Explaining Patterns of Neural Activity in the Primary Motor Cortex Using Spinal Cord and Limb Biomechanics Models

Abstract

Trainin E, Meir R, Karniel A. Explaining patterns of neural activity in the primary motor cortex using spinal cord and limb biomechanics models. J Neurophysiol 97: 3736 -3750, 2007. First published March 14, 2007; doi:10.1152/jn.01064.2006. What determines the specific pattern of activation of primary motor cortex (M1) neurons in the context of a given motor task? We present a systems level physiological model describing the transformation from the neural activity in M1, through the muscle control signal, into joint torques and down to endpoint forces and movements. The redundancy of the system is resolved by biologically plausible optimization criteria. The model explains neural activity at both the population, and single neuron, levels. Due to the model's relative simplicity and analytic tractability, it provides intuition as to the most salient features of the system as well as a possible causal explanation of how these determine the overall behavior. Moreover, it explains a large number of recent observations, including the temporal patterns of single-neuron and population firing rates during isometric and movement tasks, narrow tuning curves, non cosine tuning curves, changes of preferred directions during a task, and changes of preferred directions due to different experimental conditions. I N T R O D U C T I O NThe primate motor system is a highly complex system leading to sophisticated motor activities resulting from the concerted activity of many cortical, sub-cortical and skeletal modules involving multiple feedback loops (Dum and Strick 2005). One of the key components in this system is the primary motor cortex (M1), which plays a major role in voluntary limb movement. Projections from M1 influence muscles through direct synapses onto motorneurons and indirectly through spinal inter-neurons (Kandel et al. 2001). It is now well understood that M1 is highly heterogeneous (Alexander and Crutcher 1990; Ashe 2005;Crutcher and Alexander 1990;Kakei et al. 1999) and that different populations in M1 represent different motor control signals and therefore cannot be uniformly interpreted. However, physiology provides evidence that some M1 neurons are highly correlated with muscle activity (e.g., Fetz and Cheney 1980;Morrow and Miller 2003). We refer to such neurons as muscle-related cells. Our model is aimed at explaining the functional behavior of these muscle related cells in M1 in the context of arm movement.Many statistical models (e.g., Georgopoulos et al. 1982;Paninski et al. 2004;Sanger 1996) describe how neuronal activity of single neurons changes with hand variables and play an important role in neural prosthetic applications (Schwartz 2004). However, these approaches do not provide answers to the following basic questions. How do neural activities, projected from the motor cortex toward the spinal cord, result in hand movement and force? Given the redundancy of the controlled system (Bernstein 1967), how does the brain select specific control signals to achieve a motor task? The model provided here di...

Descending Corticospinal Control of Intersegmental Dynamics

“…This suggests that the output from M1 is involved in the compensation for limb inertia by modulating activity of the appropriate muscle groups in accordance with the dynamic loads caused by the interaction between coupled limb segments (Sergio and Ostry, 1994;Almeida et al, 1995;Ostry, 1998, 1999). Thus, our results support the hypothesis that the output of M1 contains information about the dynamic variables such as muscle forces (Scott and Kalaska, 1997;Cisek et al, 1998;Sergio and Kalaska, 2003;Sergio et al, 2005;Hamel-Pâquet et al, 2006;Trainin et al, 2007;Ajemian et al, 2008), rather than only kinematic variables such as movement direction (Georgopoulos et al, 1982;Schwartz et al, 1988;Caminiti et al, 1990;Fu et al, 1995;Bizzi et al, 2000;Mussa-Ivaldi and Bizzi, 2000). Our findings are also compatible with the "leading joint hypothesis" (Dounskaia et al, 2002), which proposes a hierarchical role for different joints in multijoint movements.…”

Concurrent adaptation of force and impedance in the redundant muscle system

“…In contrast to previous models based on numerical optimization (Burdet and Milner 1998;Harris and Wolpert 1998;Stroeve 1999;Guigon et al 2007;Trainin et al 2007) or (linear) optimal control (Todorov and Jordan 2002;Izawa et al 2008), which can be used to predict the behavior after learning, our model is able to predict how sensory information is used to modify the motor commands during the entire learning process, from one movement to the next. It may also be used to investigate convergence to a local optimum, Simulations of horizontal arm movements were performed using the two-joint six-muscle model described in this diagram which occurs in tasks with multiple optima such as slalom movements (Todorov and Jordan 1998), or cases when motor learning does not converge.…”

“…What is the computational mechanism of this learning? Optimization algorithms such as used in (Burdet and Milner 1998;Harris and Wolpert 1998;Stroeve 1999;Todorov and Jordan 2002;Guigon et al 2007;Trainin et al 2007;Izawa et al 2008) only predict final learning outcomes, while models created to predict gradual changes of force during adaptation (Kawato et al 1987;Katayama and Kawato 1993;Gribble and Ostry 2000;Thoroughman and Shadmehr 2000;Donchin et al 2003;Emken et al 2007) do not possess a mechanism to counteract mechanical instability and do not accurately predict the evolution of muscle activation observed during human motor learning (Franklin et al 2003b).…”

CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

“…Current neurophysiological models able to predict trial to trial modifications of force or torque (Kawato et al, 1987;Katayama and Kawato, 1993;Gribble and Ostry, 2000;Thoroughman and Shadmehr, 2000;Donchin et al, 2003;Emken et al, 2007) and corresponding nonlinear adaptive controllers for robots (Slotine and Li, 1991;, which use a monotonic antisymmetric (in most cases, linear) update of the feedforward command, have no explicit mechanism to alter the limb impedance independently from joint torque (or limb posture), and, therefore, cannot learn to compensate for unstable dynamics (Osu et al, 2003). Models based exclusively on optimization of cost functions such as minimization of end-point variance and/or muscle activation (Burdet and Milner, 1998;Harris and Wolpert, 1998;Stroeve, 1999;Todorov, 2000;Todorov and Jordan, 2002;Guigon et al, 2007;Trainin et al, 2007;Izawa et al, 2008) can only predict final learning outcomes, whereas our model can account for the complete progression of experimentally observed changes in force and impedance throughout learning. This algorithm, when combined with a method for generalization (Donchin et al, 2003), and a method for storing and accessing multiple internal representations (Haruno et al, 2001) could provide a powerful description of motor adaptation.…”

“…Our novel algorithm learns the time-varying motor commands to individual muscles that produce the same force and mechanical impedance observed when humans adapt to changes in environmental forces, including those arising from instability in the environment. It departs significantly from algorithms based on optimization (Burdet and Milner, 1998;Harris and Wolpert, 1998;Stroeve, 1999;Todorov, 2000;Todorov and Jordan, 2002;Guigon et al, 2007;Trainin et al, 2007;Izawa et al, 2008) as it predicts the transients of learning, as well as from existing supervised learning schemes (Kawato et al, 1987;Slotine and Li, 1991;Katayama and Kawato, 1993;Gribble and Ostry, 2000;Thoroughman and Shadmehr, 2000;Donchin et al, 2003;Emken et al, 2007) because they have no mechanism to counteract mechanical instability.…”