Gain control in insect olfaction for efficient odor recognition

Ramón Huerta

Institute for Nonlinear Science

UCSD

What is time and dynamics buying us for pattern recognition purposes?

One way to tackle it

1. Start from the basics of pattern recognition: organization, connectivity, etc..

2. See when dynamics (time) is required.

The goal

How does an engineer address a pattern recognition problem?

1. Feature extraction. For example: edges, shapes, textures, etc…

2. Machine learning. For example: ANN, RBF, SVM, Fisher, etc..

What is easy ? What is difficult?

1. Feature extraction: very difficult (cooking phase)

2. Machine learning: very easy (automatic phase)

Feature Extraction

High divergence-convergence ratiosfrom layer to layer.

Antennal Lobe (AL)Mushroom body (MB)

Antenna

Mushroom body lobes

Location of learning

How insects appear to do it

Machine Learning

Stage

Bad news

The feature extraction stage is mostly genetically prewired

Good news

The machine learning section seems to be “plastic”

Feature Extraction

Antennal Lobe (AL) Mushroom body (MB)

Antenna

Mushroom body lobes

Machine Learning

Stage

Spatio-temporal coding occurs here No evidence of time here

The basic question

Can we implement a learning machine with

• fan-in, fan-out connectivities,

• the proportion of neurons,

• local synaptic plasticity,

• and inhibition?

Huerta et al, Neural Computation 16(8) 1601-1640 (2004)

Marr, D. (1969). A theory of cerebellar cortex. J. Physiol., 202:437-

470.

Marr, D. (1970). A theory for cerebral neocortex. Proceedings of the

Royal Society of London B, 176:161-234.

Marr, D. (1971). Simple memory: a theory for archicortex. Phil.

Trans. Royal Soc. London, 262:23-81.

Willshaw D, Buneman O P, & Longuet-Higgins, HC (1969) Non-holographic associative memory, Nature 222:960

CALYX

Display Layer

IntrinsicKenyon Cells

PNs (~800) iKC(~50000) eKC(100?)

AL

MB lobes

Decision layer

ExtrinsicKenyon Cells

No learningrequired

Learningrequired

k-winner-take-all

Stage I: Transformation into a large displayStage II: Learning “perception” of odors

1y

0y

2y

3y

KCs coordinates

0x10

1x00

2x10 3x

11

Class 1 Class 2

1

0 1

0x

2x

3x

1x

AL coordinates

1y

0y

2y

3yw

Hyperplane:Connections from the

KCs to MB lobes

MB lobe neuron:decision

Odor classification

Odor 4

Odor 3

Odor 2

Odor 1

Odor N

Class 1

Class 2

Sparse code P

rob

abil

ity

of d

iscr

imin

atio

n

# of active KCs

Capacity for discriminating

We look for maximum number of odors that can be discriminated for different activate KCs,

Note: we use Drosophila numbers

KCn

# of active KCs

TO

TA

L #

OF

O

DO

RS

It has been shown both inLocust (Laurent)

and Honeybee (Menzel)

the existence of sparse code~1% activity

Narrow areas of sparse activity

Without GAIN CONTROL

There can be major FAILURE

Feature Extraction

Antennal Lobe (AL) Mushroom body (MB)

Antenna

Mushroom body lobes

Machine Learning

Stage

GAIN CONTROL

But nobody knows why

Evidence for gain control in the AL

•These neurons can fire up to100 Hz

•The baseline firing rate is 3-4Hz

Data from Mark Stopfer, Vivek Jayaraman and Gilles Laurent

Honeybee: Galizia’s group

•There seems to be local GABA circuits in the MBs.

•Locust and honeybee circuits are different:

Honeybee 10 times more inhibitory neurons than locust

Let’s concentrate on the locust problem:

How do we design the AL circuit such that it has gain control?

I

i

N

j

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j IIj

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fIfwfwtd

fd

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fd

E I

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1 1

1 1

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1

Mean field of 4 populations of neurons

inputreceivetheyiSE / inputreceivetheyiS I /

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)(1

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)(1

ESi

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tfS

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)(2

ESii

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We apply mean field

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j

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j EIj

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IfwfwS

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Define new set of variables 21 )1( xxgpNX IEIEE 21 )1( yygpNY IEIEI

To obtain the mean field eq.

YYgp

gpXFIY

gp

gpXFgpNY

XYFIYFgpNX

IEIEI

IIIIII

EIEI

IIIIIIEIEI

EEEEIEIEE

)1(

)1(

Where we use 0EEp

constIx ),(* We look for the condition such that

Whose condition is:

*1

*2

*1

*1

*2

*1 ,,

Y

I

IYYIIII

Y

E

EXXEIEI

dudF

FDgp

dudF

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with21

21)1(

,uu

uu du

d

du

dD and IYXYX EE *

1*2

IYXXYXY II *1

*2

This works if and are linear

EF IF

BUT!

The gain control depends only on the inhibitory connections

The excitatory neurons are not at high spiking frequencies orsilent, but but not very high (3-4) Hz. So0*

2 EYX

ceX

erfXa

XF XE

22 2/21

22)(

SIMULATIONS: 400 Neurons

The gain control condition from the MF can be estimated as 2/EIEIIIII pgpg

A few conclusions:

•Gain control can be implemented in the AL network

•It can be controlled by the inhibitory connectivity. The rest of the parameters are free.

Things to do:

I do not know whether under different odor intensities the AL representation is the same.

Thanks to • Marta Garcia-Sanchez• Loig Vaugier• Thomas Nowotny• Misha Rabinovich

• Vivek Jayaraman• Ofer Mazor• Gilles Laurent