how do neurons deal with uncertainty? sophie deneve group for neural theory ecole normale...
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How do neurons deal with uncertainty?
Sophie DeneveGroup for Neural Theory
Ecole Normale SupérieureParis, France.
Dealing with uncertainties: two approaches
State of the word or body
Neural representation
Behavioral response
Sensor noise
Neural noise
Effector noise
Efficient encoding(information maximization)
Efficient decoding(ideal observer)
Tuning curves, neural code
Signal processing
ss x
nr f s
x Sensory variable
ˆ ˆy g x Behavioral estimate
Velocity perception as bayesian inference:Weiss, Simoncelli and Adelson, 2002
Bayesian perception and action
Infering 3D structures from 2D images. Knill and Richards, 1996
Bayesian integration for optimal motor strategies.Kording and Wolpert, 2004.Multisensory integration.
Van Beers, Sittig and Gon, 1999, Ernst and Banks 2002
1| |P V S P S V P V
Z
Dealing with uncertainties: two approaches
Internal beliefs
Behavioral strategies
State of the word or body
Inference Prediction
decisions explorations
PredictionLikelihood
Maximize Utility/Reward
Models, Probabilities, priors….
Bayesian models
max , |x
reward y x p x s
x
ss x
Sensory variable
Posterior probabilities
| ,r f p x s priors
Two parts of the lecture
• Part 1: How can variables encoded by population of noisy neurons be estimated? How can they be combined?
• Part 2: Are probability distributions and belief states encoded in neurons or neural populations?
Population coding
Population coding
Georgopoulos 1986
Population Codes
Tuning Curves Average pattern of activity
-100 0 1000
20
40
60
80
100
Direction (deg)
Act
ivit
y
-100 0 1000
20
40
60
80
100
Preferred Direction (deg)
Act
ivit
y s?
i i ir f x x f x
Poisson Variability in Cortex
Trial 1
Trial 2
Trial 3
Trial 4
Mean Spike Count
Variance of Spike Count
Noisy population Codes
Pattern of activity (r)
-100 0 1000
20
40
60
80
100
Preferred Direction (deg)
Act
ivit
y s?X?
exp
!
k
i ii
f x f xp r k
k
Poisson noise:
Independent: | |i
i
p x p r xr
Population Vector
x
P
riPi-100 0 100
0
20
40
60
80
100
Preferred Direction (deg)
Act
ivit
y
s?X?
i ii
P r P
-45 0 450
20
40
60
80
100
Act
ivit
y
Maximum likelihoodˆpvx
pv
Preferred Direction of motion (deg)
22 1
ˆpv pvx xI x
ˆ anglepv i ii
x r P
Fisher information
Maximum performance for an unbiased estimator
-45 0 450
20
40
60
80
100
Act
ivit
y
Maximum likelihoodˆMLx
ML
Preferred Direction of motion (deg)
2 1ML I x
ˆ arg max |MLx p xx
r
i i
i
xf
xfxI
2'
Fisher Information
• For one neuron with Poisson noise
• For n independent neurons :
The more neurons, the better! Small variance is good!
Large slope is good!
