2/11/2007

Bischoff(Grethe)-Arbib Basal Ganglia Modeling

Presented by James Bonaiuto

2/11/2007

Amanda Bischoff (Grethe)’s Thesis

• Models the basal ganglia (and some cortical areas) in three tasks:– Elbow flexion-extension– Reciprocal aiming– Sequential arm movements

• Dopamine levels were modified to model the effects of Parkinson’s

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Hypothesis on Basal Ganglia Function

• Basal Ganglia– Indirect pathway –

movement inhibition– Direct pathway –

provides next sensory state to cortex

• Cortex– Preparatory areas –

project to indirect path– Movement-related

areas – project to direct pathway

2/11/2007

Model Overview - Cortex

• Pre-SMA– Projects sequential information to SMA and indirect pathway

• SMA– Contains information on the overall sequence – Keeps track of which movement is next – Project current movement to MC and direct pathway of basal

ganglia– Project next movement to premovement population in MC and

indirect pathway of basal ganglia• Motor Cortex

– Carries out motor command – Handles fine-tuning of movement– Projects motor parameters to brainstem and direct pathway of

basal ganglia

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Next Sensory State Information

• Why aren’t the basal ganglia responsible for movement initiation?– Crutcher & Alexander (1990) – movement related

putamen neurons fire an average of 33 ms after the onset of a movement (after activation of MC – 56ms later, and SMA – 80 ms later)

– Mink & Thach (1991b) – movement-related activity in GPe and GPi is also late

– Turner & Anderson (1997) – GP neurons rarely change discharge before activity of agonist muscles

2/11/2007

Basic Model

• Segregated direct (movement)/indirect (preparation) pathways

• Neat modeling trick:– To model up/down states

of putamen neurons, the time constant is a sigmoid of the membrane potential

– Same trick is used later to slowdown the cortex in the absence of dopamine

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Elbow Flexion-Extension

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Elbow Flexion-Extension - Results

<Demonstration>

2/11/2007

Reciprocal Aiming

• Winstein et al. (1997) – Stylus tapping between two targets of varying sizes

• Fitt’s Law – speed/accuracy tradeoff– ID=log2(2A/W)– MT=a+bID

• Parkinson’s patients– Slower overall time– Constrained trajectory– Reached to smaller area of target

• Predictions: – Slower speed is due to inability of BG to release inhibition of

movement – Decrease in SMA and MC activity causes reduction in speed

and variation of movement

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Reciprocal Aiming - Model

• Input: target positions in joint space– Problem when targets

overlap in joint space• SMA_INH prepares

upcoming movement – BG inhibits before appropriate– WTA– only fires in relation to

movement in preparation• SMA_MVT receives info from

both targets– Inhibition from SMA_INH –

only responds to current target

• MC_MVT– Encodes joint coordinates -

converted to Cartesian space

– Movement time calculated from firing rate

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Reciprocal Aiming - Results

• Normal - Qualitatively similar to Winstein et al.’s (1997) control data

• 50% Dopamine– No contact with target, no pause between

movements• Because neural part of model taking less time

than arm– Hypothesis: slowdown in putamen function

may cause slowdown in cortex too• Changed time constants of SMA and MC to

depend on dopamine level• With dopamine depletion – takes longer for

neurons to reach maximum and maximum is less than with dopamine (because of longer time constant)

• Reduction in MC firing rates causes delays between movements

• Caused restricted arm trajectory – lower velocity

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Reciprocal Aiming Results

SMA-Proper Motor Cortex

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Reciprocal Aiming Results

Putamen GPe STN GPi SNc

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Reciprocal Aiming Results

50% Dopamine 20% DopamineNormal

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Sequential Arm Movements

• Extends SMA module for a sequence of three movements

• Tanji & Shima (1994) – SMA neurons selective for sequence order, others selective for movement no matter where it was in a sequence

• Tanji & Mushiake (1996) - Pre-SMA active for visual stimuli – indicate sequence to be performed

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Sequential Arm Movements - Model

• Pre-SMA– Now selective for different sequence

permutations• SMA

– New population selective for different sequence permutations and subsequences

• After the current movement begins, SMA_INH primes SMA_MVT for the next movement

• MC_MVT needs to reach a threshold firing rate to produce target for movement generator

• Hardcoded relationships between SMA_SEQ, SMA_MVT and SMA_INH

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Sequential Arm Movements - Results

• Seq123 and seq12 active until target 1 reached• Seq12 primes target 2 neurons in SMA_PROPER_INH and seq23• Target 1 reached – seq23 reaches full activation• Seq23 primes target 3 neurons in SMA_PROPER_INH• Drop dopamine - seq123 is active longer

– MC_MVT peaks for each movement lower than for previous one - each movement depends on activation from previous movement

SMA-Proper Motor Cortex

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Sequential Arm Movements - Results

Putamen GPe STN GPi SNc

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Sequential Arm Movements - Results

• Reduce dopamine– Beginnings of pause between each

submovement– Akinesia - took longer to initiate 1st

movement– Bradykinesia – each movement take

longer and longer

• Indirect pathway is overactive (inhibits motor programs), direct pathway is less capable of responding to current motor command

• Slower time constant and higher GPi inhibition -> SMA doesn’t know status of current motor program so doesn’t command the next movement

Normal

50%Dopamine

20%Dopamine

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Discussion

• Can the same model do all three tasks?– Reciprocal aiming and flexion-extension can be cast

as 2 movement sequences– Requires new weights for the SMA_SEQ12 and

SMA_SEQ21 populations– How can these weights be learned?

• The future work section lists the inclusion of cortico-STN projections– The GPR model includes these, but has an opposite

take on the basal ganglia function (action selection)– Are these views reconcilable?