Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.
Many human behaviours and pathologies have been attributed to the putative mirror neuron system, a neural system that is active during both the observation and execution of actions. While there are now a very large number of papers on the mirror neuron system, variations in the methods and analyses employed by researchers mean that the basic characteristics of the mirror response are not clear. This review focuses on three important aspects of the mirror response, as measured by modulations in corticospinal excitability: (1) muscle specificity; (2) direction; and (3) timing of modulation. We focus mainly on electromyographic (EMG) data gathered following single-pulse transcranial magnetic stimulation (TMS), because this method provides precise information regarding these three aspects of the response. Data from paired-pulse TMS paradigms and peripheral nerve stimulation (PNS) are also considered when we discuss the possible mechanisms underlying the mirror response. In this systematic review of the literature, we examine the findings of 85 TMS and PNS studies of the human mirror response, and consider the limitations and advantages of the different methodological approaches these have adopted in relation to discrepancies between their findings. We conclude by proposing a testable model of how action observation modulates corticospinal excitability in humans. Specifically, we propose that action observation elicits an early, non-specific facilitation of corticospinal excitability (at around 90ms from action onset), followed by a later modulation of activity specific to the muscles involved in the observed action (from around 200ms). Testing this model will greatly advance our understanding of the mirror mechanism and provide a more stable grounding on which to base inferences about its role in human behaviour.
We report a case of a healthy, right-hand dominant young male who was a volunteer for a pilot run of a functional MRI (fMRI) study. The fMRI was performed with a 3.0 Tesla MRI scanner using a finger tapping task-based activity. The subjects were instructed to perform flexion of the right thumb and left thumb consecutively (activation task) and neuronal activation in bilateral primary motor cortex (PMC) were observed during each task. One particular subject demonstrated bilateral PMC activation during the left-thumb movement task, instead of the expected activation of the contralateral PMC alone.
Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings.