Stable Ensemble Performance with Single-Neuron Variability during Reaching Movements in Primates

Carmena, Jose M.; Lebedev, Mikhail А.; Henriquez, Craig S.; Nicolelis, Miguel A. L.

doi:10.1523/jneurosci.2772-05.2005

Cited by 147 publications

(146 citation statements)

References 26 publications

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“…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).…”

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confidence: 99%

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Face-selective neurons maintain consistent visual responses across months

McMahon

Jones

Bondar

et al. 2014

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

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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...

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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.

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“…The issue of classification has long been a central topic in the analysis of multielectrode data, either for spike sorting or for getting insight into interactions among ensembles of neurons (Fernandez et al, 2000;Nicolelis, 2003;Shoham et al, 2003;Carmena et al, 2005;Fernandez et al, 2005;Nicolelis, 2005;Suner et al, 2005;Hochberg et al, 2006). A major challenge in this context is to acquire meaningful data from different functional types of neurons, but there is not a standard way for addressing how many neuronal types are in a given multielectrode recording.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks and Retinal Ganglion Cell Responses

Bonomini¹,

Ferrández²,

Fernández³

2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

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“…Furthermore, isolations are typically stable for hours, in some cases days. Thus, the desire to collect a good deal of data quickly from stable isolations can provide sufficient motivation [e.g., 12], particularly if neural plasticity is the topic of study [e.g., 13,14]. Still, the strongest motivations relate to the simultaneous nature of array recordings, and the need to know how the activity of different neurons varies together across not-quite-identical trials [15].…”

Section: General Motivations For Implanted-array Recordingmentioning

confidence: 99%

Techniques for extracting single-trial activity patterns from large-scale neural recordings

Churchland

Sahani

et al. 2007

Current Opinion in Neurobiology

137

115

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SummaryLarge, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies -some employing simultaneous recording, some not -indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior.

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Stable Ensemble Performance with Single-Neuron Variability during Reaching Movements in Primates

Cited by 147 publications

References 26 publications

Face-selective neurons maintain consistent visual responses across months

Face-selective neurons maintain consistent visual responses across months

Artificial Neural Networks and Retinal Ganglion Cell Responses

Techniques for extracting single-trial activity patterns from large-scale neural recordings

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