Neuromodulation of Spatial Associations: Evidence from Choice Reaction Tasks During Transcranial Direct Current Stimulation

Various portions of human behavior and cognition are influenced by covert implicit processes without being necessarily available to intentional planning. Implicit cognitive biases can be measured in behavioral tasks yielding SNARC effects for spatial associations of numerical and non-numerical sequences, or yielding the implicit association test effect for associations between insect-flower and negative-positive categories. By using concurrent neuromodulation with transcranial direct current stimulation (tDCS), subthreshold activity patterns in prefrontal cortical regions can be experimentally manipulated to reduce implicit processing. Thus, the application of tDCS can test neurocognitive hypotheses on a unique neurocognitive origin of implicit cognitive biases in different spatial-numerical and non-numerical domains. However, the effects of tDCS are not only determined by superimposed electric fields, but also by task characteristics. To outline the possibilities of task-specific targeting of tDCS, task characteristics and instructions can be varied systematically when combined with neuromodulation. In the present thesis, implicit cognitive processes are assessed in different paradigms concurrent to left-hemispheric prefrontal tDCS to investigate a verbal processing hypothesis for implicit associations in general. In psychological experiments, simple choice reaction tasks measure implicit SNARC and SNARC-like effects as relative left-hand vs. right-hand latency advantages for responding to smaller number or ordinal sequence targets. However, different combinations of polarity-dependent tDCS with stimuli and task procedures also reveal domain-specific involvements and dissociations. Discounting previous unified theories on the SNARC effect, polarity-specific neuromodulation effects dissociate numbers and weekday or month ordinal sequences. By considering also previous results and patient studies, I present a hybrid and augmented working memory account and elaborate the linguistic markedness correspondence principle as one critical verbal mechanism among competing covert coding mechanisms. Finally, a general stimulation rationale based on verbal working memory is tested in separate experiments extending also to non-spatial implicit association test effects. Regarding cognitive tDCS effects, the present studies show polarity asymmetry and task-induced activity dependence of state-dependent neuromodulation. At large, distinct combinations of the identical tDCS electrode configuration with different tasks influences behavioral outcomes tremendously, which will allow for improved task- and domain-specific targeting

Modern Developments in Transcranial Magnetic Stimulation (TMS) – Applications and Perspectives in Clinical Neuroscience

Transcranial magnetic stimulation (TMS) is being increasingly used in neuroscience and clinics. Modern advances include but are not limited to the combination of TMS with precise neuronavigation as well as the integration of TMS into a multimodal environment, e.g., by guiding the TMS application using complementary techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), or magnetoencephalography (MEG). Furthermore, the impact of stimulation can be identified and characterized by such multimodal approaches, helping to shed light on the basic neurophysiology and TMS effects in the human brain. Against this background, the aim of this Special Issue was to explore advancements in the field of TMS considering both investigations in healthy subjects as well as patients

Adaptive networks for robotics and the emergence of reward anticipatory circuits

Currently the central challenge facing evolutionary robotics is to determine how best to extend the range and complexity of behaviour supported by evolved neural systems. Implicit in the work described in this thesis is the idea that this might best be achieved through devising neural circuits (tractable to evolutionary exploration) that exhibit complementary functional characteristics. We concentrate on two problem domains; locomotion and sequence learning. For locomotion we compare the use of GasNets and other adaptive networks. For sequence learning we introduce a novel connectionist model inspired by the role of dopamine in the basal ganglia (commonly interpreted as a form of reinforcement learning). This connectionist approach relies upon a new neuron model inspired by notions of energy efficient signalling. Two reward adaptive circuit variants were investigated. These were applied respectively to two learning problems; where action sequences are required to take place in a strict order, and secondly, where action sequences are robust to intermediate arbitrary states. We conclude the thesis by proposing a formal model of functional integration, encompassing locomotion and sequence learning, extending ideas proposed by W. Ross Ashby. A general model of the adaptive replicator is presented, incoporating subsystems that are tuned to continuous variation and discrete or conditional events. Comparisons are made with Ross W. Ashby's model of ultrastability and his ideas on adaptive behaviour. This model is intended to support our assertion that, GasNets (and similar networks) and reward adaptive circuits of the type presented here, are intrinsically complementary. In conclusion we present some ideas on how the co-evolution of GasNet and reward adaptive circuits might lead us to significant improvements in the synthesis of agents capable of exhibiting complex adaptive behaviour

A Neurocomputational Model of the Functional Role of Dopamine in Stimulus-Response Task Learning and Performance

Thesis (Ph.D.) - Indiana University, Psychology, 2009The neuromodulatory neurotransmitter dopamine (DA) plays a complex, but central role in the learning and performance of stimulus-response (S-R) behaviors. Studies have implicated DA's role in reward-driven learning and also its role in setting the overall level of vigor or frequency of response. Here, a neurocomputational model is developed which models DA's influence on a set of brain regions believed to be involved in the learning and execution of S-R tasks, including frontal cortex, basal ganglia, and cingulate cortex. An `actor' component of the model is trained, using `babble' (random behavior selection) and `critic' (rewarding and punishing) components of the model, to perform acceptance/rejection responses upon presentation of color stimuli in the context of recently presented auditory tones. The model behaves like an autonomous organism learning (and relearning) through `trial-and-error'. The focus of the study, the impact of hypo- and hyper-normal DA activity on this model, is investigated by three different dopaminergic pathways--two striatal and one prefrontal cortical--being manipulated independently during the learning and performance of the color response task. Hypo-DA conditions, analogous to Parkinsonism, cause slowing and reduction of frequency of learned responses, and, at extremes, degrade the learning (either initial or reversal) of the task. Hyper-DA conditions, analogous to psychostimulant effects, cause more rapid response times, but also can lead to perseveration of incorrect learning of response on the task. The presence of these effects often depends on which DA-ergic pathway is manipulated, however, which has implications for interpretation of the pharmacological experimental data. The proposed model embodies an integrative theory of dopamine function which suggests that the base rate of DA cell activity encodes the overall `activity-oriented motivation' of the organism, with hunger and/or expectation of reward driving both response vigor and tendency to generate an explorative `babble' response. This more `tonic' feature of DA functionality coexists naturally with the more extensively-studied `phasic' reward-learning features. The model may provide better insights on the role of DA system dysfunction in the cognitive and motivational symptoms of disorders such as Parkinsonism, psychostimulant abuse, ADHD, OCD, and schizophrenia, accounting for deficits in both learning and performance of tasks

