Title: Circuit Mechanisms of Sensorimotor Learning
Abstract: The relationship between the brain and the environment is flexible, forming the foundation for our ability to learn. Here we review the current state of our understanding of the modifications in the sensorimotor pathway related to sensorimotor learning. We divide the process into three hierarchical levels with distinct goals: (1) sensory perceptual learning, (2) sensorimotor associative learning, and (3) motor skill learning. Perceptual learning optimizes the representations of important sensory stimuli. Associative learning and the initial phase of motor skill learning are ensured by feedback-based mechanisms that permit trial-and-error learning. The later phase of motor skill learning may primarily involve feedback-independent mechanisms operating under the classic Hebbian rule. With these changes under distinct constraints and mechanisms, sensorimotor learning establishes dedicated circuitry for the reproduction of stereotyped neural activity patterns and behavior. The relationship between the brain and the environment is flexible, forming the foundation for our ability to learn. Here we review the current state of our understanding of the modifications in the sensorimotor pathway related to sensorimotor learning. We divide the process into three hierarchical levels with distinct goals: (1) sensory perceptual learning, (2) sensorimotor associative learning, and (3) motor skill learning. Perceptual learning optimizes the representations of important sensory stimuli. Associative learning and the initial phase of motor skill learning are ensured by feedback-based mechanisms that permit trial-and-error learning. The later phase of motor skill learning may primarily involve feedback-independent mechanisms operating under the classic Hebbian rule. With these changes under distinct constraints and mechanisms, sensorimotor learning establishes dedicated circuitry for the reproduction of stereotyped neural activity patterns and behavior. Many of our behaviors are modified through sensorimotor learning. Here we broadly define sensorimotor learning as an improvement in one’s ability to interact with the environment by interpreting the sensory world and responding to it with the motor system. Let’s take an example of braking the car while driving in traffic. To perfect this task, one needs to learn the skill to accurately estimate the flow of traffic (perceptual learning; novices tend to focus on the car in front of them, while experts can selectively use a more diverse set of cues). When one identifies the slowing of the traffic, the visual information initiates a motor program to brake the car (associative learning). They also improve the skill of manipulating the brake smoothly (motor skill learning; try braking with your left foot in an empty parking lot—you’ll be surprised.). As illustrated by this example, even a relatively simple behavior involves a multi-level learning process. Accordingly, this review discusses neural changes during sensorimotor learning in these three hierarchical levels. We note, however, that these levels are closely intertwined with each other and often occur simultaneously. Therefore, some mechanisms are likely shared across these levels. An unfortunate consequence of the broad scope of this review is that many studies or even systems that deserve attention had to be excluded. Despite this compromise, we hope that the broad scope helps us to underscore the distinct requirements of each step, which provide distinct constraints on the underlying neural mechanisms (Figure 1). Learning of sensorimotor behavior involves selective extraction and efficient processing of sensory information to generate an appropriate action. At the sensory processing stage, rich and multiplex information in the environment is transmitted to the sensory organs, where attributes of sensory stimuli are transduced to electrical signals, such as action potentials. As the transduced signal reaches the central nervous system, cognitive factors actively determine what is sampled and what is ignored in the environment. In this vein, the perceptual stage of sensorimotor learning is a process of establishing optimal representations of external stimuli that are deemed to be meaningful, a process known as perceptual learning. This process involves changes in response properties of individual and populations of neurons. In this section, we review recent attempts to understand dynamic changes in sensory representations during perceptual learning, and discuss how these changes are implemented through alterations in operation modes of the underlying circuit. Despite decades of research, there is still a controversy as to where in the brain neurons change their response properties with perceptual enhancement during sensorimotor learning and whether and how such changes are causally linked to behavioral improvement. Experiments in visual psychophysics demonstrated that the improved perceptual ability is restricted to the trained stimulus feature (e.g., orientation) as well as the location in visual space. These results are often interpreted as evidence for the involvement of early stages of cortical visual processing, where neurons are highly selective to physical attributes of visual stimuli, they have relatively small receptive fields, and the retinotopic organization is preserved. However, recent