Neocortical mechanisms in motor learning

The ability to learn novel motor skills has fundamental importance for adaptive behavior. Neocortical mechanisms support human motor skill learning, from simple practice to adaptation and arbitrary sensory-motor associations. Behavioral and neural manifestations of motor learning evolve in time and involve multiple structures across the neocortex. Modifications of neural properties, synchrony and synaptic efficacy are all related to the development and maintenance of motor skill.     

The mechanisms of motor learning]

In the paper the general principles of the motor learning are analyzed. A thesis is argued that instrumental conditioning is the basis of the motor learning, but the mechanisms of insight and the apparatus of anticipation of the action result also play an important role. At comparison of some theories of learning a hypothesis is suggested about the possibility of synthetic approach. Two stages can be distinguished in the process of the motor learning: formation of semantic and coordination programs. Formation and realization of learned movements opposite to the initial coordinations are constantly controlled by the motor cortex, which realizes the learned inhibition of the interfering coordinations.     

Central mechanisms of motor skill learning

Recent studies have shown that frontoparietal cortices and interconnecting regions in the basal ganglia and the cerebellum are related to motor skill learning. We propose that motor skill learning occurs independently and in different coordinates in two sets of loop circuits: cortex-basal ganglia and cortex-cerebellum. This architecture accounts for the seemingly diverse features of motor learning.     

Neural mechanisms of social learning and decision-making

One of the hallmarks of human society is the ubiquitous interactions among individuals. Indeed, a significant portion of human daily routine decision making is socially related. Normative economic theory, namely game theory, has prescribed the canonical decision strategy when "rational" social agents have full information about the decision environment. In reality, however, social decision is often influenced by the trait and state parameters of selves and others. Therefore, understanding the cognitive and neural processes of inferring the decision parameters is pivotal for social decision making. Recently, both correlational and causal non-invasive neuroimaging studies have started to reveal the critical neural computations underlying social learning and decision-making, and highlighted the unique roles of "social" brain structures such as temporal-parietal junction(TPJ) and dorsomedial prefrontal cortex(dmPFC). Here we review recent advances in social decision neuroscience and maintain the focus on how the inference about others is dynamically acquired during social learning, as well as how the prosocial(altruistic)behavior results from orchestrated interactions of different brain regions specified under the social utility framework. We conclude by emphasizing the importance of combining computational decision theory with the identification of neural mechanisms that represent, evaluate and integrate value related social information and generate decision variables guiding behavioral output in the complex social environment.     

Neural mechanisms for learning hierarchical structures of information

Spatial and temporal information from the environment is often hierarchically organized, so is our knowledge formed about the environment. Identifying the meaningful segments embedded in hierarchically structured information is crucial for cognitive functions, including visual, auditory, motor, memory, and language processing. Segmentation enables the grasping of the links between isolated entities, offering the basis for reasoning and thinking. Importantly, the brain learns such segmentation without external instructions. Here, we review the underlying computational mechanisms implemented at the single-cell and network levels. The network-level mechanism has an interesting similarity to machine-learning methods for graph segmentation. The brain possibly implements methods for the analysis of the hierarchical structures of the environment at multiple levels of its processing hierarchy.     

Neural mechanisms underlying uninstructed orofacial movements during reward-based learning behaviors

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Temporal difference models and reward-related learning in the human brain

Temporal difference learning has been proposed as a model for Pavlovian conditioning, in which an animal learns to predict delivery of reward following presentation of a conditioned stimulus (CS). A key component of this model is a prediction error signal, which, before learning, responds at the time of presentation of reward but, after learning, shifts its response to the time of onset of the CS. In order to test for regions manifesting this signal profile, subjects were scanned using event-related fMRI while undergoing appetitive conditioning with a pleasant taste reward. Regression analyses revealed that responses in ventral striatum and orbitofrontal cortex were significantly correlated with this error signal, suggesting that, during appetitive conditioning, computations described by temporal difference learning are expressed in the human brain.     

Neural mechanisms and computations underlying stress effects on learning and memory

Stress has complex effects on memory function that can vary depending on the type of information that is learned and in relation to inter-individual characteristics. Recent work has also shown that stress can switch performance between memory systems, biasing it toward habit in detriment of spatial or goal-directed strategies. In addition, novel synaptic mechanisms have been implicated in the effects of stress in plasticity and memory. Computational modeling is emerging as a useful approach to integrate and to ascertain neural and cognitive computations underlying different effects of stress in memory. Having provided novel explanations for the inverted-U-shaped relationship between stress and cognitive performance, model-based analysis studies can improve our understanding of diverse effects of stress in cognition and psychopathology.     

Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data

The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can efficiently solve difficult segmentation tasks. In this model, multiplicative recurrent connections learn a context-dependent gating of dendro-somatic information transfers to minimize error in the prediction of somatic responses by the dendrites. Consequently, these connections filter the redundant input features represented by the dendrites but unnecessary in the given context. The model was tested on both synthetic and real neural data. In particular, the model was successful for segmenting multiple cell assemblies repeating in large-scale calcium imaging data containing thousands of cortical neurons. Our results suggest that recurrent gating of dendro-somatic signal transfers is crucial for cortical learning of context-dependent segmentation tasks.     

Neural and psychological mechanisms underlying appetitive learning: links to drug addiction

The complexity of drug addiction mirrors the complexity of the psychological processes that motivate animals to work for any reinforcer, be it a natural reward or a drug. Here, we review the role of the nucleus accumbens, together with its dopaminergic and cortical innervation, in responding to reinforcement. One important contribution made by the nucleus accumbens is to the process through which neutral stimuli, once paired with a reinforcer such as a drug, have the capacity to motivate behaviour. This process may be one of several contributing to addiction, and it may be amenable to pharmacological intervention.     

