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Basal Ganglia
Caudate nucleus
Putamen
Striatum
Globus pallidus, external
Globus pallidus, internal
Subthalamic nucleus
Basal ganglia
Substantia nigra
basal ganglia
• recall: major DA
targets, involved in
movement &
motivation
BG Disorders
In humans, basal ganglia dysfunction
associated with both hypokinetic and
hyperkinetic movement disorders
Hypokinetic
akinesia
bradykinesia
rigidity
Hyperkinetic
chorea
ballism
tics
A Parkinson’s Brainstem
Parkinson’s disease
• Progressive death of
dopamine neurons
• Hypokinetic disorder
(also tremor)
• Treated with
dopamine precursor
(L-Dopa) or agonists
• Movie
•
Huntington’s
disease
Progressive death of
striatal spiny neurons
• Hyperkinetic disorder:
chorea
• Similar problems
from subthalamic
nucleus lesions, also
Tourette’s, OCD
• Treated with
dopamine blockade
disease:
striatal
degenerati
on
healthy
Medium spiny neurons
• Principal neuron type in
striatum
• Recipient of corticostriatal
inputs
• Extensive dendrites – each
receives input from 10,000
fibers
• Unusual: GABAergic
(inhibitory) projections
– Also collaterals (competitive
network? for competition based
on value?)
Striasomes/Patch
Matrix
The corticostriatal projection
• Input nucleus of
basal ganglia:
striatum
• topographic
projection from
entire cortex
(including sensory,
motor, associative
areas)
– ultimately
reciprocated
• also dopamine
Voorn et al 2004
Parkinson’s disease
• Progressive death of
dopamine neurons
• Hypokinetic disorder
(also tremor)
• Treated with
dopamine precursor
(L-Dopa) or agonists
• Movie
•
Huntington’s
disease
Progressive death of
striatal spiny neurons
• Hyperkinetic disorder:
chorea
• Similar problems
from subthalamic
nucleus lesions, also
Tourette’s, OCD
• Treated with
dopamine blockade
disease:
striatal
degenerati
on
healthy
Parkinson’s treatment
• Suggested by model, STN
lesions (primates) & GPi
lesions in humans alleviate
PD symptoms
– huge success of animal
research, modeling
• More recently, turned to
reversible/tunable deep brain
(STN) stimulation
(DeLong 1990)
Deep-brain stimulation for PD
• Target subthalamic
nucleus (usually)
• High frequency
rhythmic stimulation
• Mechanism not
entirely clear
Model of BG disorders
•
•
hypokinetic & hyperkinetic disorders caused by imbalance in direct/indirect
pathways (Arbin et al. 1989; Alexander & Crutcher 1990)
Dopamine excites striatal MSNs projecting to direct pathway and inhibits those
projecting to indirect pathway (this is an oversimplification)
(DeLong 1990)
Model of BG disorders
•
•
hypokinetic & hyperkinetic disorders caused by imbalance in direct/indirect
pathways (Arbin et al. 1989; Alexander & Crutcher 1990)
Dopamine excites striatal MSNs projecting to direct pathway and inhibits those
projecting to indirect pathway (this is an oversimplification)
Hypokinetic (Parkinson’s)
(DeLong 1990)
Hyperkinetic (Huntington’s)
(DeLong 1990)
The Dopamine Revolution
A Parkinson’s Brainstem
Dopamine responses
• Burst to unexpected
reward
• Response transfers to
reward predictors
• Pause at time of
omitted reward
Schultz et al. 1997
The Standard Model
Reward Prediction Error
Q(t+1) = Q(t) + α[r(t+1) - Q(t)]
Q(t) = Estimate of EU at t
r
= Reward on last trial
26
Bush and Mosteller
New
Old
Association = Association +
Strength
Strength
27
Correction
Bush and Mosteller
New
Value
Estimate
Correction
28
=
Old
Value
Estimate
=
Old
Value
Estimate
+
Correction
-
Obtained
Reward
Association Strength
Bush and Mosteller
1
2
3
4
5
6
7
Trial Number
29
8
9
10
More dopamine responses
reward
following 0%
predictive cue
reward
following 50%
predictive cue
reward following
100% predictive
cue
(Fiorillo et al 2003)
no reward
following 100%
predictive cue
First trial
Conditioned
stimulus
Reward
Last trial
0.5s
Bayer and Glimcher, 2005, 2007
32
33
34
Neuronal Population
N=44
RPE in Humans:
Specific model
RPE = outcome ($) – lottery expected value ($)
n = 12 subjects, 3003 trials
random effects
Basal ganglia
• “Loop” organization
• Input (from cortex): striatum
• Output (back to cortex, via thalamus): globus
pallidus (internal)
Direct and indirect pathways
• Parallel paths through BG
• Opposite effects on thalamus,
motor ctx
– direct pathway has 2x
inhibition: net facilitation,
“go”
– indirect pathway has 3x
inhibition: net inhibition,
“no-go”
• Recordings:
– Striatum: excitation &
inhibition related to
movement execution
– GPi: inhibition related to
movement execution
• Why have two pathways?
