1 Spiking neuron models of the basal ganglia: dopaminergic modulation of selection and oscillatory properties Kevin Gurney, Mark Humphries, Rob Stewart Adaptive Behaviour Research Group University of Sheffield, UK
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Spiking neuron models of the basal ganglia: dopaminergic modulation of selection and oscillatory properties
Kevin Gurney, Mark Humphries, Rob Stewart
Adaptive Behaviour Research Group
University of Sheffield, UK
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Rationale: basal ganglia and action selection
Aim: to understand underlying function of basal ganglia.
While learning is crucial – what is being learned?
Hypothesis: Main computational role of basal ganglia is to perform action selection
Supported by high (systems) level model Simple leaky integrators to represent population
dynamics
BUT…..
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Beyond the systems level
Do more realistic models support the selection hypothesis? Constraints provided by:
Specific neuronal properties Physiological phenomena displayed by BG in toto…. If the price of a model performing selection is its failure exhibit these
phenomena, the selection hypothesis is in question
In particular, can models display oscillatory phenomena in BG?
If so, then we can use the model to explore possible function of these oscillations
Function or artifact?!
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Systems level – the model architecture
cf, Hazrati and Parent, 1992, Mink and Thach 1993, Nambu et al 2000, Sato et al 2000
• Assumes relatively diffuse projection from STN• Emphasises STN’s role as input nucleus
striatum STN
Cortex (‘salience’ input)
output nuclei
- + Striatum
input
STN
output
Diffuseprojection
3 ‘channels’
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New functional architecture:selection and control pathways
Interpret GP efferents as control signals for modulating selection pathway
Gurney et al, 2001
Selection pathway Control pathway
Striatum (D2)Striatum (D1) STN
EP/SNr GP
Cortex/thalamus
Diffuseprojection
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Oscillations in basal ganglia: matching mechanisms to phenomena
Basal ganglia display a wide range of oscillatory phenomena – from <1Hz to >100Hz
These are probably associated with a correspondingly wide range of underlying mechanisms
We focus on four BG features. Intrinsic nature of STN-GP coupling Dopaminergic modulation of this coupling Rebound bursting in STN Synaptic patterning
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Constructing the model
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Rebound bursting in STN
Time
Current
IK
Beurrier et al 1999
ILIT
IK > IL burst ends
inhibition
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Importance of synaptic patterning Inhibition at soma or proximal dendrites acts divisively (rather than ‘subtractively’)70% of GPe input is proximal or somatic (Bevan et al 1997)
cortex STN
GP
Proximal dendrites somaDistal dendrites
Captured phenomenlogically: use inhibition in proximal dendrites/soma to explicitly ‘gate’ more distal input
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Dopaminergic action in striatum
Glu
Ctx
Striatum
Glu
Ctx
StriatumDAD1
Increased PSP
DAD2
decreased PSP
W = W0(1 + λ) W = W0(1 - λ)
λ < 1
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GABA
GP
STN
Dopaminergic action in STN
Glu
Ctx
STN
DAD2 DA
D2
W = W0(1 – k1 λ) W = W0(1 – k2 λ)
K1, k2 < 1
Similar story in GP…
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Dopamine: hypotheses
Low levels of dopamine serve to couple STN and GP more tightly and to make STN more sensitive to its input
Dopamine in striatum will make channel selection easier to achieve
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Model neurons: summary Leaky Integrate and Fire with
AMPA NMDA, GABA, synaptic currents Shunting inhibition at proximal dendrites and soma Spontaneous currents Rebound bursting in STN, Dopamine in striatum, STN and GP. Inter-neuronal delays
All of the above parametrised by best estimates from the literature
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Network Based on systems level model
3 discrete channels
64 neurons per channel, per nucleus
Probabilistic connection scheme within channels (only 25% of all possible connections made)
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Constraining phenomena 1: Low frequency oscillations in STN-GP
(Magill et al, Neuroscience,106, 2001)
Low frequency oscillations (LFOs) in STN are driven by cortical slow wave under urethane anaesthesia.
GP does not oscillate in control (normal DA) conditions. Only shows oscillation under dopamine depletion (6-OHDA lesion)
Residual LFOs (with 6-OHDA lesion) in STN and GP under cortical ablation
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Data – STN control
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20Hz
1s
STN unitactvity
Model - STN control
Multi-taper spectrum
Frequency (Hz)
0 1 2 3 4 5
Pow
er
0
100
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
10
20
Spike Trig Wav av
Time(s)
-1.0 -0.5 0.0 0.5 1.0
mea
n fir
ing
rate
(H
z)
0
10
20
30
Pseudo-eeg
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Data – GP control
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20Hz
1s
GP unitactvity
Model - GP control (1)
Spike Trig Wav av
Time(s)
-1.0 -0.5 0.0 0.5 1.0m
ean
firin
g ra
te (
Hz)
0
10
20
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
50
Multi-taper spectrum
Frequency (Hz)
0 10 20 30 40 50
Pow
er
0
25
50
20
20Hz
1s
GP unitactvity
Model GP control (2)
Multi-taper spectrum
Frequency (Hz)
0 25 50
Pow
er
0
100
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
50
Spike Trig Wav av
Time(s)
-1.0 -0.5 0.0 0.5 1.0
mea
n fir
ing
rate
(H
z)
0
10
20
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Data – STN DA-depleted
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20Hz
1s
STN unitactvity
Model STN DA-depleted
Multi-taper spectrum
Frequency (Hz)
0 1 2 3 4 5
Po
wer
0
1000
Auto corr.