Neural mechanisms of learning: an analysis of imprinting in the domestic chick

Learning is a complex set of processes involving the acquisition and storage of information. Imprinting in the domestic chick was studied to analyse the neural basis of storage. The recently hatched chick learns the characteristics of a visually conspicuous object by being exposed to it. When a chick is trained in this way, biochemical changes can be detected in the dorsal part of the forebrain. Through a series of experiments it was shown that these changes are unlikely to be non-specific consequences of training, but more probably reflect some aspect of the storage process. By using a radioautographic technique to localize the brain region more precisely, part of the hyperstriatum ventrale was implicated in this process. Bilateral destruction of the region before imprinting prevented acquisition, and bilateral destruction after imprinting impaired retention. After exposure for 140 min to an imprinting stimulus there was an increase in the area of contact between presynaptic and postsynaptic elements in the region. This effect was found on the left side only. Sequential lesions to left and right sides confirmed that there is a hemispheric asymmetry in the role of the region in the storage of information. The area receives input from the visual pathways and possibly from other sensory pathways, and projects to regions that are thought to be involved in the control of locomotor and viscero-endocrine functions. The results afford an opportunity for the further analysis both of storage and of the whole set of neural processes that underlie imprinting in the domestic chick.U     

Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process

Background

Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed.

Methodology/Principal Findings

We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d'') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30–60 Hz) and alpha (8–14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.

Conclusions/Significance

We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.     

Reward representations and reward-related learning in the human brain: insights from neuroimaging

This review outlines recent findings from human neuroimaging concerning the role of a highly interconnected network of brain areas including orbital and medial prefrontal cortex, amygdala, striatum and dopaminergic mid-brain in reward processing. Distinct reward-related functions can be attributed to different components of this network. Orbitofrontal cortex is involved in coding stimulus reward value and in concert with the amygdala and ventral striatum is implicated in representing predicted future reward. Such representations can be used to guide action selection for reward, a process that depends, at least in part, on orbital and medial prefrontal cortex as well as dorsal striatum.     

Neural mechanisms of imitation

Recent advances in our knowledge of the neural mechanisms of imitation suggest that there is a core circuitry of imitation comprising the superior temporal sulcus and the 'mirror neuron system', which consists of the posterior inferior frontal gyrus and adjacent ventral premotor cortex, as well as the rostral inferior parietal lobule. This core circuitry communicates with other neural systems according to the type of imitation performed. Imitative learning is supported by interaction of the core circuitry of imitation with the dorsolateral prefrontal cortex and perhaps motor preparation areas--namely, the mesial frontal, dorsal premotor and superior parietal areas. By contrast, imitation as a form of social mirroring is supported by interaction of the core circuitry of imitation with the limbic system.     

Neural mechanisms of aggression

Unchecked aggression and violence exact a significant toll on human societies. Aggression is an umbrella term for behaviours that are intended to inflict harm. These behaviours evolved as adaptations to deal with competition, but when expressed out of context, they can have destructive consequences. Uncontrolled aggression has several components, such as impaired recognition of social cues and enhanced impulsivity. Molecular approaches to the study of aggression have revealed biological signals that mediate the components of aggressive behaviour. These signals may provide targets for therapeutic intervention for individuals with extreme aggressive outbursts. This Review summarizes the complex interactions between genes, biological signals, neural circuits and the environment that influence the development and expression of aggressive behaviour.     

Neural mechanisms of emesis

Emesis is a reflex, developed to different degrees in different species, that allows an animal to rid itself of ingested toxins or poisons. The reflex can be elicited either by direct neuronal connections from visceral afferent fibers, especially those from the gastrointestinal tract, or from humoral factors. Emesis from humoral factors depends on the integrity of the area postrema; neurons in the area postrema have excitatory receptors for emetic agents. Emesis from gastrointestinal afferents does not depend on the area postrema, but probably the reflex is triggered by projections to some part of the nucleus tractus solitarius. As with a variety of other complex motor functions regulated by the brain stem, it is likely that the sequence of muscle excitation and inhibition is controlled by a central pattern generator located in the nucleus tractus solitarius, and that information from humoral factors via the area postrema and visceral afferents via the vagus nerve converge at this point. This central pattern generator, like those for motor functions such as swallowing, presumably projects to the various motor nuclei, perhaps through interneuronal pathways, to elicit the sequential excitation and inhibition that controls the reflex.     

Stages of motor skill learning

Successful learning of a motor skill requires repetitive training. Once the skill is mastered, it can be remembered for a long period of time. The durable memory makes motor skill learning an interesting paradigm for the study of learning and memory mechanisms. To gain better understanding, one scientific approach is to dissect the process into stages and to study these as well as their interactions. This article covers the growing evidence that motor skill learning advances through stages, in which different storage mechanisms predominate. The acquisition phase is characterized by fast (within session) and slow learning (between sessions). For a short period following the initial training sessions, the skill is labile to interference by other skills and by protein synthesis inhibition, indicating that consolidation processes occur during rest periods between training sessions. During training as well as rest periods, activation in different brain regions changes dynamically. Evidence for stages in motor skill learning is provided by experiments using behavioral, electrophysiological, functional imaging, and cellular/molecular methods.     

Neural mechanisms of object recognition

Single-unit recordings from behaving monkeys and human functional magnetic resonance imaging studies have continued to provide a host of experimental data on the properties and mechanisms of object recognition in cortex. Recent advances in object recognition, spanning issues regarding invariance, selectivity, representation and levels of recognition have allowed us to propose a putative model of object recognition in cortex.