xf
xfxI
i
ii
2'
Derivative of the tuning curve
Tuning curve (mean activity)
-100 0 1000
20
40
60
80
100
Direction (deg)
Act
ivit
y
x
Variance of the maximum likelihood estimate
22'2
1ˆML ML
i
i i
x xf
f
• Population of neuron with independent Poisson noise:
-45 0 450
20
40
60
80
100
Act
ivit
y
Recurrent network
Preferred Direction of motion (deg)
-45 0 450
20
40
60
80
100
Act
ivit
y
Recurrent networkˆrx
r
Preferred Direction of motion (deg)
r ML
If the network is a line attractor, and if
We have:
1 Wdt
fNetwork parameters
Covariance of the neural noise
Derivative of the tuning curve
Line attractor networks
-45 0 450
20
40
60
80
100
Act
ivit
y
Preferred Direction of motion (deg)
ˆrx
-45 0 450
20
40
60
80
100
Act
ivit
y
Preferred Direction of motion (deg)
ˆrx
-45 0 450
20
40
60
80
100
Act
ivit
yPreferred Direction of motion (deg)
ˆrx
Neuron 1
Neu
ron
2
Neuro
n 3
Line attractor networks
-45 0 450
20
40
60
80
100
Act
ivit
y
Preferred Direction of motion (deg)
-45 0 450
20
40
60
80
100
Act
ivit
yPreferred Direction of motion (deg)
-45 0 450
20
40
60
80
100
Act
ivit
y
Preferred Direction of motion (deg)
Neuron 1
Neu
ron
2
Neuro
n 3
Line attractor networks
Neuron 1
Neu
ron
2
Neuro
n 3
-45 0 450
20
40
60
80
100
Line attractor networks
Neuron 1
Neu
ron
2
Neuro
n 3
-45 0 450
20
40
60
80
100
-45 0 450
20
40
60
80
100
Act
ivit
y
Preferred Direction of motion (deg)
ˆrx
Line attractor networks
Neuron 1
Neu
ron
2
Neuro
n 3
Line attractor networks
Neuron 1
Neu
ron
2
Neuro
n 3
Line attractor networks
Neuron 1
Neu
ron
2
Neuro
n 3
-45 0 450
20
40
60
80
100
Act
ivit
y
Preferred Direction of motion (deg)
ˆrxˆ r
Line attractor networks
Neuron 1
Neu
ron
2
Neuro
n 3
Covariance of neural noise
Shape of stable manifold
Direction of projection
Line attractor networks
Neuron 1
Neu
ron
2
Neuro
n 3
Covariance of neural noise
Shape of stable manifold
Direction of projection
Line attractor networks
WT
dt
fNeuron 1
Neu
ron
2
Neuro
n 3
Covariance of neural noise
Shape of stable manifold
Direction of projection
1 =
Cue integration
Visual capture: The ventriloquism effect
Multi-sensory integration
Pro
babi
lity
Position of the object, x^ ^
( | ) ( | )( | , ) vis aud
vis aud
p x p xp x
N
r rr r
Combining cues from several modalities
( / )visp x r( / )audp x r
( / , )vis audp x r r
Pro
babi
lity
Position of the object, x^ ^
Combining cues from several modalities: Gaussiab distributions
ˆvisx
ˆaudx
visaud
Pro
babi
lity
Position of the object, x^ ^
Combining cues from several modalities
ˆvisx
ˆaudxˆbix
ˆ ˆ ˆ ˆ ˆaud visbi vis aud vis vis aud aud
vis aud vis aud
x x x w x w x
visaud
Visual xvis
Auditoryxaud
Bimodal
Visual xvis
Auditoryxaud
Bimodal
Visual xvis
Auditoryxaud
Bimodal
Visual more reliable: Visual capture
Auditory more reliable: Auditory capture:
Analogy with the center-of-mass
Ernst and Banks, 2002
From: Banks et al, Nature, 2002
0 67 133 2000
0.05
0.1
0.15
0.2
Th
resh
old
(S
TD
)Visual noise level (%)
Measured bimodal STD
Predicted by the optimal model
0 67 133 2000
0.2
0.4
0.6
0.8
1
Vis
ua
l we
igh
t
Visual noise level (%)
Unimodal visual STD
Unimodal Tactile STD
Measured visual weight
Visual widthxv
Haptic widthxt
Bimodal width
Prior, likelihood and posterior
-45 0 450
20
40
60
80
100
Preferred position (x)