experiments using a newly developed double-training paradigm challenged this notion by demonstrating that the feature discrimination (e.g., contrast) ability can be transferred to a new retinal location if subjects were primed at the second location with a task-irrelevant feature (e.g., orientation) (Xiao et al., 2008Xiao L.Q. Zhang J.Y. Wang R. Klein S.A. Levi D.M. Yu C. Complete transfer of perceptual learning across retinal locations enabled by double training.Curr. Biol. 2008; 18: 1922-1926Abstract Full Text Full Text PDF PubMed Scopus (184) Google Scholar). This observation indicates that perceptual learning may also involve changes in non-retinotopic higher brain areas. Neurophysiological mechanisms underlying these observations in psychophysics have been under intense scrutiny. Theoretical studies have proposed that changes in the tuning curve of individual neurons, such as sharpening, gain modulations, or shift in the peak in early stages of sensory processing, could increase the neuron’s ability to discriminate similar stimuli (Teich and Qian, 2003Teich A.F. Qian N. Learning and adaptation in a recurrent model of V1 orientation selectivity.J. Neurophysiol. 2003; 89: 2086-2100Crossref PubMed Scopus (78) Google Scholar) (Figure 2A). These theories are supported by several experimental studies, where neurons in V1 and V4 increase their selectivity to task-relevant stimuli (Poort et al., 2015Poort J. Khan A.G. Pachitariu M. Nemri A. Orsolic I. 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These theories propose that perceptual learning involves appropriate routing and weighting of the most informative inputs from the sensory processing stage to the decision stage, while neural properties in early sensory areas are unaltered (Law and Gold, 2009Law C.T. Gold J.I. Reinforcement learning can account for associative and perceptual learning on a visual-decision task.Nat. Neurosci. 2009; 12: 655-663Crossref PubMed Scopus (126) Google Scholar, Petrov et al., 2005Petrov A.A. Dosher B.A. Lu Z.L. The dynamics of perceptual learning: an incremental reweighting model.Psychol. Rev. 2005; 112: 715-743Crossref PubMed Scopus (0) Google Scholar). Consistently, during motion discrimination training in monkeys, little change was observed in motion-evoked responses in the middle temporal area, a motion-sensitive sensory area, but responses to task-specific motion stimuli emerged and gradually increased in the lateral intraparietal area, a region known to be involved in decision making (Law and Gold, 2008Law C.T. Gold J.I. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area.Nat. Neurosci. 2008; 11: 505-513Crossref PubMed Scopus (236) Google Scholar). Furthermore, a more recent experiment showed minimal changes in stimulus discriminability of neural ensembles in mouse vibrissal primary somatosensory cortex (vS1) during learning of a whisker-mediated object-localization task, also supporting such late-stage models (Peron et al., 2015Peron S.P. Freeman J. Iyer V. Guo C. Svoboda K. A cellular resolution map of barrel cortex activity during tactile behavior.Neuron. 2015; 86: 783-799Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar). Single-neuron responses must be considered in the context of the underlying population activity structures. Recent simulation suggested that, at least within certain constraints, sharpening or amplification in the tuning of single neurons at early stages of sensory processing is neither necessary nor sufficient to improve population codes. For instance, sharpening of the tuning curve can be mediated by changes in intracortical connectivity, which can alter correlation statistics and lead to a large loss of information (Bejjanki et al., 2011Bejjanki V.R. Beck J.M. Lu Z.L. Pouget A. Perceptual learning as improved probabilistic inference in early sensory areas.Nat. Neurosci. 2011; 14: 642-648Crossref PubMed Scopus (0) Google Scholar, Seriès et al., 2004Seriès P. Latham P.E. Pouget A. 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Correlated neuronal discharges that increase coding efficiency during perceptual discrimination.Neuron. 2003; 38: 649-657Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar). Indeed, a recent study in songbirds found that after auditory discrimination learning, larger signal correlations in cortical neurons coincided with smaller noise correlations for task-relevant auditory stimuli, but not for task-irrelevant or novel stimuli (Jeanne et al., 2013Jeanne J.M. Sharpee T.O. Gentner T.Q. Associative learning enhances population coding by inverting interneuronal correlation patterns.Neuron. 2013; 78: 352-363Abstract Full Text Full Text PDF PubMed Scopus (45) Google Scholar). In contrast, two monkey studies demonstrated a reduction in noise correlations in neurons in the medial superior temporal area or V1 during perceptual learning (Gu et al., 2011Gu Y. Liu S. Fetsch C.R. Yang Y. Fok S. Sunkara A. DeAngelis G.C. Angelaki D.E. 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By monitoring odor representations by mitral cells in the mouse olfactory bulb, it was found that mitral cells became better at discriminating the odorants when mice were trained to discriminate between very similar odorants. However, when mice discriminated between very dissimilar odorants, counterintuitively, the representations of the two odorants gradually became more similar. This bidirectional effect was interpreted such that learning achieves an optimal separation of representations of familiar stimuli, balancing the robustness of discrimination and capacity of coding (Chu et al., 2016Chu M.W. Li W.L. Komiyama T. Balancing the robustness and efficiency of odor representations during learning.Neuron. 