Reinagel lectures 2006

Take home message about LGN1. Lateral geniculate nucleus transmits information from retina to cortex2. It is not known what computation if any occurs in the LGN3. For white noise stimuli, responses are precise and reliable4. PRECISION is the trial to trial jitter in spike TIMING (order 1msec)

feed forward inhibition may be the mechanism of precise timing5. RELIABILITY is the trial to trial variability in spike NUMBER (subpoisson)

refractoriness may be the mechanism of reliable spike count6. BURSTING in the LGN is a distinct biophysical phenomenon, of unknown

importance. The *right* question to ask is whether the burstingstate is visually primed and whether priming itself encodes information

7. We now have a visually behaving rodent prep to address all these questions

Take home message about efficient coding1. Natural scenes are full of spatial and temporal correlations2. This suggests WHY center-surround RF's are GOOD: redundancy reduction2. Test: LGN responses to natural scenes are decorrelated (whitened)3. More generally: are natural scenes optimal stimuli?

is this even the right question?

www-biology.ucsd.edu/labs/reinagel/

LGN

Retina

Cortex

Ramon y Cajal

Hubel 1960 (alert cat)Hubel & Wiesel 1961 (anesthetized)

Lateral Geniculate Nucleus

• spiking inputs• intrinsic properties• local circuits• cortical feedback

Gating? Attention? Binding? Prediction testing? Nothing?

What happens in the LGN?

LGN

Retina

Cortex

Reinagel & Reid 2000

LGN response to purely temporal stimuli

Lum

inance

Repeat

Reinagel & Reid, 2000

Descriptive questions:• how precise is the timing?• how reliable is the number?• are there internal patterns?

In each case:• visual information?• mechanism of encoding?• mechanism of decoding?

PSTH peaks are milliseconds wide

Reinagel & Reid, 2002

1500 1600 1700 1800 1900 20000

0.5

A

0.2 0.1 -0.1 0.6 0.9 0.20.2

-0.90.3

-0.3-0.1

PS

TH

Norm'dt

Time (ms)

Reinagel & Reid 2002

Temporal patterns conserved across animals

Precision of spike times used (ms)

0.5 1 2 4 8 16 32 64 1280

50

100

Mu

tual In

form

ati

on

(b

its/

s)a b c de

Temporal precision of visual information

Theory of Shannon, 1948Method of Strong et al., 1998Result of Reinagel & Reid, 2000

Mechanisms Underlying Precise Timing

Pouille & Scanzian 2001

Mean 4Variance 0

Mean 4Variance 4

Deterministic Poisson

Spike Count: Trial to Trial Variability

Variance in Spike #Mean Spike #

Random Deterministic(Poisson)

= 1 = 0

Measure of variability

PSTH

LGN vs. Poisson Model

PSTH

0 100 500 10000

0.25

0.5

0.75

1

bin size T (msec)

Fano F

act

or

LGN Variability << Poisson

LGN

Poisson

Variability increases from retina to cortexF

ano

Fa

cto

r a

t ~ 4

0 H

z

0

1

RGC LGN V1

Kara, Reinagel & Reid, 2000

FF

FiringRate

200

0

0

1

RGC LGN V1

0 500

Time (ms)

When firing rate is high, variability is low

Kara, Reinagel & Reid, 2000

Refractoriness Regularizes?

PSTH

Poisson model

Poisson withRefractory Period

0 10 20 30 40 50 60 70 80 90 100

prob

abili

ty

ISI

data

Estimating refractoriness from data

Method:Berry & Meister1998

model

0 5 10 15 20 25 30 350

0.5

1

time since last spike (ms)

reco

very

fun

ctio

nRecovery Function

absolute andrelativerefractoriness

0 500 10000

100

200

300

400

500

time (ms)

firin

g ra

te (

sp/s

)

observed

free

Free Firing Rate

Refractory models for all cell types

0

1

2

Fan

o F

acto

r

0 200 0

Time (ms)

V1LGNRGC

Kara, Reinagel & Reid, 2000

Variability increases from retina to cortexF

ano

Fa

cto

r at

~ 4

0 H

z

0

1

RGC LGN V1

Kara, Reinagel & Reid, 2000

Refractoriness decreasesfrom retina to cortex

Rec

over

y F

unct

ion

0 10 20 30

Time (ms)

0.0

1.0

0.5

V1

RGC

Kara, Reinagel & Reid, 2000

Summary of Reliability

• Spike count has sub-Poisson variability

• High FR High Reliability

• Refractoriness completely explains

• Noise is low, but doubling each synapse

- firing rate is decreasing

- refractoriness is decreasing

Thalamic Bursts (It)

Jahnsen and Llinas (1984)

Hubel and Wiesel (1961)

