Motor Learning with Unstable Neural Representations

Rokni, Uri; Richardson, Andrew G.; Bizzi, Emilio; Seung, H. Sebastian

doi:10.1016/j.neuron.2007.04.030

Cited by 234 publications

(255 citation statements)

References 38 publications

Supporting

Mentioning

228

Contrasting

Order By: Relevance

“…Some evidence from longitudinal recordings from primary visual and somatosensory cortices supports this idea (26,38). Results from the motor cortex are more variable, with some groups reporting consistent relations between behavior and the preferred direction of single neurons (39)(40)(41) and others finding some degree of change across days (5,42) or even within the course of a single session (6). Because activity outside of the primary sensory and motor cortical areas is less tightly coupled to action in the periphery, we might expect to see more change over time in higher level association areas.…”

Section: Discussionmentioning

confidence: 99%

“…However, in the absence of direct evidence, there is no reason to assume that this is the case, or more generally that stable network performance implies stable units. And indeed, both theoretical (4) and experimental results from motor cortex (5,6) demonstrate that a network composed of unstable units may nonetheless exhibit stable performance. Recent recordings from the mouse hippocampus showed that reliable location signaling is driven largely by neurons that gradually enter and leave the population of functionally active place cells over the course of several days (7).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Face-selective neurons maintain consistent visual responses across months

McMahon

Jones

Bondar

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Face perception in both humans and monkeys is thought to depend on neurons clustered in discrete, specialized brain regions. Because primates are frequently called upon to recognize and remember new individuals, the neuronal representation of faces in the brain might be expected to change over time. The functional properties of neurons in behaving animals are typically assessed over time periods ranging from minutes to hours, which amounts to a snapshot compared to a lifespan of a neuron. It therefore remains unclear how neuronal properties observed on a given day predict that same neuron's activity months or years later. Here we show that the macaque inferotemporal cortex contains face-selective cells that show virtually no change in their patterns of visual responses over time periods as long as one year. Using chronically implanted microwire electrodes guided by functional MRI targeting, we obtained distinct profiles of selectivity for face and nonface stimuli that served as fingerprints for individual neurons in the anterior fundus (AF) face patch within the superior temporal sulcus. Longitudinal tracking over a series of daily recording sessions revealed that face-selective neurons maintain consistent visual response profiles across months-long time spans despite the influence of ongoing daily experience. We propose that neurons in the AF face patch are specialized for aspects of face perception that demand stability as opposed to plasticity.vision | fMRI | physiology M any biological systems perform in a stable manner over time. The consistent behavior that is evident at the global level of the organism can persist despite continuous change in the system's component parts. A cup of coffee, for instance, tastes the same today as it did one year ago, despite the fact that the receptors in our taste buds are replaced every two weeks (1). In addition to the continuous replacement of individual cells, the protein constituents of cells likewise undergo nearly continuous turnover (2). What happens in the central nervous system? Because the lifespan of most neurons in the brain approaches that of the organism (3), it is conceivable that the population of faceselective cells that fire today when you see your mother, for instance, is identical to the population that fired under the same circumstances 10 years ago. However, in the absence of direct evidence, there is no reason to assume that this is the case, or more generally that stable network performance implies stable units. And indeed, both theoretical (4) and experimental results from motor cortex (5, 6) demonstrate that a network composed of unstable units may nonetheless exhibit stable performance. Recent recordings from the mouse hippocampus showed that reliable location signaling is driven largely by neurons that gradually enter and leave the population of functionally active place cells over the course of several days (7). In that study, only 15% of longitudinally monitored neurons were found to retain the same place fields in two sessions that were separ...

