Spikes alone do not behavior make: why neuroscience needs biomechanics

Abstract

Neural circuits do not function in isolation; they interact with the physical world, accepting sensory inputs and producing outputs via muscles. Since both these pathways are constrained by physics, the activity of neural circuits can only be understood by considering biomechanics of muscles, bodies, and the exterior world. We discuss how animal bodies have natural stable motions that require relatively little activation or control from the nervous system. The nervous system can substantially alter these motions, by subtly changing mechanical properties such as body or leg stiffness. Mechanics can also provide robustness to perturbations without sensory reflexes. By considering a complete neuromechanical system, neuroscientists and biomechanicians together can provide a more integrated view of neural circuitry and behavior.

Figure 1

Schematic of the crucial rôle that biomechanics plays in understanding both behavior and neuroscience. The images depict fish locomotion as an example, but the relationships are true for any circuit with a motor output. In this example, neural circuits activate muscles that produce force to move the body, which then interacts with the environment. The environment produces fluid dynamic forces back on the body, and the muscle force depends on the body motion according to the nonlinear force-length and force-velocity properties of muscle. Finally, the output of the neural circuit is influenced by sensory inputs such as proprioception. The movement of the body (“behavior”) depends in an intricate way on biomechanical interactions (gray box).

Figure 2

Neural activity and body curvature in a neuromechanical model of a lamprey. The body is shown in gray, with thick black lines to indicate regions where motor neurons are active, and black points to indicate the location of zero curvature. The phase lag between muscle activity and curvature is indicated by a blue arrow. Simulations shown in panel A and B have identical neural activation patterns, but differ in muscle strength and body stiffness (A, relatively strong muscles and stiff body; B, relatively weak muscles and less stiff body). Modified from [25].

Figure 3

Instantiation of the system of Fig. 1 in a model of insect locomotion. A Mechanical model. Extensor and flexor muscles actuate simplified “hip-knee” geometry modeling coxa-femur and femur-tibia joints. B Six hemisegments constitute a CPG oscillator network that drives motor neurons (MNs) in a feedforward manner. Joint torques monitored by campaniform sensilla modulate relative phases of MN bursts (via S+ and S- neurons), but primary environmental feedback comes from mechanical reaction forces and stretch and stretch-rate force dependence in muscles. Filled circles and open arcs respectively denote excitatory and inhibitory connections. C Forces produced by muscle depend on length (panel 1) and shortening velocity (panel 2). Data from Ahn and Full [53] shown in black; fits shown with red dashed lines. D Response of the model as diagrammed in panels A-C to a rapid lateral perturbation. 1 Time course of perturbation force. 2 Lateral velocity after the perturbation. Solid black line shows the unperturbed model. Dashed blue line shows the model with no sensory feedback, while solid orange, red, and brown lines show differing sensory feedback gains. For comparison, experimental data from [52] is overlaid with a thick green line. 3 Trajectory of the model's mass center in the horizontal plane. Feedback reduces heading change after the perturbation. Modified from [54, 55].