Bursting in the LGN

• dominate during sleep, when vision is suppressed

• frequent under anesthesia, when vision is absent

• almost never seen in alert animals, when vision is happening

ERGO

Bursts are irrelevant to vision

• not rhythmic or synchronous in anesthetized animals

• visual in anesthetized animals

• synapses prefer bursts

• do occur in alert animals, and rare signals can be important

• cool computational ideas

ERGO

Bursts are crucial to vision

-0.6 -0.4 -0.2 0

-0.1

0

0.1

0.2

Time before spike (s)

Before a burst

Before a tonic spike

Optimal Guess of Stimulus

Visual inputs trigger bursts

0

0.5

1

1.5

Bit

s/event

0

0.05

0.1

0.15

0.2

Codin

g E

ffici

ency

Burst BurstTonic Tonic

Reinagel, Godwin, Sherman & Koch 1999

Bursts: Triggering vs. Priming

time

active

inactiveLT-C

a+

+

channel

state

A

P

tim

es

Tri

gger

synapti

cin

put •• • ••• • •

*Ca

++

spik

e

observable

Bursts in LGN are distinct code words

Denning & Reinagel 2005

Alitto, Weyand & Usrey 2005 Lesica & Stanley 2004

Summary: Bursting• LGN neurons have 2 states

• Visual inputs trigger responses in both states

• Visual inputs also control the state

BUT All this is under anesthesia

What about alert?

- Stimulus ensemble matters

- Behavioral state may also

- Triggering and priming

• spiking inputs • intrinsic properties • local circuits • cortical feedback

What happens in the LGN?

Directions

• Do bursts occur and are they visual in alert animals?

• Function of cortical feedback to the LGN?

• Does precision in the LGN matter for perception?

Flister, Meier , Conway & Reinagel (unpub)

An awake behaving rodent prep for vision

Thanks to collaborators at CSHL

Bursts in LGN in the awake, behaving rat

Flister, Meier & Reinagel (unpub)

[break]

Center Surround Opponent RFs

Kuffler 1958

Natural scenes are spatially correlated

Spatial correlations in unnatural images

Natural Image

-40 -20 0 20 400.2

0.4

0.6

0.8

1

distance

Correlation

100

10210

-2

100

102

Power spectrum

cycles/degree

Spatial correlation in natural images

(cf. Field 1987; Tadmore & Tolhurst; Ruderman & Bialek; van Hateren)

Natural Image

-20 0 200

0.2

0.4

0.6

0.8

1

Distance (pixels)

Correlation

10-2

100

102

10-2

100

102

104

Power spectrum

Spatial frequency

-20 0 200

0.2

0.4

0.6

0.8

1

10-2

100

102

10-2

100

102

104

(cf. Barlow 1961)

0 1 2 3 410-2

10-1

100

time (s)

lum

ina

nce

-1 0 10

0.2

0.4

0.6

0.8

1

Distance (sec)

Correlation

10-1

100

101

102

103

10-6

10-4

10-2

100

102

Power Spectrum

Temporal frequency (Hz)

Natural temporal stimulus

(cf. Dong & Atick 1995; van Hateren 1997)

0 1 2 3 410

-2

10-1

100

time (s)

lum

ina

nce

-2 -1 0 1 20

0.2

0.4

0.6

0.8

1

distance (sec)

Correlation

10-1

100

101

103

Power Spectrum

10-5

100

102

Temporal frequency (Hz)

(cf. Dan Atick & Reid 1996)

+

Barlow 1961 Redundancy Reduction Hypothesis

+

Sensory neurons decorrelate natural inputs to reduce redundancy

Dan, Atick & Reid 1996

Dan, Atick & Reid 1996

Whitening in the fly

Van Hateren 1997

Summary: Redundancy Reduction

• Shannon 1948: Optimal codes lack redundancy

• Kuffler 1958: Center-surround receptive fields in retinaHubel 1960: Center-surround RFs in LGN

• Barlow 1961: Center-surround RFs reduce redundancy for natural scenes

• Dan, Atick & Reid 1996: Responses in LGN are less redundant for natural scenes

white natural0

50

100

150

200

250

300A

Info

rma

tion

(b

its/s

)

white natural0

1

2

3

4

5

6

7B

Info

rma

tion

(b

its/s

pk)

white natural0

0.2

0.4

0.6

0.8

1C

Eff

icie

ncy

(b

its/b

it)

Bullfrog Auditory Neuron: Natural Stimulus is ‘Optimal’

Rieke, Bodnar & Bialek 1995

Cat LGN Neuron: Opposite result

white natural0

20

40

60

80

Info

rma

tion

(bits

/s)

A

white natural0

0.5

1

1.5

2

2.5

3

Info

rma

tion

(bits

/spk

)

B

white natural0

0.1

0.2

0.3

0.4

0.5

0.6

Eff

icie

ncy

(bits

/bit)

C

Analog LED Stimuli

Reinagel & Reid, in prep.

10 0 101 102 103101

102

103

104

105

temporal frequency (Hz)

Po

we

rLGN Responses to full field temporal stimuli

Natural visual stimulus

Random visual stimulus

(replication ofDan et al 1996)