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Face-selective neurons maintain consistent visual responses across months

McMahon

Jones

Bondar

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…It was recently suggested that redundancy in the motor cortex provides robustness to noise in the system by allowing changes in neuronal activity that do not significantly affect behavior. Thus, the initial low trial-to-trial variability (which we observed) has been defined as "background" variability, which reflects a stable optimal manifold (rather than a single point) in the neuronal space in which all points produce a similar end result, but with different motor commands (Rokni et al, 2007).…”

Section: Suggested Framework For Motor Learningmentioning

confidence: 99%

“…Computational and modeling studies have put forward the intriguing idea that neuronal variability can be important for learning in general (Kirkpatrick et al, 1983;Fusi, 2002) and for motor learning in particular (Rokni et al, 2007;Fiete et al, 2007;Faisal et al, 2008). In other words, variability enables dynamic representations and continuous exploration of possible motor states and appropriate neuronal configurations that can lead to the desired state by trial and error.…”

Section: Introductionmentioning

confidence: 99%

Trial-to-Trial Variability of Single Cells in Motor Cortices Is Dynamically Modified during Visuomotor Adaptation

Mandelblat-Cerf¹,

Paz²,

Vaadia³

2009

J. Neurosci.

View full text Add to dashboard Cite

Neurons in all brain areas exhibit variability in their spiking activity. Although part of this variability can be considered as noise that is detrimental to information processing, recent findings indicate that variability can also be beneficial. In particular, it was suggested that variability in the motor system allows for exploration of possible motor states and therefore can facilitate learning and adaptation to new environments. Here, we provide evidence to support this idea by analyzing the variability of neurons in the primary motor cortex (M1) and in the supplementary motor area (SMA-proper) of monkeys adapting to new rotational visuomotor tasks. We found that trial-to-trial variability increased during learning and exhibited four main characteristics: (1) modulation occurred preferentially during a delay period when the target of movement was already known, but before movement onset; (2) variability returned to its initial levels toward the end of learning; (3) the increase in variability was more apparent in cells with preferred movement directions close to those experienced during learning; and (4) the increase in variability emerged at early phases of learning in the SMA, whereas in M1 behavior reached plateau levels of performance. These results are highly consistent with previous findings that showed similar trends in variability across a population of neurons. Together, the results strengthen the idea that single-cell variability can be much more than mere noise and may be an integral part of the underlying mechanism of sensorimotor learning.

show abstract

“…However, several recent studies provided evidence that neural encoding properties need not be stable under all circumstances. Instead, they can change dynamically with time, both within a trial and across repeated trials (Chestek et al, 2007;Churchland and Shenoy, 2007;Rokni et al, 2007). Such changes may be attributable to changes in contextual parameters.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Encoding of Movement Direction in Motor Cortical Neurons

Rickert¹,

Riehle²,

Aertsen³

et al. 2009

J. Neurosci.

View full text Add to dashboard Cite

When we perform a skilled movement such as reaching for an object, we can make use of prior information, for example about the location of the object in space. This helps us to prepare the movement, and we gain improved accuracy and speed during movement execution. Here, we investigate how prior information affects the motor cortical representation of movements during preparation and execution. We trained two monkeys in a delayed reaching task and provided a varying degree of prior information about the final target location. We decoded movement direction from multiple single-unit activity recorded from M1 (primary motor cortex) in one monkey and from PMd (dorsal premotor cortex) in a second monkey. Our results demonstrate that motor cortical cells in both areas exhibit individual encoding characteristics that change dynamically in time and dependent on prior information. On the population level, the information about movement direction is at any point in time accurately represented in a neuronal ensemble of time-varying composition. We conclude that movement representation in the motor cortex is not a static one, but one in which neurons dynamically allocate their computational resources to meet the demands defined by the movement task and the context of the movement. Consequently, we find that the decoding accuracy decreases if the precise task time, or the previous information that was available to the monkey, were disregarded in the decoding process. An optimal strategy for the readout of movement parameters from motor cortex should therefore take into account time and contextual parameters.

show abstract

Motor Learning with Unstable Neural Representations

Cited by 234 publications

References 38 publications

Face-selective neurons maintain consistent visual responses across months

Face-selective neurons maintain consistent visual responses across months

Trial-to-Trial Variability of Single Cells in Motor Cortices Is Dynamically Modified during Visuomotor Adaptation

Dynamic Encoding of Movement Direction in Motor Cortical Neurons

Contact Info

Product

Resources

About