A Preliminary Model For The Role Of The Basal Ganglia In Natural Motor Control And Motor Programming

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A preliminary model for the role of the basal ganglia in natural motor control and motor programming MURI Meeting, June 4th, 2002 Steve Massaquoi, Zhi-Hong Mao, Eugene Lim, Munther Dahleh, and Eric Feron

Massachusetts Institute of Technology

Modeling the lower (motor) loop of the basal ganglia in movement • Our model produced reasonable representations of normal and abnormal basal ganglionic functions in lower loop – Cruise movement generation – Dysfunctions in Parkinson’s disease and dystonia

Proposed role of the basal ganglia in regulating cruise movement velocity

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θ target (t )

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1

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Basal ganglionic − cortical interaction

Neuronal integrator

θ command (t ) θcommand (t )

Plant

Movement

Muscular-skeletal system

θ )(t ) θ (t

θ (t ) θ (t )

Model simulator structure

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Dysfunction of the basal ganglia in Parkinson’s disease • Signs of Parkinson’s disease – Tremor at rest – Slowness of movement (bradykinesia) – Rigidity of body

Model simulator structure

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Dysfunction of the basal ganglia in dystonia • Signs of dystonia – Abnormal posture and tremor

Model simulator structure

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V e lo c it y

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Tim e (s e c ) 1.5

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Modeling the lower (motor) loop of the basal ganglia in movement • Our model produced reasonable representations of normal and abnormal basal ganglionic functions in lower loop – Cruise movement generation – Dysfunctions in Parkinson’s disease and dystonia

Role of the higher (cognitive) loop of the basal ganglia in programmed behavioral tasks • Conflict resolution over access to limited cognitive resources • Switching, brain states transition, and sequence generation • Circuit in higher loop is the same as in lower loop • A toy example

Conclusions and future work • Conclusions – The model is very simple – The model is consistent with the neuronal anatomy of the basal ganglia – The same circuit in the model is able to control cruise movement, to reproduce signs in the Parkinson’s disease and dystonia, and to execute programmed behavioral tasks

• Future work – Self tuning (learning) through reinforcement – Learning cooperative behavior

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