Neu
ral r
espo
nse
|p xrLikelihood:
p xPrior:
||
p x p xp x
p
rr
r
Posterior (Bayes Theorem)
Prior, likelihood and posterior
-45 0 450
20
40
60
80
100
Preferred position (x)
Neu
ral r
espo
nse
| | ir
i ii i
p x p r x K f x r
Flat prior
| ' ir
ii
p x K f x r
log | '' log i ii
p x K f x r r
Gain as encoding certainty
-45 0 450
20
40
60
80
100
Act
ivit
y
visML
Preferred Direction of motion (deg)
ˆvisMLx
1ML
x
| visp x rVisual input:
-45 0 450
20
40
60
80
100
Act
ivit
y ˆaudMLx
Preferred Direction of motion (deg)
audML
2ML
x
| audp x rAuditory input:
1 2ML
x
| bip x r
Product
ˆvisMLx
ˆvisaudx
ˆbiMLx
Act
ivit
y
-45 0 450
10
20
Preferred eye-centered position-45 0 45
0
10
20
Preferred eye position
Act
ivit
y
-45 0 450
10
20
Act
ivit
y
Preferred head-centered position
Eye position
Eye-centered position
Head-centered position
Visual layer Eye position layer
Auditory layer
Integrating several noisy population code
-45 0 450
10
20
Preferred eye-centered position
Act
ivit
y
-45 0 450
10
20
Preferred eye position
Act
ivit
y
-45 0 450
10
20
Act
ivit
y
Preferred head-centered position
Maximum Likelihood
Eye position
Eye-centered position
Head-centered position
Visual layer
Auditory layer
Eye position layer
-45 0 450
10
20
Preferred eye-centered position
Act
ivit
y
-45 0 450
10
20
Preferred eye position
Act
ivit
y
-45 0 450
10
20
Act
ivit
y
Preferred head-centered position
Eye position
Eye-centered position
Head-centered position
Visual layer
Auditory layer
Eye position layer
Multidirectional sensory prediction
-45 0 450
10
20
Preferred eye-centered position
Act
ivit
y
-45 0 450
10
20
Preferred eye position
Act
ivit
y
-45 0 450
10
20
Act
ivit
y
Preferred head-centered position
Visual layer
Auditory layer
Eye position layer
tac vis eyex x x
eyexvisx
How do we do coordinate transform?
-45 0 450
10
20
Preferred eye-centered position
Act
ivit
y
-45 0 450
10
20
Preferred eye position
Act
ivit
y
-45 0 450
10
20
Act
ivit
y
Preferred head-centered position
Visual layer
Auditory layer
Eye position layer
+
Not Like This!
-45 0 450
10
20
Preferred eye-centered position-45 0 45
0
10
20
Preferred eye position
Act
ivit
y
-45 0 450
10
20
Act
ivit
y
Preferred head-centered position
Visual layer
Auditory layer
Eye position layer
Look-up table
Look up table.
-45 0 450
10
20
Preferred eye-centered position-45 0 45
0
10
20
Preferred eye position
Act
ivit
y
-45 0 450
10
20
Act
ivit
y
Preferred head-centered position
Visual layer
Auditory layer
Eye position layer
Radial basis function map
Radial basis function mapIterative (IBF)
Bimodal visuo-tactile input
Visual layer Eye position layer
Auditory layer
Neuron 1
Neu
ron
2
Neuro
n 3
Bimodal visuo-tactile input
Neuron 1
Neu
ron
2
Neuro
n 3
Visual layer Eye position layer
Auditory layer
ˆvisx ˆeyex
audx
Bimodal visuo-tactile input, weak tactile input
Visual layer Eye position layer
Auditory layer
Neuron 1
Neu
ron
2
Neuro
n 3
Bimodal visuo-tactile input
Neuron 1
Neu
ron
2
Neuro
n 3
Visual layer Eye position layer
Auditory layer
ˆvisx ˆeyex
audx
Unimodal visual input
Visual layer Eye position layer
Auditory layer
Neuron 1
Neu
ron
2
Neuro
n 3
Performance: The variance of the estimates are less than 4% worse than variance of the best (bayesian) estimator.
