Elsevier

Journal of Physiology-Paris

Volume 106, Issues 3–4, May–August 2012, Pages 112-119
Journal of Physiology-Paris

Synthesizing complex movement fragment representations from motor cortical ensembles

https://doi.org/10.1016/j.jphysparis.2011.09.003Get rights and content

Abstract

We have previously shown that the responses of primary motor cortical neurons are more accurately predicted if one assumes that individual neurons encode temporally-extensive movement fragments or preferred trajectories instead of static movement parameters (Hatsopoulos et al., 2007). Building on these findings, we examine here how these preferred trajectories can be combined to generate a rich variety of preferred movement trajectories when neurons fire simultaneously. Specifically, we used a generalized linear model to fit each neuron’s spike rate to an exponential function of the inner product between the actual movement trajectory and the preferred trajectory; then, assuming conditional independence, when two neurons fire simultaneously their spiking probabilities multiply implying that their preferred trajectories add. We used a similar exponential model to fit the probability of simultaneous firing and found that the majority of neuron pairs did combine their preferred trajectories using a simple additive rule. Moreover, a minority of neuron pairs that engaged in significant synchronization combined their preferred trajectories through a small scaling adjustment to the additive rule in the exponent, while preserving the shape of the predicted trajectory representation from the additive rule. These results suggest that complex movement representations can be synthesized in simultaneously firing neuronal ensembles by adding the trajectory representations of the constituents in the ensemble.

Highlights

Motor cortex encodes temporally extensive velocity trajectories called pathlets. ► Pathlets from neuron pairs can be predicted by adding the neurons’ pathlets. ► The additive rule follows from the exponential encoding model given independence. ► The additive rule underestimates simultaneous firing of synchronized neurons. ► For synchronized neurons, the rule can be adjusted with a simple gain factor.

Introduction

At the beginning of the 20th century, Sherrington proposed that the motor cortex was the site where complex motor actions were synthesized (Leyton and Sherrington, 1917). According to this proposal, individual motor cortical neurons represent elementary motor actions, and complex movements are synthesized by the coordinated activations of multiple motor cortical neurons which are mediated by the extensive anatomical connectivity among these neurons (Huntley and Jones, 1991, Keller, 1993). This is very different than the contemporary viewpoint that motor cortical neurons encode abstract movement parameters such as position, velocity, direction, or force (Evarts, 1968, Smith et al., 1975, Hepp-Reymond et al., 1978, Georgopoulos et al., 1982, Georgopoulos et al., 1984, Moran and Schwartz, 1999, Paninski et al., 2004). However, we have recently provided evidence that the encoding properties of single primary motor cortical neurons (MI) are more accurately characterized as temporally-extensive movement trajectories which is reminiscent of the original Sherringtonian view of motor cortical encoding. In particular, we have shown that MI neurons possess preferred movement trajectories that extend several hundred milliseconds in duration. By temporally integrating these preferred trajectories, we find that individual neurons encode fragments of movement (termed “pathlets”) that possess unique spatiotemporal shapes. Pathlet representations have been experimentally demonstrated during constrained two-dimensional reaching movements and, more recently, during grasping movements (Hatsopoulos et al., 2007, Saleh et al., 2010).

In this work, we sought to test the second part of Sherrington’s proposal by showing how the co-activation of multiple MI neurons in an ensemble represent movement trajectories which are synthesized from the movement fragments represented by the constituent neurons in the ensemble. In particular, in the vast majority of cases, the simultaneous firing of two MI neurons encodes a preferred movement trajectory which is the linear sum of the preferred trajectories of each of the neurons in the pair which we term the “additive rule”. Moreover, we show that a scaled version of the additive rule applies to neuronal pairs that engage in significant synchronization.

Section snippets

Behavioral task

Three macaque monkeys (Macaca mulatta) were operantly trained to perform a random-sequence task (RS) by moving a cursor to targets via contralateral arm movements (Fig. 1A). A sequence of seven targets appeared on the projection surface. At any one time, a single target appeared at a random location in the workspace, and the monkey was required to move to it. As soon as the cursor reached the target, the target disappeared and a new target appeared in a new, random location. After reaching the

Results

We recorded extracellularly from multiple single units in MI using chronically implanted electrode arrays while monkeys performed a random-sequence (RS) task with their arm (see Fig. 1A). Instead of prescribing a limited set of movements, we had the animal generate a rich variety of trajectories and paths with different spatial shapes, velocities, and positions. We then applied a generalized linear model (Eq. (2)) to fit the observed spiking response for each neuron individually to an

Discussion

We have demonstrated how the repertoire of trajectory representations can be expanded by combining the preferred trajectories of the constituent neurons in simultaneously firing neuronal pairs. For those neurons in an ensemble that fire in a conditionally independent manner, we propose a very simple additive rule by which their individual trajectory representations are combined. Among the cell pairs that we examined, the vast majority did not engage in significant synchronization. In this case,

Acknowledgements

We thank Elise Covic, Adam Dickey, Matthew Fellows, Zach Haga, and Dawn Paulsen, Aaron Suminski, and Qingqing Xu for their help with the surgical implantation of the arrays, training of monkeys, data collection and analysis. This work was supported by National Institute of Neurological Disorders and Stroke Grant R01 NS045853.

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