GENERAL COMMENTARY
published: 05 February 2010
doi: 10.3389/neuro.07.001.2010

INTEGRATIVE NEUROSCIENCE

Shifting to automatic
Leonardo Restivo1 and Paul W. Frankland 1,2*1

Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
Department of Physiology and Institute of Medical Science, University of Toronto, Toronto, ON, Canada*Correspondence: paul.frankland@sickkids.ca

2

A commentary on
Dynamic reorganization of striatal circuits
during the acquisition and consolidationof a skill.
by Henry H. Yin, Shweta Prasad Mulcare,
Monica R. F. Hilário, Emily Clouse, Terrell
Holloway, Margaret I. Davis, Anita C.
Hansson, David M. Lovinger and Rui M.
Costa (2009). Nat. Neurosci. 12, 333–341.
How does the brain acquire and retain
complex motor kills? In a well-known zen
story, a centipede was asked how he could
coordinate all of his numerous feet withoutstumbling. The centipede said that he had
never given it a thought. From that time on,
the centipede became unable to move. This
story nicely illustrates the idea that many
motor skills that we commonly use throughout our life are mostly automatic and notaccessible to conscious recall. The benefit ofthis shift to automation (or habit) is that it
frees us from continuously allocating attentional resources to motor sequences, making multi-tasking a possibility. However,
habit formation represents only the laststage of the complex process of motor skill
learning: the acquisition of motor skills ischaracterized by an initial phase of rapid
improvement of the performance whichis then followed by a later phase during
which memory becomes more automaticas performance reaches an asymptotic level(Shiffrin and Schneider, 1977).
Learning new motor sequences likelydepends on a wide network of brain
areas, necessarily involving the interaction
between sensory and motor systems (seePennartz et al., 2009 for recent progress).
The fine tuning of new motor sequences
is achieved through effortful evaluation ofsensory information about the outcome of
the movements, and with sufficient repetition only sensory feedback is needed tolink movement sequences. This two-stage
process of motor skill acquisition suggests

Frontiers in Integrative Neurosciencethat the brain networks underlying theexpression of motor performance gradually reorganize over time. Indeed, recentevidence indicates that two distinct compartments of the monkey striatum contribute differentially to the early and latestages of procedural learning (Miyachi et al.,2002). The dorsomedial region of the striatum (DMS), which is primarily innervatedby association cortices, seems to be preferentially recruited during initial stages of
visuo-motor learning (Miyachi et al., 2002).
In contrast, the dorsolateral portion (DLS),
which is primarily innervated by sensorimotor areas, is more important for the gradualacquisition of automatic behavior (Miyachiet al., 1997; Tang et al., 2007).
Building on these observations, Yinet al. (2009) now identify region specificchanges in striatal neural activity that map
onto different phases of skill learning. Yinand colleagues implanted mice with microelectrode arrays aimed at the DMS and theDLS and trained them in the acceleratingrotarod task. The rotarod task requires
the acquisition of complex movementsthat take both time and practice to learn.
As with many new skills, performance inthis task is characterized by rapid initial
improvement on the first day of training,with performance asymptoting after 3 daysof training. The authors used this task tofinely dissect early and late phases of theskill learning process.
In vivo recordings showed a functional
dissociation between striatal regions: DMS
neurons showed robust rate modulation
during early training phase, while DLS
showed increased rate modulation during
the extended training period. Accordingly,
DMS excitotoxic lesions impaired skill
learning when performed early during
training, but had no effect when performed
after extended training. In contrast, DLS
lesions affected both early and late phasesof training suggesting that DLS and DMS
activation is required to acquire the motor

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skill, while the DMS is disengaged as theskill becomes more automatic.
Ex vivo recordings showed that DMS
and DLS medium spiny neurons exhibitedtraining phase-related changes in glutamatergic transmission. EPSP slope measured
in response to increasing afferent stimulation in the two striatal regions revealed thatthe average synaptic strength was higher inDMS during early training, but increased in
the DLS after extended training.
Medium spiny striatal neurons can besegregated into two distinct populations
on the basis of their projections: striatonigral neurons, projecting to substantia
nigra, and striatopallidal neurons which
send their projections to the external
globus pallidus. These two populations
exhibit distinct physiological properties
and express different dopaminergic receptors, with striatonigral and striatopallidal
neurons preferentially expressing D1 andD2 receptors, respectively. Using D2-eGFP
mice, Yin and colleagues found that D2
expressing striatopallidal neurons locatedin DLS exhibited a significant increase insynaptic strength in comparison to D1
expressing neurons from the same region
when mice underwent extended training.
Accordingly, D1 blockade had no effecton performance when injected during
the extended training phase, whereas D2
blockade impaired performance at both
early and late training phases. Therefore,
these data suggest that skill automatization likely involves an increase in synaptic activation of D2 expressing medium
spiny neurons in the DLS. Yin and colleagues also noted that D2 expressingneurons have more and stronger inhibitory projections on D1 expressing neurons than the converse (Taverna et al.,2008), a difference which might provide
a mechanistic basis for neural competition between striatopallidal and striatonigral circuits during skill learning andsubsequent consolidation.

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Restivo and Frankland

The study by Yin and colleagues brings
new, compelling evidence that functional
reorganization of the internal circuitry of
the striatum accompanies different stagesof skill learning. This finding is further supported by Kimchi et al. (2009) data showingthat progressive change of DLS activity islikely to occur also during learning of an
instrumental task. Such dynamic reorganization of brain circuits seems to be a fundamental property of learning systems where
initial information is subjected to rapid degradation and new memory traces have to
be functionally integrated with previouslyacquired information (McClelland et al.,
1995; Frankland and Bontempi, 2005). The
advantage of functional circuit reorganization resides in the capability to integrate
new information into several distinct neuralcontexts, making the new memory accessible in various situations. Moreover, as a consequence of functional embedding of newmemories into distinct neuronal networks,
memory traces may eventually become lessprone to degradation and more durable.
What happens to the skill memory
once it has been acquired? Recent evidenceshows that neural rewiring rapidly occurs
in the motor cortex after training mice inthe accelerating rotarod (Yang et al., 2009).
These data might raise the question whether
motor cortex rewiring precedes, co-occurs

Frontiers in Integrative Neuroscience

Shifting to automaticor is a consequence of synaptic strengthchanges taking place in the DLS. It is tempting to speculate that skill learning involvesa three stage process where initial effortfullearning is accomplished by DMS due toits connections with associative prefrontal
cortices. Automatization might be subsequently achieved through a strengtheningof synapses onto DLS neurons projecting
to basal ganglia and thalamic structures
which eventually convey information to themotor cortex where the new skill memory isembedded in a more ample, cortical-based,motor repertoire.

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Received: 20 December 2009; published online: 05 February
2010.
Citation: Restivo L and Frankland PW (2010)
Shifting to automatic. Front. Integr. Neurosci. 4:1. doi:
10.3389/neuro.07.001.2010
Copyright © 2010 Restivo and Frankland. This is an
open-access article subject to an exclusive license agreement between the authors and the Frontiers ResearchFoundation, which permits unrestricted use, distribution,and reproduction in any medium, provided the original
authors and source are credited.

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