Result: IBF is an optimal bayesian estimator
ˆ ˆ ˆ ˆbi vis vis aud aud eyex w x w x x
visx eyex^
ˆ ˆ ˆ ˆbi vis vis aud aud eyex w x w x x
r ML
ˆaudx
From: Banks et al, Nature, 2002 Visual layer Eye position layer
Tactile layer
0 67 133 2000
0.05
0.1
0.15
0.2
Th
resh
old
(S
TD
)Visual noise level (%)
Measured bimodal STD
Predicted by the optimal model
0 67 133 2000
0.2
0.4
0.6
0.8
1
Vis
ua
l we
igh
t
Visual noise level (%)
Unimodal visual STD
Unimodal Tactile STD
Ernst and Banks, 2002
From: Banks et al, Nature, 2002
Measured visual weight
-45 0 450
20
40
60
80
100
Act
ivit
y
Why does it work?Reason 1: Line attractor networks work for different gains
ˆMLx
Preferred Direction of motion (deg)
G=Gain
xRPx
x /maxargˆ
22 '2
1ML
i
i i
G f
Gf
22 1MLML G
G
ML
WT
dt
fNeuron 1
Neu
ron
2
Neuro
n 3
1 =
-45 0 450
20
40
60
80
100
Act
ivit
y
Preferred Direction of motion (deg)
22 1MLr G
Why does it work? Reason 2: For independent Poisson noise, doing sum is
equivalent to multiplying the posteriors
-45 0 450
20
40
60
80
100
Act
ivit
y
visML
Preferred Direction of motion (deg)
ˆvisMLx
1ML
x
| visp x rVisual input:
-45 0 450
20
40
60
80
100
Act
ivit
y ˆaudMLx
Preferred Direction of motion (deg)
audML
2ML
x
| audp x rAuditory input:
1 2ML
x
| bip x r
Product
ˆvisMLx
ˆvisaudx
ˆbiMLx
sum-45 0 45
0
40
80
120
160
200
Act
ivit
y
biML
Preferred Direction of motion (deg)
ˆbiMLx
Generalization: Implicit probabilistic encoding
• Linear combinations correspond to optimal cue integration as long as the distribution of neural noise belong to the exponential family:
We have
Beck, Latham and Pouget, 2007
| exp .p x Z xr r h r
1 2 1 2
1| | |p x p x p x
Z r r r r
Structure of the iterative basis function network.
???Multi-sensory Representation.
Receptive fields in different modalities are roughly aligned in space.
Visual and tactile receptive fields in VIP:
Is multi-sensory integration simply realized by the convergence of all sensory inputs on a common spatial map?
Frame of reference of a multi-sensory cell’s receptive field: Sensory alignment hypothesis.
Model prediction: partially shifting receptive fields.
Duhamel et al, 1997, 2001
The modality dominance influence the receptive field shift.
Visual dominant: Tactile dominant:
Estimate
Visual estimate Tactile estimate
Estimate
rx
(Avillac et al, 2001)
Visual and tactile receptive fields of a VIP cell
VIP
Duhamel et al, 1997Avillac et al, 2001
LIPSC
Jay and Sparks, 1986Stricanne et al, 1995
Shift Ratio as a function of eye-centered dominance
0
0.5
1
1 0.52 0.254Eye-centered dominance
Shi
ft R
atio
Visual input
0
0.5
1
1Eye-centered dominance (confidence given to the visual modality)
Shi
ft R
atio
Tactile (auditory) input
Visual input
Head-centered
PMv
Graziano and Gross, 1998
Taking time into account
• Inference and predictions need to take place on short time scale.
• Events start and end unpredictably, objects move...
• We need to estimate the states of relevant variable on-line. No time to converge to an attractor.
• Spike per spike computations (and coding?)
Explicit encoding of probabilities by population codes
|i ir p x s f x dx
| i ii
p x s r f x
Convolutional codes (Zemel, Dayan, Pouget, Sahani)
Basis function coding (Andersen, Barber, Eliasmith)
Time? Inference is hard.
Experimental evidence is lacking.
It is not enough to store past input to combine it with new input.
Dupont Durand Smith
P(guilty)
It is not enough to store past input to combine it with new input.
Dupont Durand Smith
P(guilty)
It is not enough to store past input to combine it with new input.
Dupont Durand Smith
P(guilty)
It is not enough to store past input to combine it with new input.
P(guilty)
Dupont Durand Smith
It is not enough to store past input to combine it with new input.
P(guilty)
Dupont Durand Smith
It is not enough to store past input to combine it with new input.
P(guilty)
Dupont Durand Smith
It is not enough to store past input to combine it with new input.
P(guilty)
Dupont Durand Smith
It is not enough to store past input to combine it with new input.
P(search)
London Paris
It is not enough to store past input to combine it with new input.
P(search)
London Paris
It is not enough to store past input to combine it with new input.
P(search)
London Paris
Hidden Markov Model
1ts ts 1ts
txdttx dttx Hidden variable
Observations
cause
0 0
1| | | |
t
t dt t dt t dt t dt t dt t t tx
p x s p s x p x x p x sZ
Inference can be performed recurrently:
Observations are spikes:
| |tt t i t
i
p s x p s x 1 is
ii
f x dtZ
0 (no spike) or 1 (spike)
Hidden Markov Model
1ts ts 1ts
txdttx dttx Hidden variable
Observations
cause
1 or 0tx
0 or 1| =1 or 0t t dtp x x
1 , 0i if f
stimulus present/absent
Probability that stimulus appears/disappears
Firing rate of neuron i given that stimulus is present/absent
Example 1: Detecting the presence of a stimulus
?
