Neuronal Interactions Improve Cortical Population Coding of Movement Direction

Maynard, Edwin M.; Hatsopoulos, Nicholas G.; Ojakangas, Catherine L.; Acuna, Bettina D.; Sanes, Jerome N.; Normann, Richard A.; Donoghue, John P.

doi:10.1523/jneurosci.19-18-08083.1999

Cited by 259 publications

(193 citation statements)

References 54 publications

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“…Variations of noise correlations with respect to speed of moving targets and movement direction have been reported in pairs of MT and motor-cortical neurons, respectively (42,43). Our study provides a theoretical framework to test hypotheses on how a particular underlying connectivity and dynamical properties structure the fluctuations in neural networks.…”

Section: Discussionmentioning

confidence: 66%

Stimulus-dependent variability and noise correlations in cortical MT neurons

Ponce‐Alvarez

Thiele

Albright

et al. 2013

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

123

152

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Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multistability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.C ortical activity is highly variable during spontaneous activity (1-4) and even when tested under constant experimental conditions (5-8). This variability is thought to limit the capacity of individual neurons to transmit information (9). Furthermore, variability is often correlated among neurons, and thus, it cannot be completely removed by averaging the population response (9-12). Recent experimental studies have examined the secondorder statistics of neural responses across a variety of species, cortical areas, tasks, and stimulus and/or attentional conditions (13-17). In particular, it has been shown that the Fano factor (FF)-that is, the ratio between the variance of the spike counts over trials and its mean-is reduced when a stimulus is applied (16), thus improving the encoding of the stimulus. Importantly, both preferred and nonpreferred stimuli reduced the FF. In addition, the evoked noise correlation-that is, the trial-to-trial covariance of stimulus induced activity between two simultaneously recorded neurons-is also reduced upon stimulus presentation (18), after stimulus adaptation (19) or perceptual learning (20), and under attention (14, 21), an effect that could, under certain conditions, lead to more reliable estimates of the mean population activity (22). Hence, there is a growing body of evidence suggesting that the encoding of a signal through cortical activity may be improved by minimizing both trialby-trial variability and noise correlations. However, it remains an open experimental and theoretical question, whether these statistics are themselves tuned to different stimulus features, an aspect that may be overlooked when only analyzing preferred and nonpreferred stimuli.Here, we examined the statistics of responses of area-middle temporal (MT) neurons in awake, fixating primates, to moving gratings and different plaid patterns of different directions, as well as moving gratings of differ...

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Section: Discussionmentioning

confidence: 66%

Stimulus-dependent variability and noise correlations in cortical MT neurons

Ponce‐Alvarez

Thiele

Albright

et al. 2013

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

123

152

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“…Churchland et al (2006) determined that ϳ50% of the variance in performance was already present in these cortical neural signals rather than arising from the "motor noise" that has been attributed to discrete recruitment of size-ordered motor units (Jones et al, 2002). It is also likely that the cortical variability is itself correlated among multiple units (Maynard et al, 1999). Churchland et al (2006) interpreted this variability as noise, but it may be indicative of purposeful exploration of the complex solution space afforded by an SLR to which these neurons project.…”

Section: The General Utility Of Regulatorsmentioning

confidence: 99%

Spinal-Like Regulator Facilitates Control of a Two-Degree-of-Freedom Wrist

Raphael

Tsianos²,

Loeb³

2010

Journal of Neuroscience

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The performance of motor tasks requires the coordinated control and continuous adjustment of myriad individual muscles. The basic commands for the successful performance of a sensorimotor task originate in "higher" centers such as the motor cortex, but the actual muscle activation and resulting torques and motion are considerably shaped by the integrative function of the spinal interneurons. The relative contributions of brain and spinal cord are less clear for reaching movements than for automatic tasks such as locomotion. We have modeled a two-axis, four-muscle wrist joint with realistic musculoskeletal mechanics and proprioceptors and a network of regulatory circuitry based on the classical types of spinal interneurons (propriospinal, monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory, and Ib-inhibitory pathways) and their supraspinal control (via biasing activity, presynaptic inhibition, and fusimotor gain). The modeled system has a very large number of control inputs, not unlike the real spinal cord that the brain must learn to control to produce desired behaviors. It was surprisingly easy to program this model to emulate actual performance in four very different but well described behaviors: (1) stabilizing responses to force perturbations; (2) rapid movement to position target; (3) isometric force to a target level; and (4) adaptation to viscous curl force fields. Our general hypothesis is that, despite its complexity, such regulatory circuitry substantially simplifies the tasks of learning and producing complex movements.

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“…These studies have led to the view that cortical neurons act as independent processors of information (Gochin et al, 1994;Rolls et al, 1997Rolls et al, , 2004Reich et al, 2001;Purushothaman and Bradley, 2005). Although correlations between neurons may be important factors for neural coding (Zohary et al, 1994;Vaadia et al, 1995;Dan et al, 1998;Abbott and Dayan, 1999;Bair et al, 2001;Nirenberg and Latham, 2003;Averbeck and Lee, 2004;Johnson, 2004), few studies have demonstrated that considering neuronal interactions increases predictive information over that contained in individual neurons (Riehle et al, 1997;Hatsopoulos et al, 1998;Gat and Tishby, 1998;Maynard et al, 1999;Averbeck et al, 2003;Samonds et al, 2004). Gawne and Richmond (1993) and, more recently, Schneidman et al (2003) have proposed a theoretical framework for interpreting neuronal ensemble activity in which information encoded by an ensemble of neurons is compared with the sum of information encoded by the component neurons.…”

Section: Introductionmentioning

confidence: 99%

Redundancy and Synergy of Neuronal Ensembles in Motor Cortex

Narayanan

Kimchi²,

Laubach³

2005

J. Neurosci.

119

100

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We examined the ability of neuronal ensembles from rat motor cortex to predict behavioral performance during a reaction time task. We found that neurons that were the best individual predictors of task performance were not necessarily the neurons that contributed the most predictive information to an ensemble of neurons. To understand this result, we applied a framework for quantifying statistical relationships between neurons (Schneidman et al., 2003) to all possible combinations of neurons within our ensembles. We found that almost all neurons (96%) contributed redundant predictive information to the ensembles. This redundancy resulted in the maintenance of predictive information despite the removal of many neurons from each ensemble. Moreover, the balance of synergistic and redundant interactions depended on the number of neurons in the ensemble. Small ensembles could exhibit synergistic interactions (e.g., 23 Ϯ 9% of ensembles with two neurons were synergistic). In contrast, larger ensembles exhibited mostly redundant interactions (e.g., 99 Ϯ 0.1% of ensembles with eight neurons were redundant). We discuss these results with regard to constraints on interpreting neuronal ensemble data and with respect to motor cortex involvement in reaction time performance.

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Neuronal Interactions Improve Cortical Population Coding of Movement Direction

Cited by 259 publications

References 54 publications

Stimulus-dependent variability and noise correlations in cortical MT neurons

Stimulus-dependent variability and noise correlations in cortical MT neurons

Spinal-Like Regulator Facilitates Control of a Two-Degree-of-Freedom Wrist

Redundancy and Synergy of Neuronal Ensembles in Motor Cortex

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