Van Essen et al., 1992
F unctionA rchitectureC onnectionsT opography
Adelson & Bergen, 1990
PIT
V1 V2 V3
PIP
V3a
MDP
MIP
PO
MT
V4
VIP
LIP
MST
FST7a
STPp
CIT
STPa
AIT
OV
T
DP
Wallisch & Movshon, 2008
after Felleman & Van Essen, 1991
Ungerleider & Mishkin, 1982
Object discriminationLandmark discrimination
Macaque visual cortex
V1
V2
MT
FST
AITv
CITv
7a
STP
LIP, VIP
MST
DP
V4
PITd
PITvCITd
AITd
VOTVP
FEF
Visualinput
Record
Hubel and Wiesel, 1968
V1
0% coherence 50% coherence 100% coherence
Coherence controls visibility
Newsome and Paré, 1988
Downing and Movshon, 1989
V1
V2
MT
FST
AITv
CITv
7a
STP
LIP, VIP
MST
DP
V4
PITd
PITvCITd
AITd
VOTVP
FEF
Visualinput
Record
Maunsell and Van Essen, 1983
MT
Hubel and Wiesel, 1968
V1
0% coherence 50% coherence 100% coherence
Visualstimulus
Neuronalresponse
Behavioraljudgement
FixationPoint
Pref target
Null target
10 deg
Receptive field
DotsAperture
Fix Pt
Dots
Targets
1 sec
Britten, Shadlen, Newsome & Movshon, 1992
Barlow, Levick & Yoon, 1971
Britten, Shadlen, Newsome & Movshon, 1992
Britten, Shadlen, Newsome & Movshon, 1992
0
20
40
60
Num
ber
of c
ells
0.1 1 10Threshold ratio (neuron/behavior)
Britten, Shadlen, Newsome & Movshon, 1992
Neuronal threshold, choice (%)
Neu
rona
l thr
esho
ld, f
ixat
ion
(%)
1 10 1001
10
100
Britten, Shadlen, Newsome & Movshon, 1992
Visualstimulus
Neuronalresponse
Behavioraljudgement
Neurometric function Psychometric function
Visualstimulus
Neuronalresponse
Behavioraljudgement
Neurometric function Psychometric function
?
0.1 1 10 100
Motion strength (% coherence)10 20 30 40 50 60 70 80
Trial number
0
5
10
15
20
25
30
35
40
45
50
Res
pons
e (im
puls
es/s
ec)
0.4
0.5
0.6
0.7
0.8
0.9
1
Pro
port
ion
corr
ect
NeuronBehavior
Correct trialsError trials
Shadlen, Britten, Newsome & Movshon, 1996
Britten, Newsome, Shadlen, Celebrini & Movshon, 1996
Britten, Newsome, Shadlen, Celebrini & Movshon, 1996
010
2030
40
0
20
40
Pro
ba
bili
ty
PREF resp
NULL resp
0.3 1 3 10
Response ratio ("preferred"/"null")
0.0
0.5
1.0
Choic
e p
robabili
ty
0.0
0.2P
roport
ion o
f tr
ials
1.066
0.0 0.2
Proportion of trials
0.548
C
Britten, Newsome, Shadlen, Celebrini & Movshon, 1996
0 500 1000 1500 2000
Time (msec)
0.0
0.2
0.4
Mean n
orm
aliz
ed r
esponse
0 500 1000 1500 2000
Time (msec)
-0.1
0.0
0.1
Diffe
rence in n
orm
aliz
ed r
esponse
Britten, Newsome, Shadlen, Celebrini & Movshon, 1996
Britten, Newsome, Shadlen, Celebrini & Movshon, 1996
Visualstimulus
Neuronalresponse
Behavioraljudgement
Neurometric function Psychometric function
Choice probability
Albright, 1984
V1
V2
MT
FST
AITv
CITv
7a
STP
LIP, VIP
MST
DP
V4
PITd
PITvCITd
AITd
VOTVP
FEF
Visualinput
Stimulate
FixationPoint
Pref target
Null target
10 deg
Receptive field
DotsAperture
Fix Pt
Dots
Targets
1 sec
ElectStim
Salzman, Murasugi, Britten and Newsome, 1992
Salzman, Murasugi, Britten and Newsome, 1992
Salzman, Murasugi, Britten and Newsome, 1992
Salzman, Murasugi, Britten and Newsome, 1992
Visualstimulus
Neuronalresponse
Behavioraljudgement
Neurometric function Psychometric function
Choice probability
0 90 180
Difference in preferred direction (deg)
0.0
0.5
Inte
rneuro
nal corr
ela
tion
MT neuron pairs (Zohary et al.)