Hidden Markov Model
1ts ts 1ts
txdttx dttx Hidden variable
Observations
cause
tx
|t t dtp x x
1|it t t ip s x f x x
Position, speed and acceleration of the arm
Noisy dynamics of the arm.
Tuning curve of neuron i
Example 2: Tracking the state of the arm during movement
0px ˆ 1px ˆ 2px
Evidence for “explicit” encoding of probability in neural activity: Shadlen et al
Gold and Shadlen, 2004
Rightward motion: saccade right
Leftward motion: saccade left
Evidence for “explicit” encoding of probability in neural activity: Shadlen et al
Medio-temporal cortex (MT): Lateral intra-parietal cortex (LIP)
Firi
ng r
ate
Time
BRAIN
Preferreddirection
Non-preferreddirection
Increase with coherence
Evidence for “explicit” encoding of probability in neural activity: Shadlen et al
Medio-temporal cortex (MT):Lateral intra-parietal cortex (LIP)
Firi
ng r
ate
Time
1ts ts 1ts
txdttx dttx
0if
0| tp x s 1if
LIP/MT model (sequential probability test)
logt
p leftL
p right
Response left>response right
Response right>response left
Saccade right
Saccade Left
logt
p rightL
p left
LIP/MT model (sequential probability test)
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
LIP/MT model (sequential probability test)
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
MTLIP
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
LIP/MT model (sequential probability test)
LIP/MT model (sequential probability test)
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
LIP/MT model (sequential probability test)
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
LIP/MT model (sequential probability test)
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
LIP/MT model (sequential probability test)
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
LIP/MT model (sequential probability test)
Left>Right
Right>Left
Saccade right
Saccade Left
logt
p leftL
p right
logt
p rightL
p left
Evidence for “explicit” encoding of probability in neural activity: Shadlen et al
Go to the log (replace products by sums):
0 0
1| | |t dt t dt tp x s p s x p x s
Z
Assume the direction of motion never change during the trial:
it dt t i t
i
L L w s In the limit of small temporal discretization step, dt:
1ii t
i
Lw s
dt
OR
0
0
1|log
0 |t dt
tt dt
p x sL
p x s
Firing rate when x is 1
Firing rate when x is 0
1
log0
i
i
f
f
?Bayesian temporal integration in single neurons
txt dtx t dtx
1ts ts 1ts
Bayesian temporal integration in single neurons
?
0if 1if
11| 0on t dt tr p x x
dt
10 | 1off t dt tr p x x
dt
Rate of switching on:
Rate of switching off:
txt dtx t dtx
1ts ts 1ts
Bayesian temporal integration in single neurons
?
ionq i
offq
onr
offr
Rate of switching on:
Rate of switching off:
1 1L L ion off i t
i
Lr e r e w s
dt
?
Time
tL
Leak Synaptic input
' it i t
i
LL w s
t
Bayesian integration corresponds to leaky synaptic integration.
0 100 200 300 400-10
-5
0
5
0 100 200 300 400-10
-5
0
5
Time
Log
odd
ratio
Log
odd
ratio
A.
Time
B.
Accumulation of evidence results in linear ramps followed by a saturation.
Slowly dynamics Fast dynamics
If the stimulus varies very slowly, the neuron is an integrator
For faster stimuli, this integration is leaky
Importance of using the right temporal statistics
Left>Right
Right>Left
Motion right
Motion left
Motion right
Motion left
Generalization to multiple states
0px ˆ 1px ˆ 2px
exp tkkl l kj j
l j
LM L W s
dt
Continuous variablesSequences of discrete state Multiple choice
|jt t kp s x x
|t dt k t l klp x x x x
0log |t k tp x x s
Input layer
Recurrent layer
W
M
Alternative approaches• Linearization of the recurrent equations. Recurrent network of leaky
integrate and fire neuron. (Rao, 2001)
• Membrane potentials are log probabilities. Spike generation exponentiates (Rao, 2003)
• Firing rate are probabilities. Multiplicative dendrites (Beck and Pouget, 2007)
• Generalization of line attractor networks (Deneve et al, 2007).
