Building neural representations of habits.

Memories for habits and skills ("implicit or procedural memory") and memories for facts ("explicit or episodic memory") are built up in different brain systems and are vulnerable to different neurodegenerative disorders in humans. So that the striatum-based mechanisms underlying habit formation could be studied, chronic recordings from ensembles of striatal neurons were made with multiple tetrodes as rats learned a T-maze procedural task. Large and widely distributed changes in the neuronal activity patterns occurred in the sensorimotor striatum during behavioral acquisition, culminating in task-related activity emphasizing the beginning and end of the automatized procedure. The new ensemble patterns remained stable during weeks of subsequent performance of the same task. These results suggest that the encoding of action in the sensorimotor striatum undergoes dynamic reorganization as habit learning proceeds.

A new striatal model and its relationship to basal ganglia diseases.

A new model of the striatum has recently been proposed. This model suggests that the somatotopic regions of the striatum correspond to state spaces governing various aspects of organism behavior (e.g., reaching, egomotion, task planning). The model is reviewed, and shown to be applicable to sequencing tasks. The model is also discussed in the context of some basal ganglia diseases.

Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques.

The technical advances in hardware and software for multiunit recordings have made it easier to gather data from a large number of neurons for behavioral correlations. This paper discusses several such advances in implantable hardware, magnetic resonance imaging of electrodes in situ, and data analysis software for multiple simultaneous signals.

A model for the functioning of the striatum.

A model is presented for the operation of the striatum. The model posits that the basal ganglia are responsible for driving smooth transitions of state for an organism. We propose that this is accomplished through the computation of a potential function within the striatum on which a gradient descent is performed toward the goal state. The model suggests that various somatotopic regions of the striatum correspond to state spaces, each of which pertains to a different aspect of the organism. This paper discusses this model only in the context of motor control, i.e., egomotion and limb movement. The model appears to account for a variety of experimental results, and for some unusual properties of the striatum.

A dynamical-systems model for Parkinson's disease.

The juxtaposition of hypokinetic and hyperkinetic symptoms in Parkinson's disease (PD) presents a challenge in modeling the basal ganglia. We propose a model of the striatum that can account for the mixture of symptoms seen in PD. In the model, the problem of motor planning is cast in terms of a particle in a potential, where potentials are generated internally in striatal modules, subject to afferent control. Planned movement is governed by Hamilton's equations, where potential energy is supplied by potentials expressed in the striatum. To test the model in realistic situations, a dynamic simulation of a two-link robot arm was used. Normal movement is modeled and shown to exhibit observed experimental properties. Symptoms of PD are reproduced by modeling hypothetical consequences of PD pathology.