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
250
Spike Trig Wav av
Time(s)
-1.0 -0.5 0.0 0.5 1.0
mea
n fir
ing
rate
(H
z)
0
10
20
30
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Data – GP DA-depleted (in phase)
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20Hz
1s
GP unitactvity
Model - GP DA-depleted (in-phase)
Multi-taper spectrum
Frequency (Hz)
0 1 2 3 4 5
Pow
er
0
100
200
2D Graph 7
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
50Spike Trig Wav av
Time(s)
-1.0 -0.5 0.0 0.5 1.0m
ean
firin
g ra
te (
Hz)
0
10
20
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Data – GP DA-depleted (anti-phase)
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20Hz
1s
GP unitactvity
Model - GP DA-depleted (anti-phase)
Multi-taper spectrum
Frequency (Hz)
0 1 2 3 4 5
Pow
er
0
500
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
50
Spike Trig Wav av
Time(s)
-1.0 -0.5 0.0 0.5 1.0m
ean
firin
g ra
te (
Hz)
0
10
20
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Data – cortical ablation and DA-depleted
Most neurons do not show LFOs but residual LFO activity…
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Model - no cortex (DA-depleted)
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
50
100
Multi-taper spectrum
Frequency (Hz)
0 1 2 3 4 5
Pow
er
0
100
Multi-taper spectrum
Frequency (Hz)
0 25 50
Pow
er
0
Time(s)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Bin
cou
nt
0
10
20
1s
1s
STN
GP
Multi-taper spectrum
Frequency (Hz)
0 1 2 3 4 5
Pow
er
0
25
50
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LFO countsS
TN +
ctx
GP
+ct
x
STN
-ctx
GP
-ctx
STN
+ct
x
GP
+ct
x
STN
-ctx
GP
-ctx
LFO
s (%
)
0
25
50
75
100
Control Data
Control Model
DA dep. data
DA dep. model
In DA control conditions, no GP LFOs, STN driven by cortex
LFO in GP promoted by DA depletion
Residual LFO in STN & GP under cortical ablation
Neuron is LFO if significant peak in power spectrum below 1.5Hz
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Mean firing rates
STN
+ct
x
GP
+ct
x
STN
-ctx
GP
-ctx
STN
+ct
x
GP
+ct
x
STN
-ctx
GP
-ctx
Mea
n fir
ing
rate
s (H
z)
0
10
20
Control Data
Control Model
DA dep. data
DA dep. model
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LFO – mechanistic explanation Low frequency oscillations associated with
rebound bursting will be ‘unmasked’ at low levels of dopamine….
GP more likely to generate pre-conditioning hyperpolarisation
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Constraining phenomena 2: gamma oscillations in STN
(Brown et al., Exp Neuro. 177, 2002)
There is gamma oscillation (40-80Hz) in alert rats
This is increased (86% mean) by systemic D2 agonist (quinpirole)
Local field potential spectrum (control)
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Frequency
0 20 40 60 80 100
Pow
er
0
20
40
60
Frequency
0 20 40 60 80 100
Pow
er
0
20
40
60
Model simulated D2 agonist
Frequency (Hz)
0 20 40 60 80 100
No.
of
sini
fican
t pe
aks
0
20
40
60ControlDA agonist
DA=0.2control
DA=0.8‘D2 agonist’
128% power
increase →
Mean power spectra (192 neurons)
Peaks in power spectrum
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Gamma oscillations: explanation Gamma oscillations are associated with the
natural frequency of oscillation of the GP-STN circuit
determined by circuit delays
At control levels of dopamine, the presence of some LFO masks gamma
Can’t be doing gamma during quiet phase of LFO period.
At higher levels of dopamine, gamma is unmasked
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Selection experiments
Cortical input(Mean firing rate)
time
ch1
ch2
1 2.5
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Selection and switching
Ctx Ch1: 20 HzCtx Ch2: 40 Hz
Time
Mea
n fir
ing
rate
SN
r
ch2ch1 ch3
ctx
time
ch1
ch2
1 2.5
Firing rate
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DA depletion prevents selection
Ctx Ch1: 12 HzCtx Ch2: 20 Hz
Time
Mea
n fir
ing
rate
SN
r
Firing rate
ctx
time
ch1
ch2
1 2.5
ch2ch1 ch3
LFOs?
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Effects of DA depletion overcome by highly salient action
Time
Mea
n fir
ing
rate
SN
r
Ctx Ch1: 20 HzCtx Ch2: 40 Hz
Firing rate
ctx
time
ch1
ch2
1 2.5
ch2ch1 ch3
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DA increase results in simultaneous selection
Ctx Ch1: 20 HzCtx Ch2: 40 Hz
Time
Mea
n fir
ing
rate
SN
r
Firing rate
ctx
time
ch1
ch2
1 2.5
ch2ch1 ch3
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Summary A spiking model of BG constrained by known
physiology is able to account for a range oscillatory phenomena
Oscillations are modulated under Dopaminergic control of STN and GP
The same model displays selection and switching properties, thereby supporting the selection hypothesis for BG function
Currently exploring computational role of LFOs Perturb BG to selection in otherwise unresolved selection
competition?
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The adaptive behaviour research group
Peter RedgravePaul Overton
Kevin GurneyTony Prescott
Mark HumphriesBen Mitchinson
Rob StewartRic Wood
Jonathan Chambers
Tom Stafford Ψ