Reinagel lectures 2006
Take home message about LGN
1. Lateral geniculate nucleus transmits information from retina to cortex
2. It is not known what computation if any occurs in the LGN
3. For white noise stimuli, responses are precise and reliable
4. PRECISION is the trial to trial jitter in spike TIMING (order 1msec)
feed forward inhibition may be the mechanism of precise timing
5. RELIABILITY is the trial to trial variability in spike NUMBER (subpoisson)
refractoriness may be the mechanism of reliable spike count
6. BURSTING in the LGN is a distinct biophysical phenomenon, of unknown
importance. The *right* question to ask is whether the bursting
state 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 coding
1. Natural scenes are full of spatial and temporal correlations
2. This suggests WHY center-surround RF's are GOOD: redundancy reduction
2. 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/
Lateral Geniculate Nucleus
Retina
LGN
Cortex
Ramon y Cajal
Hubel 1960 (alert cat)
Hubel & Wiesel 1961 (anesthetized)
What happens in the LGN?
• spiking inputs
• intrinsic properties
• local circuits
• cortical feedback
Gating? Attention? Binding? Prediction testing? Nothing?
Retina
LGN
Cortex
Reinagel & Reid 2000
Repeat
Luminance
LGN response to purely temporal stimuli
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?
Reinagel & Reid, 2000
PSTH peaks are milliseconds wide
Reinagel & Reid, 2002
Temporal patterns conserved across animals
A
PSTH
0.2
0.1
-0.1
0.6
0.9 0.2
0.2
0.3
-0.9
-0.3
-0.1
Norm'd
t
0.5
0
1500
1600
1700
1800
1900
2000
Time (ms)
Reinagel & Reid 2002
Mutual Information (bits/s)
Temporal precision of visual information
a b
c
4
8
de
100
50
0
0.5
1
2
16
32
64 128
Precision of spike times used (ms)
Theory of Shannon, 1948
Method of Strong et al., 1998
Result of Reinagel & Reid, 2000
Mechanisms Underlying Precise Timing
Pouille & Scanzian 2001
Deterministic
Mean
4
Variance 0
Poisson
Mean
4
Variance 4
Spike Count: Trial to Trial Variability
Measure of variability
Variance in Spike #
Mean Spike #
Random
(Poisson)
=1
Deterministic
=0
LGN vs. Poisson Model
PSTH
PSTH
LGN Variability << Poisson
Fano Factor
1
Poisson
0.75
0.5
0.25
0
0 100
LGN
500
bin size T (msec)
1000
Variability increases from retina to cortex
Fano Factor at ~ 40 Hz
1
0
RGC
LGN
V1
Kara, Reinagel & Reid, 2000
When firing rate is high, variability is low
LGN
RGC
200
V1
Firing
Rate
0
FF
1
0
0
500
Time (ms)
Kara, Reinagel & Reid, 2000
Refractoriness Regularizes?
