Independent circuits in the basal ganglia for the evaluation and selection of actions

The basal ganglia are critical for selecting actions and evaluating their outcome. Although the circuitry for selection is well understood, how these nuclei evaluate the outcome of actions is unknown. Here, we show in lamprey that a separate evaluation circuit, which regulates the habenula-projecting globus pallidus (GPh) neurons, exists within the basal ganglia. The GPh neurons are glutamatergic and can drive the activity of the lateral habenula, which, in turn, provides an indirect inhibitory influence on midbrain dopamine neurons. We show that GPh neurons receive inhibitory input from the striosomal compartment of the striatum. The striosomal input can reduce the excitatory drive to the lateral habenula and, consequently, decrease the inhibition onto the dopaminergic system. Dopaminergic neurons, in turn, provide feedback that inhibits the GPh. In addition, GPh neurons receive direct projections from the pallium (cortex in mammals), which can increase the GPh activity to drive the lateral habenula to increase the inhibition of the neuromodulatory systems. This circuitry, thus, differs markedly from the "direct" and "indirect" pathways that regulate the pallidal (e.g., globus pallidus) output nuclei involved in the control of motion. Our results show that a distinct reward-evaluation circuit exists within the basal ganglia, in parallel to the direct and indirect pathways, which select actions. Our results suggest that these circuits are part of the fundamental blueprint that all vertebrates use to select actions and evaluate their outcome

Changes in spinal reflex and locomotor activity after a complete spinal cord injury: a common mechanism?

Locomotor activity and spinal reflexes (SRs) show common features in different mammals, including humans. Here we report the time-course of the development of locomotor activity and SRs after a complete spinal cord injury in humans. SRs evoked by tibial nerve stimulation were studied, as was the leg muscle electromyography activity evoked by mechanically assisted locomotion (Lokomat) in biceps femoris, rectus femoris, tibialis anterior and gastrocenmius medialis. Around 8 weeks after the injury, an early SR component (latency 60-120 ms) appeared, as in healthy subjects, and a well-organized leg muscle activity was present during assisted locomotion. At around 6 months after injury an additional, late reflex component (latency 120-450 ms) appeared, which remained even 15 years after the spinal cord injury. In contrast, the early component had markedly decreased at 18 months after injury. These changes in SR were associated with a loss of electromyography activity and a successively stronger electromyography exhaustion (i.e. decline of electromyography amplitude), when comparing the level of electromyography activity at 2 and 10 min, respectively, during assisted locomotion. These changes in electromyography activity affected mainly the biceps femoris, gastrocenmius medialis and tibialis anterior but less so the rectus femoris. When the amplitude relationship of the early to late SR component was calculated, there was a temporal relationship between the decrease of the early component and an increase of the late component and the degree of exhaustion of locomotor activity. In chronic, severely affected but sensori-motor incomplete spinal cord injury subjects a late SR component, associated with an electromyography exhaustion, was present in subjects who did not regularly perform stepping movements. Our data are consistent with the proposal of a common mechanism underlying the changes in SR activity and locomotor activity after spinal cord injury. These findings should be taken into consideration in the development of novel rehabilitation schemes and programs to facilitate regeneration-inducing therapies in spinal cord injury subject

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Can small scale search behaviours enhance large-scale navigation?

We develop a spiking neural network model of an insect-inspired CPG which is used to underpin steering behaviour for a Braitenberg-like vehicle. We show that small scale search behaviour, produced by the CPG, improves navigation by recovering useful sensory signals