• Deterministic spike generation mechanism: spikes signal increase in probability (Deneve, 2007).
… …
Speed inputPosition input
Preferred arm speedPreferred arm position
Spi
kes
#
Force
Tim
e
Act
ivit
yA
ctiv
ity
Act
ivit
y
Preferred force
f ( 1)t
f (2)
f (1)
Optimal filtering of noisy sensory input in the presence of movement
0x
INTERNAL MODEL
1x2x
Activity
Act
ivit
y
Activity
Eff
eren
t mot
or c
omm
and
Position input
Speed inputArm position
Arm
spee
d
Sensori-motor Map
Efferent motor command
Feedforward connections: W
Predictive (lateral) connections:
px
sxcx
ipx'i
px
'kcx
kcx
'jsx
jsx
Lateral connections: M
Network model
# S
pik
es
Preferred arm position
# S
pik
es
Preferred arm position
# S
pik
es
Preferred arm position
# Spikes
Preferred arm
speed
# Spikes
Preferred arm
speed
# Spikes
Preferred arm
speed
ˆ ( )px t
ˆ ( )sx t
Position
Speed
Tuning curves of sensorimotor neuron reflects the reliability and covariance of the different sensory and motor variables.
0
5
10
Act
ivit
y
0 10 20 30 40 50
Speed (cm/sec)
Prediction: Partially shifting representation of position and speed.
Arm at 0cm
Arm at 20cm
Population vector points in the right direction!
Individual cells shifts their velocity tunings curves with position
0
5
10
Act
ivit
y
0 10 20 30 40 50
Speed (cm/sec)
Prediction: Partially shifting representation of position and speed.
Arm at 0cm
Arm at 20cm
Cell’s selectivity is described by the 2D tuning curve:
Individual cells shifts their velocity tunings curves with position
unbiased
shifting
Spike generation.
• Rate coding: information is in spike count. Spike times are random (Poisson).
• Deterministic spike generation rule: each spike signals a change in the belief state.
LIP/MT model
Left>Right
Right>Left
Rightward motion
Leftward motion
logt
p rightL
p left
Firing rate
Noisy reports on a murder case?
Dupont Durand Smith
P(guilty)
Durand
Durand
Smith
Durand
Dupont
LIP/MT model
Left>Right
Right>Left
Rightward motion
Leftward motion
logt
p rightL
p left
Integrate and fire
Deterministic firing
tL
time
-2
0
2
0 2000
Spi
kes
Ot
' it i t
i
LL w s
t
Syn
apti
c in
put
time
' t t
GG O
t
tO
-4
-2
0
2
4
Lo
g o
dd
s
500 1000 1500 2000 2500 3000tL
tG
Integrate input (what I know)
(What I know)
Integrate output (what I told)-
(What I told in the past)
' t o t
GG g O
t
' it i t
i
LL w s
t
Mechanism for spike generation in Bayesian neurons
tO
-4
-2
0
2
4
Lo
g o
dd
s
500 1000 1500 2000 2500 3000tL
tG
Integrate input (what I know)
Integrate output (what I told)-
Mechanism for spike generation in Bayesian neurons
0 100 200 300 400
-5
-4
-3
-1
0
1
2
3
4
0
Time0 100 200 300 400
-5
-4
-2
-1
0
1
2
0
Time
go
tLtt tV GL
Integrator with adapting threshold Integrate and fire with adaptive time constant
Neurons cannot all be integrators
? ??
1000 2000 3000 4000 5000-10
0
10
1000 2000 3000 4000 5000-10
0
10
1000 2000 3000 4000 5000-10
0
10
Lt
Time
1000 2000 3000 4000 5000-10
0
10
1000 2000 3000 4000 5000-10
0
10
1000 2000 3000 4000 5000-10
0
10
Spikes deterministically signal an increase in probability
? ??
Lt
Time
Generalization to continuous variables
xtime
exp tkkl l kj j
l j
LM L W s
dt
W
M
Threshold for spiking: expkkl l kj t
l j
GM G O
dt
Time
Implication 1: Spikes signal increase in probability
A spike signals an increase in the probability of a binary variable.
