A computational Approach to Behavior - Machines · California gull vs. alkali fly (Larus...

A computational Approach to Behavior

Pietro Perona California Institute of Technology

26 March 2017 AAAI Symposium on

``Computational Principles of Natural and Artificial Intelligence’’ Stanford University

California gull vs. alkali fly (Larus californicus vs Ephydra hians)

Mono Lake, CA 37°58’50.6”N, 119°06'33.4"W

environment

development, regulation

decision, control

perception learning

natl. selection gene expression

genes

behavior

brain+ body

environment

construction

decision, control

perception learning

analysis of performance

design

behavior

HW+ SW

What is `behavior’?

6

Zidane in a dish

Courtship in Drosophila

Marla B. SokolowskiNature Reviews Genetics 2, 879-892

(2001)

Phenomenology: many scales

1 frame

1 second

1 minute

1 hour

1 day

10 pix 100 pix 1000 pix

mov

emes

actio

nsac

tiviti

es

step reach glance

walk

hike

have drink

dine flirt

court

write `A’open door

Parametrization

[Eyjolfsdottir, 2014]

Ethograms

[Dankert et al., Nature Methods, April 2009]

by hand it would have taken (20+24+10)*1/3 * 3 * 7 = 400h

Levels of enlightenment

• Description

• Phenomena

• Mechanisms

FINDING AGGRESSION NEURONS IN THE FLY

X

warmth-sensitiveion channel

hyperaggressive?

LIBRARIES OF Drosophila STRAINS WITH DIFFERENT GENETICALLY LABELED POPULATIONS

OF NEURONS

⨁⨁

⨁⨁

⨁⨁

27oC

X=ion channel

gene

Analysis by synthesis

[Braitenberg, 1984]

images, trajectories

pose, movemes, actions, activities, objects, scenes

plans, goals, behavior, relationships ...

group-level goals and plans

individual goals and plans

motor programs

sensor-based control

IMAGING,TRACKING

RECOGNITION

COGNITION

SOCIAL NETWORK

PREFRONTAL CORTEX

MOTOR CORTEX, BASAL GANGLIA etc.

SPINAL CORD

SENSO

RYR

EPRESEN

TATIO

NA

LM

OTO

RPL

AN

NIN

GAction Perception

World

INDIVIDUAL

interaction, cooperation, competition

SOCIAL INTERACTION

Levels of analysis• Description

• Phenomena

• Grammar

• Purpose

• Mechanisms

• Performance

• Ontogeny

• Phylogeny

From phenomena to mechanisms:

a simple example

[Branson et al. Nature Methods, Jun. ’09]

[Branson et al. Nature Methods, Jun. ’09]

‘T-Stops’

‘X-Stops’

Wang et al. 2003.

An engineers’ solution

Try implementing it with 105 neurons

http://www.news.wisc.edu/newsphotos/images/fruit_fly_research03_6801.JPG

Regressive motion

↵�

�

[Zabala ’12]

Experiment

[Chalupka ’15]

Model

Regressive Looming

Levels of enlightenment• Description - fly trajectories

• Phenomena - stops, chases, … (actions)

• Grammar - T-stops,X-stops, (interactions)

• Purpose - Avoid predation and collisions

• Mechanisms - regressive motion(implem.?)

• Performance - Mobility,safety

• Ontogeny - Learning??

• Phylogeny - ??

Mechanism inference

[Eyjolfsdottir 2016,2017]

Fly-centric features

v: sensory inputx: motor control

len

w-len

w-ang

fwd

sideyaw

flieswalls

reproduce

eat fight

escapeh

v

x

h1i^

h2i+1^h2i

^

h1i+1^

xi+1^

h2i-1^

h1i-1^

xi-1^ xi

^

generative

Model architecture

Simulation:- prediction as input- compute vi on the fly

… …

h1i+1

h2i+1

vi-1 xi-1

vi xi

vi+1 xi+1

h1i

h2i

h1i-1

h2i-1

yi

yi^discriminative

h2i+1^

h1i+1^

xi+1^

vi xi

h1i

h2i

yi

yi^

yi

yi^

[Eyjolfsdottir 2016,2017]

Sanity check: learning on a synthetic fly

Control laws:1) walk forward with small randomness

2) rotate when close to boundary(away from higher visibility)

3) walk towards obstacle, when visible

4) extend wing when half way towards it

5) rotate when close to obstacle(alternate between left and right)

6) repeat from 1)

obstacle visible

Can the model learn generative control laws?

SynthFly Simulation

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r r rl l l l l l l

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0 100 200 300 400 500 600 700 800 900 1000-1

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r r rl l l l l l l

0r r rl l l l l l l

100

0

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0

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-1

-1

1

distance to object

r l r l r l r l r l r l r l r l r

left/right wing angle

hidden unit 91

hidden unit 175

synthetic fly simulation

time timeturn

Interesting units

Classification

Discovery

Discovery: FlyBowl

tSNE dimensionality reductionfemale male right wing extension left wing extension

hli: hidden states of unsupervised model

Discovery: IAM-OnDB

stroke length writer identity

Prediction

Simulation

Simulation: FlyBowl

fly visionchamber vision

simulated real

Simulation: FlyBowl

Simulation - handwriting

“e”

“s”

“r”

generated text character injectionsoriginal text “activate”

Summary• Brains, behavior and intelligence

• Description

• Phenomena

• Mechanisms

• Relationship with AI?

Collaborators

Kristin Branson

HeikoDankert

David Anderson Michael Dickinson

+Carlos Gonzalez

Shay OhayonRoian Egnor

Michael MaireJohn BenderTim LebestkyAlice Robie

Erik Hoopfer

Xavi Burgos

PiotrDollar

EyrúnEyjólfsdóttir

CristinaSegalin

Grant van Horn