remembering to decide: discrimination of temporally separated stimuli (selecting the best apple)...
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Remembering to decide: discrimination of temporally separated stimuli
(selecting the best apple)
Paul MillerBrandeis University
Parametric Working Memory and Sequential Discrimination
Experiments by group of R. Romo et al., UNAMNature 399:470 (1999), Cereb. Cort. 13:1196 (2003)
Choose f1 > f2
f2f1
or f2 > f1
f1 f2
Rastergram:f1(Hz)10141822263034
base delay
Trial-averaged firing rate
Fir
ing
rate
(H
z)
0
30
Time (sec)0.5 3.5
(from Miller et al. Cerebral Cortex 2003)
Tuning curve ofmemory activity
Fir
ing
rate
(H
z)
Stimulus, f1 (Hz)5
18
10 34
Romo et al. Nature 1999
A continuous attractor acts as an integrator
Time
Time
Input
Memoryactivity
... but integration yields magnitude x time
Time
Time
Input
Memoryactivity
Problem: How can a network compare an incoming stimulus with an earlier one in memory?
Especially as discrimination ≡ subtraction whereas integration ≡ addition
Sequential Discrimination
Integral feedback control: memory neurons (M) inhibit their inputs (D).
Solution:
-
+
∫ rDdt
Input
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 1 low
cue1 delay cue2
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 1 low
cue1 delay cue2
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 2 higher
cue1 delay cue2
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 2 lower
Threshold not reached
cue1 delay cue2
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 1 high
cue1 delay cue2
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 2 lower
Threshold not reached
cue1 delay cue2
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue1 delay cue2
cue 2 higher
A continuous attractor for memory
A continuous attractor for memory
Feedback too high
Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.
Feedback too high
Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.
Feedback too high
Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.
Feedback too low
Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.
Feedback too low
Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.
Feedback too low
Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.
Continuous or discrete memory?
Note psychophysics: for most continuous quantities, we can only remember (even recognize?) them in discrete categoriesExcept when quantity is encoded across different neurons (eg vision, pitch)
Simulation results
Look at Discriminating neuron
Memory = Discrete Integrator
Activity of model discriminating neuron.
base delay comparison
base delay comparison
Activity of model discriminating neuron.
Trial-averaged firing rate through time of model discriminating neuron for different pairs of stimuli
f1 = 34Hz
f1 = 10Hz
f2>f1
f2<f1
Base, f1 Delay Comparison, f2
Time (sec)0 0.5 3.5 40
100
Fir
ing
rate
(H
z) f1 = 22Hz
Miller and Wang, PNAS 2006
Base tuning
Comparison tuning
Delay tuning
f2>f1
f2<f1
Trial-averaged firing rate through time from experimental data of Romo (prefrontal cortex)
Base, f1 Delay Comparison, f2
Time (sec)0 0.5 3.5 40
35
Fir
ing
rate
(H
z) f2>f1
f2<f1
f1=12Hzf1=20Hzf1=28Hz
PFC cell from Romo's data:Initial tuning +ve to f1 : final tuning to +f2-f1
Base, f1 Delay Comparison, f2
Time (sec)0 0.5 3.5 40
60
Fir
ing
rate
(H
z)
f2>f1
f2<f1
f1=10Hzf1=22Hzf1=34Hz
PFC cell from Romo's data Initial tuning -ve to f1 : final tuning to +f1-f2
Base, f1 Delay Comparison, f2
Time (sec)0 0.5 3.5 40
35
Fir
ing
rate
(H
z)
f2<f1
f2>f1
f1=10Hzf1=22Hzf1=28Hz
Decision-making as a competition between pools
f1=22Hz
Probability of choosing f2>f1 from simulations
f1=14Hz f1=22Hz
Probability of choosing f2>f1 from simulations
f1=14Hz f1=22Hz f1=30Hz
Probability of choosing f2>f1 from simulations
Miller, in preparation
Probability of choosing f2>f1 from experiment
f1 = 20Hz f1 = 30Hz
f2
Probability of choosing f2>f1 from experiment
= fix f2 (20Hz), vary f1= fix f1 (20Hz), vary f2
Probability of choosing f2>f1 from experiment
Hernandez et al, 1997
= fix f2 (20Hz), vary f1= fix f1 (20Hz), vary f2
= fix f2 (30Hz), vary f1= fix f1 (30Hz), vary f2
fixed f1=22Hz fixed f1=30Hz
Probability of choosing f2>f1 from simulations
fixed f1=22Hz fixed f1=30Hz
Probability of choosing f2>f1 from simulations
fixed f2=22Hz fixed f2=30Hz
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 1:low
Is magnitude dissociated from duration of input?
Input
rD
rM
ID=Input -W
MD r
M
t
t
t
tcue1 delay cue2
cue1 delay cue2
cue 1:longer
Is magnitude dissociated from duration of input?
Duration of initial stimulus: = 0.5s
Is magnitude dissociated from duration of input?Simulation results
Duration of initial stimulus: = 0.5s= 0.25s
Is magnitude dissociated from duration of input?Simulation results
Duration of initial stimulus: = 0.5s= 0.25s
= 0.75s+
Is magnitude dissociated from duration of input?Simulation results
From Luna et al., Nat Neurosci 2005
Is magnitude dissociated from duration of input?Experimental results