P(xt)
syna
pse
Time
0 10 20 30 400
10
20
30
40
0 200 400 6000
1000
2000
3000
4000
5000
ISI
Co
un
t
Mean spike count
Va
rian
ce s
pik
e c
ou
nt
Implication 2: Firing rate statistics are similar to Poisson, but these fluctuations purely reflects input noise.
Poisson statistics.
The spike train is a deterministic function of the input.
Ot
1000 2000 3000
40
0
Time
Implication 3: Firing precision depends on the proportion meaningful fluctuations.
Tria
ls
Time0 1000 2000
Time0 1000 2000
Tria
ls
ConclusionInformation encoded in noisy population codes can be decoded using recurrent line attractor networks.
However, in order to combine cues and to integrate information over time, it is necessary to represent probability distributions, not only estimates of sensory and motor variables.
This representation could be implicit (i.e. gain encoding) but this implies strong limitations on the statistical problems that can to be solved.
This representation could be more explicit. This seems to account for the integrate and fire dynamics of biological neurons.
But this puts into question the traditional concept of “neural signal” and “neural noise”. Spikes signaling deterministic changes in the belief state share properties with Poisson distributed “rate models”, but merely reflect fluctuations in the input.
This also implies whole new sets of predictions for link between biophysics and behavior.
Internal beliefs
Behavioral strategies
State of the word or body
Inference Prediction
decisions explorations
PredictionLikelihood
Maximize Utility/Reward
Models, Probabilities, priors….
Bayesian models
Towards the Bayesian Brain?
Expectation-Maximization
Repeat
• Expectation step: Compute the expected value of the state given the observations and the current set of parameters.
• Maximization step: Choose parameters maximizing the probability of observations given the expected value of the state.
1ts ts 1ts
txdttx dttx
0 1 1
time
Learning
E(xt)
1ts ts 1ts
txdttx dttx
0 1 1
Repeat
• Expectation step: Compute the expected value of the state given the observations and the current set of parameters.
• Maximization step: Choose parameters maximizing the probability of observations given the expected value of the state.
Lf
time
Lb
Ltot
time
Learning
E(xt)1ts ts 1ts
txdttx dttx
0 1 1
Repeat
• Expectation step: Compute the expected value of the state given the observations and the current set of parameters.
• Maximization step: Choose parameters maximizing the probability of observations given the expected value of the state.
time
Learning
E(xt)1ts ts 1ts
txdttx dttx
0 1 1
Repeat
• Expectation step: Compute the expected value of the state given the observations and the current set of parameters.
• Maximization step: Choose parameters maximizing the probability of observations given the expected value of the state.
time
Learning
E(xt)1ts ts 1ts
txdttx dttx
0 1 1
Repeat
• Expectation step: Compute the expected value of the state given the observations and the current set of parameters.
• Maximization step: Choose parameters maximizing the probability of observations given the expected value of the state.
time
Learning
E(xt)1ts ts 1ts
txdttx dttx
0 1 1
Repeat
• Expectation step: Compute the expected value of the state given the observations and the current set of parameters.
• Maximization step: Choose parameters maximizing the probability of observations given the expected value of the state.
Dynamics at multiple time scales in spiking neurons
2 2.5 3 3.5 4 4.5 5 5.5 6-10
-5
0
5
10
Fast time constant: Inference, spiking
Time (hours)
Lt
Ot
Time (hours)
iw
onr
offr
Slow time constant: Learning, adaptation(on-line EM)
2 2.5 3 3.5 4 4.5 5 5.5 6
x 104
0.008
0.01
0.012
2 2.5 3 3.5 4 4.5 5 5.5 6
x 104
0.04
0.06
2 2.5 3 3.5 4 4.5 5 5.5 6
x 104
0
0.2
0.4
2 2.5 3 3.5 4 4.5 5 5.5 64
-0.1
-0.05
0
1ts 2
ts 3ts 4
ts 5ts 6
ts
1th 2
th 3th 4
th
11th 1
2th 1
3th 1
4th
Learning in networks
h(cuddly zebra)=1 h(dangerous tiger)=1
s(striped patch)=1
1ts 2
ts 3ts 4
ts 5ts 6
ts
1th 2
th 3th 4
th
11th 1
2th 1
3th 1
4th
1ts 2
ts 3ts 4
ts 5ts 6
ts
Divisive inhibition
Learning in networks
2( 1| )p h s
Contrast
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 2 3 4 5 6 7 8