Zohary, Shadlen and Newsome, 1994
Xup
1
Xup
2
Xup
3
!
!
!
Xup
N
Pooled MT Signal
! X "up
Xdown
1
Xdown
2
Xdown
3
!
!
!
Xdown
N
! X "
Pooled MT Signal
down
Decisionup
! X " ! X "down
>
Shadlen, Britten, Newsome & Movshon, 1996
1 10 100 1
10
Thr
esho
ld (
% c
oher
ence
)
1 10 1000.5
0.6
0.7
0.8
Number of neurons
Cho
ice
prob
abili
ty
1 100.5
0.6
0.7
0.8
Threshold (% coherence)
Cho
ice
prob
abili
ty
4
16256
16
4
!" Decision
Pooled "up" signal
Pooled "down" signal
MT neuron pool
r = 0
r = 0.18
r = 0
r = 0.18
Shadlen, Britten, Newsome & Movshon, 1996
1 10 100 1
10
Thr
esho
ld (
% c
oher
ence
)
1 10 1000.5
0.6
0.7
0.8
Number of neurons
Cho
ice
prob
abili
ty
1 100.5
0.6
0.7
0.8
Threshold (% coherence)
Cho
ice
prob
abili
ty
4
16256
16
4
!" Decision
Pooled "up" signal
Pooled "down" signal
MT neuron pool
r = 0
r = 0.18
r = 0
r = 0.18
!" Decision
Pooled "up" signal
Pooled "down" signal
MT neuron pool
+
+
Pooling noise
Pooling noise
1 100.5
0.55
0.6
0.65
0.7
Cho
ice
prob
abili
ty
Threshold (% coherence)
A
B
C
D
Jazayeri & Movshon, 2006
Computing the likelihood of each direction
log L ( )
log L ( )
= 180o
MT
population
activity
Pooling
connections
Saccade Vector Map
RightwardLeftward
Direction of Motion Map
● ●
✙
● ●
✙
● ●
Fixate350 msec
Targets appear 500 msec
Random dot motion2 sec
Delay500-1000 msec
Saccade
✙
● ●Target 2Target 1
✙
task_panels_vert_noMF.isl
● ●Target 2Target 1
✙ FP
A
B
Shadlen and Newsome, 2001
0 1 20
50
-1 0 0 1 20
50
-1 0
0 1 20
50
-1 0 0 1 20
50
-1 0
0 1 20
50
-1 0 0 1 20
50
-1 0
Time (s)
sp/s
51.2%
12.8%
0%
Shadlen and Newsome, 2001
-0.5 0 0.5 1
10
15
20
25
30
35
40
45
50
-0.5 0 0.5
10
15
20
25
30
35
40
45
50
Time (s)
motion onset saccadeM
ean
resp
onse
(sp
/s)
51.2%25.6%12.8%6.4%0%
N=106
Mike Shadlen
-0.5 0 0.5 10.5
0.6
0.7
0.8
0.9
1
Time from motion onset (s)
Pro
babi
lity
(mea
n)
-0.5 0 0.50.5
0.6
0.7
0.8
0.9
1
Time from saccade
51.2%25.6%12.8%6.4%0%
N=106Correct choicesMike Shadlen
Responses in a reaction-time version of the direction discrimination task
Average LIP activity in a reaction-time task shows evidence of integration to a “decision boundary”
Roitman & Shadlen (2002)
choose Tin
choose Tout
High motion strength
High motio
n strength
Low motion strength
Time
~1 secStimulus
onStimulus
off
Spikes/s
Time
~1 secStimulus
onStimulus
off
Spikes/s
Low motion strength
MT neurons represent ongoing motion, while LIP neurons seem to represent the output of a neural integrator that accumulates evidence for decision making
MT – sensory evidenceMotion energy “step”
LIP – decision formationAccumulation of evidence “ramp”
Threshold
Mike Shadlen
Momentary evidencee.g.,
∆Spike rate:MTRight– MTLeft
Accumulated evidencefor Rightward
andagainst Leftward
Criterion to answer “Right”
Criterion to answer “Left”
Diffusion to bound model
Palmer et al (2005) Shadlen et al (2006)
�
µ = kC
C is motion strength (coherence)
P =1
1+ e−2k C B
�
t(C) = BkCtanh(BkC) + tnd
Responses in a reaction-time version of the direction discrimination taskare well described by the “race” model of integration to a decision boundary