Overview

1. The Structure of the Visual Cortex

2. Using Selective Tuning to Model Visual Attention

3. The Motion Hierarchy Model

4. Simulation Results

5. Conclusions

A Hierarchical Neural Model for the Detection of Motion Patterns in Optical Flow Fields

“Data Flow Diagram”of Visual Areas inMacaque Brain

Blue:motion perception pathway

Green:object recognition pathway

Receptive Fields in Hierarchical Neural Networks

neuron A

receptive field of A

Receptive Fields in Hierarchical Neural Networks

receptive field of A in input layer

neuron Ain top layer

contextualinterference

poor localization

crosstalk

Problems with Information Routing in Hierarchical Networks

The Selective Tuning Concept (Tsotsos, 1988)

processingpyramid

inhibited pathways

passpathways:hierarchicalrestriction of input space

unit of interestat top

input

top-down, coarse-to-fine WTA hierarchy for selection and localization

unselected connections are inhibited

WTA achieved through local gating networks

Hierarchical Winner-Take-All

unit and connectionin the interpretive network

unit and connectionin the gating network

unit and connectionin the top-down bias network

B+1,k

U+1, k

I,k

-1,j

,k,jG

g,kb,k

M,k

I+1,x

}

layer +1

layer -1

layer

I

Selection Circuits

Two-Phase WTA for Region Selection

Phase 1:• distance-invariant• minimum difference in activation necessary

for inhibition• problem: possible split-up of attended

regions

Phase 2:• mutual inhibition grows with the distance

between units • only one coherent region is selected for

attention

3D Visualization of the Selective Tuning Network

Red: WTA phase 1 active

Green: WTA phase 2 activeBlue: inhibition Yellow: WTA winner

The Motion Perception Pathway

MST

MT

V1

feed- forward

feed- forward

feedback

input

feed- forward

feedback

feedback

What do We Know about Area V1?• cells have small receptive fields• each cell has a preferred direction of motion

direction of motion

act

ivati

on preferred direction

• there are three types of motion speed selectivity

speed of motion

act

ivati

on low-speed cells

medium-speed cells

high-speed cells

What do We Know about Area MT?• cells have larger receptive fields than in V1• like in V1, each cell has a preferred combination

of the direction and speed of motion• MT cells also have a preferred orientation of the

speed gradient

orientation of speed gradient

act

ivati

on

preferred orientation of speed gradient

without speed gradient

with speed gradient

What do We Know about Area MST?

• cells respond to motion patterns such as– translation (objects shifting positions)– rotation (clockwise and counterclockwise)– expansion (approaching objects)– contraction (receding objects)– spiral motion (combinations of rotation and

expansion/contraction)

• the response of a cell is almost independent on the position of the motion pattern in the visual field

The Motion Hierarchy Model: V1

• V1 receives optical flow patterns as input

counterclockwise rotationclockwise rotationcontractionexpansion

counterclockwise clockwise contraction expansion

The Motion Hierarchy Model: V1• V1 is simulated as 6060 hypercolumns• each column contains 36 cells: one for each

combination of direction (12) and speed tuning (3)• direction and speed selectivity are modeled with

Gaussian functions based on physiological data• the activation of a V1 cell is the product of its

activation by direction and its activation by speed• example: cells tuned towards upward motion:

input pattern: counter-clockwise rotation

high-speed cells

medium-speed cells

low-speed cells

The Motion Hierarchy Model: MT

• MT is simulated as 3030 hypercolumns• each column contains 432 cells: one for each

combination of direction (12) speed (3), and speed gradient tuning (12)

• problem: how can gradient tuning be realized from activation patterns in V1?– solution: detect gradient differences across

the three types of speed selective cells– this solution leads to a simple network

structure and remarkably good noise reduction

• the activation of an MT cell is the product of its activation by direction, speed, and gradient

The Motion Hierarchy Model: MT

• if the input is a counterclockwise rotation, these MT cells respond to – medium speed– leftward motion– upward speed gradient

MT

V1

• structure of input connections to MT cells:

The Motion Hierarchy Model: MST

• how can MST cells detect motion patterns such as rotation, expansion, and contraction based on the activation of MT cells?

counterclockwise clockwise contraction expansion

movement speed gradient

• idea: the presence of these motion patterns is indicated by a consistent angle between the local movement and speed gradient

The Motion Hierarchy Model: MST

direction of movement

orientation of speed gradient

The Motion Hierarchy Model: MST

• MST cells integrate the activation of MT cells that respond to a particular angle between motion and speed gradient

• this integration is performed across a large part of the visual field and across all 12 directions

• therefore, MST can detect 12 different motion patterns

• we simulate 55 MST hypercolumns, each containing 36 neurons (tuned for 12 different motion patterns, 3 different speeds)

The Motion Hierarchy Model: MST

“wiring” of MST cells tuned for clockwise rotation

MT motion direction tuning

MT s

peed

gra

die

nt

tun

ing

MST cells

Simulation:

clockwiserotation

Simulation:

counter-clockwiserotation

Simulation:recedingobject

Attention in the Motion Hierarchy

What happens if there are multiple motion patterns in the visual input?

Visual attention can be used to• determine the type and location of the most salient motion pattern,• focus on it by eliminating all interfering information,• sequentially inspect all objects in the visual field.

Attention in the Motion Hierarchy

Iterative application of the attentional mechanism:

Conclusions and Outlook

• the motion hierarchy model provides a plausible explanation for cell properties in areas V1, MT, and MST

• its use of distinct speed tuning functions in V1 and speed gradient selectivity in MT leads to a relatively simple network structure combined with robust and precise detection of motion patterns

• visual attention is employed to segregate and sequentially inspect multiple motion patterns

Conclusions and Outlook

• the model is well-suited for mobile robots to estimate parameters of self-motion

• the area MST in the simulated hierarchy is very sensitive to any translational or rotational self-motion

• in biological vision, MST is massively connected to the vestibular system

• in mobile robots, the simulated area MST could interact with position and orientation sensors to stabilize self-motion estimation