Neural Models of Bayesian Belief Propagation Rajesh ... - Washington

262 11 Neural Models of Bayesian Belief Propagation Rajesh P. N. Rao
A
Up
Lt 1 2 3 4 5 Rt
Dn
0.8
0.6
0.4
0.2
0
Lt
1 2 3 4 5
Spatial Positions
0.8
0.6
0.4
0.2
0
Rt
1 2 3 4 5
B
0.8
0.6
0.4
0.2
0
1 2 3 4 5
0.8
Up Dn
0.6
0.4
0.2
0
1 2 3 4 5
Figure 11.8 Input Image Configuration and Conditional Probabilities used in the Attention
Experiments. (A) Example image locations (labeled 1-5 and Up, Dn, Lt, and Rt
for up, down, left, and right) relevant to the experiments discussed in the paper. (B)
Each bar plot shows P (Ci|L, F ) for a fixed value of L (= Lt, Rt, Up, or Dn) and for an
arbitrary fixed value of F . Each bar represents the probability for the feature-location
combination Ci encoding one of the locations 1-5.
Response
0.5
0.3
Model V4 Neuron
0.0
−90 −60 −30 0 30 60 90
Orientation (in degrees)
A B
Figure 11.9 Multiplicative Modulation due to Attention. (A) Orientation tuning curve
of a feature coding model neuron with a preferred stimulus orientation of 0 degrees
with (filled squares) and without (unfilled circles) attention (from [31]). (B) Orientation
tuning curves of a V4 neuron with (filled squares) and without attention (unfilled
circles) (from [23]).