Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland ; Neuroscience Center Zurich (ZNZ) Zurich, Switzerland.
Front Neural Circuits. 2013 Jun 19;7:106. doi: 10.3389/fncir.2013.00106. eCollection 2013.
Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.
镜像神经元是指对观察到的运动行为的反应与该行为产生时的测量反应相似的神经元。从计算的角度来看,镜像神经元被视为内部逆模型存在的证据。这种基于控制理论的模型将期望的感觉目标映射到产生这些目标所需的运动命令上。为了共同探索镜像反应的形成及其对逆模型的功能贡献,我们开发了一种基于相关性的感觉和运动区域之间相互作用的理论。我们表明,在运动探索过程中传感器-运动回路内运行的简单资格加权赫布学习规则,通过异突触竞争稳定下来,自然会产生镜像神经元,并控制从感觉神经元到运动神经元的突触权重中编码的控制理论逆模型。至关重要的是,我们发现运动探索中神经码的相关性结构或刻板性决定了所学习逆模型的性质:随机运动码导致因果反转,将感觉活动模式映射到其运动原因;这种反转非常有用,可以模仿任意的感觉目标序列。相比之下,刻板的运动码会导致不那么有用的预测反转,将感觉活动映射到未来的运动动作上。我们的理论通过表明这种模型可以在简单的赫布学习框架中学习,而不需要误差信号或反向传播,从而对逆模型进行了概括,并在逆模型的因果性质、运动变异性的统计结构以及镜像神经元的感觉和运动反应之间的时间延迟之间建立了新的概念联系。将其应用于鸟类歌唱学习,我们的理论可以解释歌唱系统中令人困惑的方面,包括传感器运动门控的必要性和听觉对鸟类自身歌声(BOS)刺激的选择性。