The computational neurology of active vision

In this thesis, we appeal to recent developments in theoretical neurobiology – namely, active inference – to understand the active visual system and its disorders. Chapter 1 reviews the neurobiology of active vision. This introduces some of the key conceptual themes around attention and inference that recur through subsequent chapters. Chapter 2 provides a technical overview of active inference, and its interpretation in terms of message passing between populations of neurons. Chapter 3 applies the material in Chapter 2 to provide a computational characterisation of the oculomotor system. This deals with two key challenges in active vision: deciding where to look, and working out how to look there. The homology between this message passing and the brain networks solving these inference problems provide a basis for in silico lesion experiments, and an account of the aberrant neural computations that give rise to clinical oculomotor signs (including internuclear ophthalmoplegia). Chapter 4 picks up on the role of uncertainty resolution in deciding where to look, and examines the role of beliefs about the quality (or precision) of data in perceptual inference. We illustrate how abnormal prior beliefs influence inferences about uncertainty and give rise to neuromodulatory changes and visual hallucinatory phenomena (of the sort associated with synucleinopathies). We then demonstrate how synthetic pharmacological perturbations that alter these neuromodulatory systems give rise to the oculomotor changes associated with drugs acting upon these systems. Chapter 5 develops a model of visual neglect, using an oculomotor version of a line cancellation task. We then test a prediction of this model using magnetoencephalography and dynamic causal modelling. Chapter 6 concludes by situating the work in this thesis in the context of computational neurology. This illustrates how the variational principles used here to characterise the active visual system may be generalised to other sensorimotor systems and their disorders

Understanding motor control in humans to improve rehabilitation robots

Recent reviews highlighted the limited results of robotic rehabilitation and the low quality of evidences in this field. Despite the worldwide presence of several robotic infrastructures, there is still a lack of knowledge about the capabilities of robotic training effect on the neural control of movement. To fill this gap, a step back to motor neuroscience is needed: the understanding how the brain works in the generation of movements, how it adapts to changes and how it acquires new motor skills is fundamental. This is the rationale behind my PhD project and the contents of this thesis: all the studies included in fact examined changes in motor control due to different destabilizing conditions, ranging from external perturbations, to self-generated disturbances, to pathological conditions. Data on healthy and impaired adults have been collected and quantitative and objective information about kinematics, dynamics, performance and learning were obtained for the investigation of motor control and skill learning. Results on subjects with cervical dystonia show how important assessment is: possibly adequate treatments are missing because the physiological and pathological mechanisms underlying sensorimotor control are not routinely addressed in clinical practice. These results showed how sensory function is crucial for motor control. The relevance of proprioception in motor control and learning is evident also in a second study. This study, performed on healthy subjects, showed that stiffness control is associated with worse robustness to external perturbations and worse learning, which can be attributed to the lower sensitiveness while moving or co-activating. On the other hand, we found that the combination of higher reliance on proprioception with \u201cdisturbance training\u201d is able to lead to a better learning and better robustness. This is in line with recent findings showing that variability may facilitate learning and thus can be exploited for sensorimotor recovery. Based on these results, in a third study, we asked participants to use the more robust and efficient strategy in order to investigate the control policies used to reject disturbances. We found that control is non-linear and we associated this non-linearity with intermittent control. As the name says, intermittent control is characterized by open loop intervals, in which movements are not actively controlled. We exploited the intermittent control paradigm for other two modeling studies. In these studies we have shown how robust is this model, evaluating it in two complex situations, the coordination of two joints for postural balance and the coordination of two different balancing tasks. It is an intriguing issue, to be addressed in future studies, to consider how learning affects intermittency and how this can be exploited to enhance learning or recovery. The approach, that can exploit the results of this thesis, is the computational neurorehabilitation, which mathematically models the mechanisms underlying the rehabilitation process, with the aim of optimizing the individual treatment of patients. Integrating models of sensorimotor control during robotic neurorehabilitation, might lead to robots that are fully adaptable to the level of impairment of the patient and able to change their behavior accordingly to the patient\u2019s intention. This is one of the goals for the development of rehabilitation robotics and in particular of Wristbot, our robot for wrist rehabilitation: combining proper assessment and training protocols, based on motor control paradigms, will maximize robotic rehabilitation effects

A robot model of the basal ganglia: Behavior and intrinsic processing

The existence of multiple parallel loops connecting sensorimotor systems to the basal ganglia has given rise to proposals that these nuclei serve as a selection mechanism resolving competitions between the alternative actions available in a given context. A strong test of this hypothesis is to require a computational model of the basal ganglia to generate integrated selection sequences in an autonomous agent, we therefore describe a robot architecture into which such a model is embedded, and require it to control action selection in a robotic task inspired by animal observations. Our results demonstrate effective action selection by the embedded model under a wide range of sensory and motivational conditions. When confronted with multiple, high salience alternatives, the robot also exhibits forms of behavioral disintegration that show similarities to animal behavior in conflict situations. The model is shown to cast light on recent neurobiological findings concerning behavioral switching and sequencing