information

3
Neural Computation Bayes’ rule Let s denote a stimulus and r=(r 1,…,r N ) denote the response of one or more neurons. We define –The stimulus probability p(s) –The response probability p(r) –The joint probability p(r,s) and conditional probabilities p(r|s) and p(s|r) Bayes rule:

4
Neural Computation Discrimination of movements Stimulus is moving dot pattern with variable % of coherently moving dots. Monkey behavioral forced choice task to report direction of motion (+ or -) as a function of coherence in the stimulus (filled circles) Monkey decides on basis of neural response. Open circles are optimal discrimination performance given the neural response data

5
Neural Computation Two alternative forced choice

6
Neural Computation

7
Optimal decision: ROC curve The classification requires the definition of a threshold. The threshold z affects the classification performance: –Define the false alarm rate (size) =p(r>z|s=-) and hit rate (power) =p(r>z|s=+) –ROC (‘receiver operating characteristic’) plot (z) vs. (z) Area under curve s d / classification performance: –½ is random guessing –1 is perfect classification

8
Neural Computation Two alternative forced choice Given a stimulus s=+ and the response in two neurons p+ and p-, That give rates r+ and r- respectively. Stimulus is classified by the highest rate. What is the probability of correct classification?

9
Neural Computation Two alternative forced choice White circles are p(correct) as a function of stimulus coherence Monkeys response is as if based on two alternative forced choice

10
Neural Computation Discriminability

11
Neural Computation Discriminability (details) Discuss ex. 3.1

12
Neural Computation Likelihood ratio

13
Neural Computation Neyman-Pearson Lemma

14
Neural Computation Neyman-Pearson Lemma

15
Neural Computation Neyman-Pearson Lemma

16
Neural Computation Cricket cercal system Consider response of a population of neurons p(r 1,…,r N |s) Consider a stimulus that is parametrized by a continuous value Cricket cercal system –Hair cells send spike when deflected by wind –4 inter-neurons receive input from thousands of hair cells

17
Neural Computation Cricket cercal system

18
Neural Computation Cricket cercal system

19
Neural Computation Monkey primary motor cortex (M1)

20
Neural Computation Monkey primary motor cortex (M1)

21
Neural Computation Optimal decoding

22
Neural Computation Optimal decoding

23
Optimal decoding details log p(r|s) \propto \sum_{a=1}^4 (r_a-f_a(s))^2 Assume f_a(s)=c_{ai} v_i Then log p(r|s) as a function of v has maximum at Sum_{ja} c_{ia} c_{ja} v_j = sum_a c_{ia} r_a This is the MAP estimate Bayesian estimate requires p(s|r) which is a normalized version of p(r|s) as a function of s. Neural Computation

24
Bayesian vs. Population vector decoding

25
Neural Computation Bayesian vs. Population vector decoding

26
Neural Computation Bayesian vs. Population vector decoding

27
Neural Computation Bias and variance

28
Neural Computation Bias and variance

29
Neural Computation Fisher information

30
Neural Computation

31
Fisher information

32
Neural Computation Fisher information

33
Neural Computation Fisher information

34
Neural Computation

36
S_est=sum_i a_i r_i sum_i a_i=1 r_i is unbiased estimator of s = sum_i a_i s= s Sigma^2_est=sum_i a_i^2 sigma^2 A_i =1/n -> Sigma^2_est = sigma^2/n optimal A_i different is suboptimal.

37
Neural Computation Fisher information

38
Neural Computation Fisher information

39
Neural Computation

40
Spike Train decoding

41
Neural Computation Spike Train decoding

42
Neural Computation Spike Train decoding

43
Neural Computation Summary Decoding of stimulus from response –Two choice case Discrimination ROC curves –Population decoding MAP and ML estimators Bias and variance Fisher information, Cramer-Rao bound –Spike train decoding

44
Neural Computation

Similar presentations

OK

1 / 41 Inference and Computation with Population Codes 13 November 2012 Inference and Computation with Population Codes Alexandre Pouget, Peter Dayan,

1 / 41 Inference and Computation with Population Codes 13 November 2012 Inference and Computation with Population Codes Alexandre Pouget, Peter Dayan,

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on uk economy Ppt on field study example Funny ppt on marriage Ppt on law against child marriage Ppt on radio network controller Ppt on time management skills Ppt on tamper resistant fasteners Ppt on nitrogen cycle and nitrogen fixation examples Ppt on natural resources water Ppt on classroom management