Alexander & Crutcher 1990
Striatal PANs
41
Post-Saccadic Neurons:
•Class 1: Movement Just Completed
•Class 2: Reward Just Received
42
•Qi(t) Coded Before
Movement
•Qchosen(t) Coded
After Movement
43
Lau and Glimcher, 2009
Dopamine and plasticity
• If dopamine carries
a prediction error,
where does learning
happen?
• Potentially, the
cortico-striatal
synapse
DA and corticostriatal plasticity
Wickens et al. 1996
Three-factor learning rule? (pre/post/dopamine)
wi,t+1 = wi,t + edt
Addiction
If it is:
The Standard RPE Model
+ Addiction (Redish)
Q(t+1) = Q(t) + α[r(t+1) - Q(t)] +D
D
r
49
= Dopamine Activation
= Reward on last trial
Oculomotor matching task:
Searching for Action Values
Choice
0.10 0.20
Cues
Fix
Rewards arranged
using independent
reward probabilities
Q(t+1) = Q(t) + α[r(t+1) - Q(t)] +D
51
Q(t+1) = Q(t) + α[r(t+1) - Q(t)] +D
52
Example 1
Example 2
Stim On
Stim On
End
temporal-difference learning
Rescorla-Wagner:
Want Vn = rn
units)
 (here n indexes trials, treated as
Use prediction error dn = rn – Vn
Temporal-difference learning (Sutton & Barto):
Predict cumulative future reward:
Want Vt = rt + rt+1 + rt+2 + rt+3 + …  (here t indexes time
within trial)
= rt + Vt+1
 (clever recursive trick)
temporal difference learning
Temporal-difference learning (Sutton & Barto):
Want
Vt = rt + rt+1 + rt+2 + rt+3 + …
= rt + Vt+1
Use prediction error dt = [rt + Vt+1] – Vt
•
learn to predict cumulative future rewards rt + rt+1 +…
•
learn using what I predict at time t+1 (Vt+1) as stand in for all future rewards
–
•
so I don’t have to wait forever to learn
learn consistent predictions based on temporal difference Vt+1 – Vt
–
if Vt+1 = Vt, my predictions are consistent
–
if Vt+1 > Vt, things got unexpectedly better
–
if Vt+1 < Vt, things got unexpectedly worse
 and these act like reward to generate prediction error and learning
More dopamine responses
reward
following 0%
predictive cue
Prediction error:
Vt+1 = 0
dt = rt – Vt
reward
following 50%
predictive cue
reward following
100% predictive
cue
(Fiorillo et al 2003)
no reward
following 100%
predictive cue
More dopamine responses
reward
following 0%
predictive cue
Same story here
Vt = 0; rt = 0
dt = Vt+1
reward
following 50%
predictive cue
reward following
100% predictive
cue
(Fiorillo et al 2003)
no reward
following 100%
predictive cue
Dopamine responses interpreted
r(t)
V(t)
V(t+1) – V(t)
d(t) = r(t) + V(t+1) – V(t)
r(t)
V(t)
V(t+1) – V(t)
d(t) = r(t) + V(t+1) – V(t)
(Schultz et al. 1997)
How should this one look?
Law of Effect
“Of several
responses made to
the same situation,
those which are
accompanied or
closely followed by
satisfaction to the
animal will, other
things being equal,
be more firmly
connected with the
situation, so that,
when it recurs, they
will be more likely
to recur.”
Thorndike (1911)
policy p