PSTH
Poisson model
Poisson with
Refractory Period
probability
Estimating refractoriness from data
model
Method:
Berry &
Meister
1998
data
0
10
20
30
40
50
ISI
60
70
80
90
100
Recovery Function
recovery function
1
absolute and
relative
refractoriness
0.5
0
0
5
10
15
20
25
time since last spike (ms)
30
35
Free Firing Rate
firing rate (sp/s)
500
free
400
300
200
100
0
observed
0
500
time (ms)
1000
Refractory models for all cell types
Fano Factor
LGN
RGC
2
V1
1
0
200
0
0
Time (ms)
Kara, Reinagel & Reid, 2000
Variability increases from retina to cortex
Fano Factor at ~ 40 Hz
1
0
RGC
LGN
V1
Kara, Reinagel & Reid, 2000
Refractoriness decreases
from retina to cortex
Recovery Function
1.0
V1
0.5
RGC
0.0
0
10
20
30
Time (ms) 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)
Hubel and Wiesel (1961)
Jahnsen and Llinas (1984)
Bursting in the LGN
• dominate during sleep,
when vision is
suppressed
• not rhythmic or synchronous in
anesthetized animals
• frequent under
anesthesia, when vision
is absent
• synapses prefer bursts
• almost never seen in
alert animals, when vision
is happening
• visual in anesthetized animals
• do occur in alert animals, and
rare signals can be important
• cool computational ideas
ERGO
ERGO
Bursts are irrelevant to
vision
Bursts are crucial to vision
Visual inputs trigger bursts
Optimal Guess of Stimulus
Before a burst
0.2
Before a tonic spike
0.1
0
-0.1
-0.6
-0.4
-0.2
0
Time before spike (s)
Coding Efficiency
Bits/event
1.5
1
0.5
0
Burst
Tonic
0.2
0.15
0.1
0.05
0
Burst
Tonic
Reinagel, Godwin, Sherman & Koch 1999
AP
times
Bursts: Triggering vs. Priming
*
••
•
LT-Ca++
channel
state
Trigger
synaptic
input
Ca++
spike
observable
• •• •
•
active
inactive
time
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
What happens in the LGN?
• spiking inputs
• intrinsic properties
• local circuits
• cortical feedback
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?
An awake behaving rodent prep for vision
Thanks to collaborators at CSHL
Flister, Meier , Conway & Reinagel (unpub)
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
Spatial correlation in natural images
Natural Image
1
Correlation
Power spectrum
2
10
0.8
0.6
0
10
0.4
0.2
-40
-2
-20
0
20
distance
40
10
0
10
102
cycles/degree
(cf. Field 1987; Tadmore & Tolhurst; Ruderman & Bialek; van Hateren)
Natural Image
Correlation
Power spectrum
4
1
10
0.8
2
10
0.6
0.4
0
10
0.2
0
-2
-20
0
20
10
-2
10
0
10
2
10
4
1
10
0.8
2
10
0.6
0.4
0
10
0.2
0
-2
-20
0
20
Distance (pixels)
10 -2
10
0
10
2
10
Spatial frequency
(cf. Barlow 1961)
luminance
Natural temporal stimulus
0
10
-1
10
-2
10
0
1
2
3
4
time (s)
Correlation
Power Spectrum
2
1
10
0.8
0
10
0.6
-2
10
0.4
-4
10
0.2
0
-1
-6
0
Distance (sec)
1
10
-1
10
0
10
1
10
2
10
3
10
Temporal frequency (Hz)
(cf. Dong & Atick 1995; van Hateren 1997)
luminance
0
10
-1
10
-2
10
0
1
2
3
4
time (s)
Correlation
Power Spectrum
1
0
0.8
10
0.6
0.4
-5
10
0.2
0
-2
-1
0
1
distance (sec)
2
-1
10
0
10
1
10
2
10
3
10
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 retina
Hubel 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
Bullfrog Auditory Neuron: Natural Stimulus is ‘Optimal’
B
300
C
7
6
Information (bits/spk)
250
0.8
5
200
0.6
4
150
3
100
0
0.4
2
50
0.2
1
white
natural
0
1
Efficiency (bits/bit)
Information (bits/s)
A
white
natural
0
white
natural
Rieke, Bodnar & Bialek 1995
Cat LGN Neuron: Opposite result
B
60
40
20
0
white
natural
C
3
Information (bits/spk)
Information (bits/s)
80
2.5
2
1.5
1
0.5
0
0.6
Efficiency (bits/bit)
A
0.5
0.4
0.3
0.2
0.1
white
natural
0
white
natural
Analog LED Stimuli
Reinagel & Reid, in prep.
LGN Responses to full field temporal stimuli
105
Natural visual stimulus
Power
104
103
Random visual stimulus
102
101
10 0
101
102
temporal frequency (Hz)
103
(replication of
Dan et al 1996)