Correlated noise and the retina’s population code for direction...
Transcript of Correlated noise and the retina’s population code for direction...
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Correlated noise and the retina’s population code
for direction
Eric Shea-Brown Joel Zylberberg
Jon Cafaro Max Turner
Greg Schwartz Fred Rieke
University of Washington
b
c e
d
3 s
trial 1!!
trial 2!.!.!.
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ρ = 0.52 ρ = 0.80
180° offset tuning
0° offset tuning
n = 14 pairs
f
Stimulus
Cell-attachedspike rasters
Cell 1
DS cell responses are noisy
ON-OFF DS cells
b
c e
d
3 s
trial 1!!
trial 2!.!.!.
!
ρ = 0.52 ρ = 0.80
180° offset tuning
0° offset tuning
n = 14 pairs
f
Stimulus
Cell-attachedspike rasters
DS cell responses are noisy + noise is correlated from cell to cell
Cell 1Cell 2
rate
How does correlated noise impact sensory coding?
neuron 1 response
neur
on 2
resp
onse
uncorrelated
xx Correlation can
degrade signal encoding.
neuron 1 response
neur
on 2
resp
onse
correlated
xx
Make sure explain why have elliptical shape -- conc towards diag.
rate
How does correlated noise impact sensory coding?
Zohary et al ’94 Abbott+Dayan ’99 Sompolinsky et al. ‘01, Panzeri + Peterson ’01, Averbeck et al., Nat Rev Nsci ‘06
ν
neuron 1 response
neur
on 2
resp
onse
uncorrelated
xx
rate
Zohary et al ’94 Abbott+Dayan ’99 Sompolinsky et al. ‘01, Panzeri + Peterson ’01, Averbeck et al., Nat Rev Nsci ‘06
How does correlated noise impact sensory coding?
ν
neuron 1 response
neur
on 2
resp
onse
uncorrelated
neuron 1 response
neur
on 2
resp
onse
correlated
Correlation can enhance signal encoding.
xx
xx
rate
Zohary et al ’94 Abbott+Dayan ’99 Sompolinsky et al. ‘01, Panzeri + Peterson ’01, Averbeck et al., Nat Rev Nsci ‘06 Shamir et al ’06 Josic et al ’09 Ecker et al ’11 da Silvera and Berry, ’13 Hu et al ’14
How does correlated noise impact sensory coding?
What happens for DS cells, and what are the circuit mechanisms?
Varied effects:Depends on signal direction
vs. noise direction.
What happens in DS cell circuit, and why?
Population view of DS cell responses - (idealized!)
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spik
e co
unt
Population view of DS cell responses - (idealized!)
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spik
e co
unt
Signal direction: tangent to sphere
Noise direction:
Wait to explain idealization until show red arrow. Then, say — here’s idealization … sinusoidal curves w/ same amplitude, offset.
Population view of DS cell responses - (idealized!)
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spik
e co
unt
Signal direction: tangent to sphere
Noise direction:tangential degrades coding
tangential noise
tangentialvariance
radialvariance
Population view of DS cell responses - (idealized!)
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Signal direction: tangent to sphere
Noise direction:tangential degrades codingradial enhances coding
Which case occurs?
tangential noise
radial noise
Measure from cell pairs
Model cell population
SAY … are two possibilities. Noise could be tangent to sphere, in which case it COULD interfere with signal direction. Or, could be in radial direction, in which case guaranteed to be out of way.
spik
e co
unt
Radial Variance
Tangential Variance
= Fraction Radial Var.Radial Var.
Radial Var. + Tangential Var.
Response noise in DS cell pairs
Response noise in DS cell pairs
= Fraction Radial Var.Radial Var.
Radial Var. + Tangential Var.
With
cor
rela
tions
Without correlations
… is largely radial.
What are the underlying circuit mechanisms?
Explain scatter plot axes. Then — two things to take away. First, points are over 50 percent. Second, correlations contribute strongly to this effect.
inh
exc
cf. Cafaro and Rieke ’11
Paired alternating voltage clamp experiments: conductance correlations
Jon and Fred do intracellular recordings which yield simultaneous measurements of exc and inh, in both cells. That yields key statistics: covariance among all possible conductances within and between cells, and how these depend on stim. This leads to following model
Paired alternating voltage clamp experiments: conductance correlations
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Conductance correlations suggest common input model
mean=gi(s)inh
exc
Common noise source diverges to enter exc and inh pathways for each cell. Along way, multipled by stim-dependent gain. And, that gain is set by the mean response (as for exp nonlin).
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Conductance correlations suggest common input model
Paired alternating voltage clamp experiments: conductance correlations
inh
exc
Common input model produces radial variance
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Common input model produces radial variance
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...
Noise: Nc * g1(s)Mean: g1(s)
Nc * g2(s)
g2(s)
Nc * g3(s)
g3(s)... ~n
~m
~m~n
… radial variance!
Common input model produces radial variance
1 2 3
...1 2 3
Data -- show it again
Spike outputs preserve radial variance
model
~m~n
… radial variance!
Common input model produces radial variance
1 2 3
...1 2 3
Data -- show it again
modeldata
Spike outputs preserve radial variance
modeldata
1 2 3
...1 2 3
Bring the sphere back ... with pink and blue ... argue that the orientation of the cloud matters by rotating.
Common input model produces radial variance…
Common input model produces radial variance…
1 2 3
...1 2 3
r1 r2 r3
r1
r2
r4 r3
r5
r6
r8 r7
Stim−Dep CorrsShuff Rand Rot’ns0
10
20
30
40
Estim
atio
n Er
ror (
deg.
2 )
0
s sest
… that ~doubles coding precision
With correlations
Without correlations
h�s� sest
�2iCoding error
(deg^2)
Caveat: enough heterogeneity?
… Thus the common input - driven correlations are responsible for coding being about 180 to 200 pct more accurate.
Summary
• ON-OFF direction selective RGCs give noisy, correlated spike responses
• Intracellular recordings suggest a common input circuit model
• This circuit model – orients noise in radial direction– separates noise from signal, improving coding accuracy
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BURROUGHS-WELLCOME FUND SIMONS FOUNDATION NSF -- Math Biol., Robust Intelligence, Statistics Teragrid / XSEDE HHMI
Thank you…
Max Turner
Joel Zylberberg
Jon Cafaro
Max Turner
Fred Rieke