Locomotor adaptability in persons with unilateral transtibial amputation

Background Locomotor adaptation enables walkers to modify strategies when faced with challenging walking conditions. While a variety of neurological injuries can impair locomotor adaptability, the effect of a lower extremity amputation on adaptability is poorly understood. Objective Determine if locomotor adaptability is impaired in persons with unilateral transtibial amputation (TTA). Methods The locomotor adaptability of 10 persons with a TTA and 8 persons without an amputation was tested while walking on a split-belt treadmill with the parallel belts running at the same (tied) or different (split) speeds. In the split condition, participants walked for 15 minutes with the respective belts moving at 0.5 m/s and 1.5 m/s. Temporal spatial symmetry measures were used to evaluate reactive accommodations to the perturbation, and the adaptive/de-adaptive response. Results Persons with TTA and the reference group of persons without amputation both demonstrated highly symmetric walking at baseline. During the split adaptation and tied post-adaptation walking both groups responded with the expected reactive accommodations. Likewise, adaptive and de-adaptive responses were observed. The magnitude and rate of change in the adaptive and de-adaptive responses were similar for persons with TTA and those without an amputation. Furthermore, adaptability was no different based on belt assignment for the prosthetic limb during split adaptation walking. Conclusions Reactive changes and locomotor adaptation in response to a challenging and novel walking condition were similar in persons with TTA to those without an amputation. Results suggest persons with TTA have the capacity to modify locomotor strategies to meet the demands of most walking conditions despite challenges imposed by an amputation and use of a prosthetic limb

Genetic inactivation of the vesicular glutamate transporter 2 (VGLUT2) in the mouse: What have we learnt about functional glutamatergic neurotransmission?

During the past decade, three proteins that possess the capability of packaging glutamate into presynaptic vesicles have been identified and characterized. These three vesicular glutamate transporters, VGLUT1–3, are encoded by solute carrier genes Slc17a6–8. VGLUT1 (Slc17a7) and VGLUT2 (Slc17a6) are expressed in glutamatergic neurons, while VGLUT3 (Slc17a8) is expressed in neurons classically defined by their use of another transmitter, such as acetylcholine and serotonin. As glutamate is both a ubiquitous amino acid and the most abundant neurotransmitter in the adult central nervous system, the discovery of the VGLUTs made it possible for the first time to identify and specifically target glutamatergic neurons. By molecular cloning techniques, different VGLUT isoforms have been genetically targeted in mice, creating models with alterations in their glutamatergic signalling. Glutamate signalling is essential for life, and its excitatory function is involved in almost every neuronal circuit. The importance of glutamatergic signalling was very obvious when studying full knockout models of both VGLUT1 and VGLUT2, none of which were compatible with normal life. While VGLUT1 full knockout mice die after weaning, VGLUT2 full knockout mice die immediately after birth. Many neurological diseases have been associated with altered glutamatergic signalling in different brain regions, which is why conditional knockout mice with abolished VGLUT-mediated signalling only in specific circuits may prove helpful in understanding molecular mechanisms behind such pathologies. We review the recent studies in which mouse genetics have been used to characterize the functional role of VGLUT2 in the central nervous system

Cerebral activations related to ballistic, stepwise interrupted and gradually modulated movements in parkinson patients

Patients with Parkinson's disease (PD) experience impaired initiation and inhibition of movements such as difficulty to start/stop walking. At single-joint level this is accompanied by reduced inhibition of antagonist muscle activity. While normal basal ganglia (BG) contributions to motor control include selecting appropriate muscles by inhibiting others, it is unclear how PD-related changes in BG function cause impaired movement initiation and inhibition at single-joint level. To further elucidate these changes we studied 4 right-hand movement tasks with fMRI, by dissociating activations related to abrupt movement initiation, inhibition and gradual movement modulation. Initiation and inhibition were inferred from ballistic and stepwise interrupted movement, respectively, while smooth wrist circumduction enabled the assessment of gradually modulated movement. Task-related activations were compared between PD patients (N = 12) and healthy subjects (N = 18). In healthy subjects, movement initiation was characterized by antero-ventral striatum, substantia nigra (SN) and premotor activations while inhibition was dominated by subthalamic nucleus (STN) and pallidal activations, in line with the known role of these areas in simple movement. Gradual movement mainly involved antero-dorsal putamen and pallidum. Compared to healthy subjects, patients showed reduced striatal/SN and increased pallidal activation for initiation, whereas for inhibition STN activation was reduced and striatal-thalamo-cortical activation increased. For gradual movement patients showed reduced pallidal and increased thalamo-cortical activation. We conclude that PD-related changes during movement initiation fit the (rather static) model of alterations in direct and indirect BG pathways. Reduced STN activation and regional cortical increased activation in PD during inhibition and gradual movement modulation are better explained by a dynamic model that also takes into account enhanced responsiveness to external stimuli in this disease and the effects of hyper-fluctuating cortical inputs to the striatum and STN in particular

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior (e.g., stable locomotion from a system of coupled oscillators under perceptual guidance). Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system