2016; 92: 174-186Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar). Metabolic efficiency might be another major design principle that sensory systems aim to achieve during sensorimotor learning. 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The changes in sensory representations during perceptual learning likely involve a variety of mechanisms, among which inhibitory circuits have garnered considerable attention in recent years. This was partially due to the development of genetic tools to identify and manipulate specific subtypes of inhibitory neurons. Inhibition is mediated by the neurotransmitter GABA, which shapes the activity of principal glutamatergic neurons in space and time. Inhibition contributes to gain modulations by altering the slope of the input-output function. It can also sharpen tuning curves of principal neurons by suppressing responses to non-preferred stimuli through an increase in spike threshold (“iceberg effect”). These two parameters, changes in gain and sharpening of the tuning curve, are two of the aforementioned potential mechanisms to increase the individual neuron’s ability to discriminate similar stimuli (Figure 2A). Consistent with these notions, activation of parvalbumin (PV or Pvalb)-expressing inhibitory neurons in the mouse visual cortex sharpens orientation tuning and improves behavioral discrimination of similarly oriented visual stimuli (Lee et al., 2012Lee S.H. Kwan A.C. Zhang S. Phoumthipphavong V. Flannery J.G. Masmanidis S.C. Taniguchi H. Huang Z.J. Zhang F. Boyden E.S. et al.Activation of specific interneurons improves V1 feature selectivity and visual perception.Nature. 2012; 488: 379-383Crossref PubMed Scopus (213) Google Scholar). Furthermore, in the mouse olfactory bulb, local GABAergic neurons contribute to pattern separation of similar odors in mitral/tufted cells and enhance discrimination performance of the animal (Gschwend et al., 2015Gschwend O. Abraham N.M. Lagier S. Begnaud F. Rodriguez I. Carleton A. Neuronal pattern separation in the olfactory bulb improves odor discrimination learning.Nat. Neurosci. 2015; 18: 1474-1482Crossref PubMed Scopus (26) Google Scholar). Together with the theoretical support, it is possible that these inhibitory neurons play an active role in enhancing the principal neurons’ discriminability of stimuli during perceptual learning. Longitudinal recording from genetically defined inhibitory neural populations over learning will be a useful approach to test this idea. GABAergic inhibitory neurons are highly heterogeneous in morphology, physiological properties, and gene expression. By regulating distinct subcellular compartments of principal neurons, different subtypes of inhibitory interneurons may function to regulate the flow of information (Chen et al., 2013Chen J.L. Carta S. Soldado-Magraner J. Schneider B.L. Helmchen F. Behaviour-dependent recruitment of long-range projection neurons in somatosensory cortex.Nature. 2013; 499: 336-340Crossref PubMed Scopus (74) Google Scholar, Kepecs and Fishell, 2014Kepecs A. Fishell G. 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The resulting disinhibition of the feedforward drive could enhance cortical representations of sensory information by increasing the gain of principal neurons (Figure 2B) (Letzkus et al., 2011Letzkus J.J. Wolff S.B. Meyer E.M. Tovote P. Courtin J. Herry C. Lüthi A. A disinhibitory microcircuit for associative fear learning in the auditory cortex.Nature. 2011; 480: 331-335Crossref PubMed Scopus (289) Google Scholar). In contrast, the increased influence of non-sensory information in mouse V1, likely carried by long-range feedback inputs, coincided with the reduced activity of SOM inhibitory interneurons (Figure 2B). Artificial reactivation of SOM interneurons partially reversed the learning-related change in principal neuron activity (Makino and Komiyama, 2015Makino H. Komiyama T. Learning enhances the relative impact of top-down processing in the visual cortex.Nat. Neurosci. 2015; 18: 1116-1122Crossref PubMed Scopus (25) Google Scholar). These results are consistent with the notion that SOM inhibitory interneurons act as a gate for long-range inputs and that this gate can be flexibly adjusted by learning. Unraveling how distinct types of inhibitory neurons interact with each other to modulate the firing pattern of individual principal neurons and their population correlation structures during learning is an important future direction. So far, we have discussed changes in sensory representations during perceptual learning within a local circuit. However, neurons receive convergent inputs from other brain areas, and inter-areal interactions likely play important roles in perceptual learning. For instance, it is now evident that sensory processing involves intricate interactions of concurrent streams of information flow, one from the environment in a bottom-up manner and the other from higher-order brain areas in a top-down manner (Figure 2B). Even neurons in early stages of sensory processing may therefore be subject to influences of contexts and cognitive factors, whic