The functional logic of corticostriatal connections

Unidirectional connections from the cortex to the matrix of the corpus striatum initiate the cortico-basal ganglia (BG)-thalamocortical loop, thought to be important in momentary action selection and in longer-term fine tuning of behavioural repertoire; a discrete set of striatal compartments, striosomes, has the complementary role of registering or anticipating reward that shapes corticostriatal plasticity. Re-entrant signals traversing the cortico-BG loop impact predominantly frontal cortices, conveyed through topographically ordered output channels; by contrast, striatal input signals originate from a far broader span of cortex, and are far more divergent in their termination. The term ‘disclosed loop’ is introduced to describe this organisation: a closed circuit that is open to outside influence at the initial stage of cortical input. The closed circuit component of corticostriatal afferents is newly dubbed ‘operative’, as it is proposed to establish the bid for action selection on the part of an incipient cortical action plan; the broader set of converging corticostriatal afferents is described as contextual. A corollary of this proposal is that every unit of the striatal volume, including the long, C-shaped tail of the caudate nucleus, should receive a mandatory component of operative input, and hence include at least one area of BG-recipient cortex amongst the sources of its corticostriatal afferents. Individual operative afferents contact twin classes of GABAergic striatal projection neuron (SPN), distinguished by their neurochemical character, and onward circuitry. This is the basis of the classic direct and indirect pathway model of the cortico-BG loop. Each pathway utilises a serial chain of inhibition, with two such links, or three, providing positive and negative feedback, respectively. Operative co-activation of direct and indirect SPNs is, therefore, pictured to simultaneously promote action, and to restrain it. The balance of this rival activity is determined by the contextual inputs, which summarise the external and internal sensory environment, and the state of ongoing behavioural priorities. Notably, the distributed sources of contextual convergence upon a striatal locus mirror the transcortical network harnessed by the origin of the operative input to that locus, thereby capturing a similar set of contingencies relevant to determining action. The disclosed loop formulation of corticostriatal and subsequent BG loop circuitry, as advanced here, refines the operating rationale of the classic model and allows the integration of more recent anatomical and physiological data, some of which can appear at variance with the classic model. Equally, it provides a lucid functional context for continuing cellular studies of SPN biophysics and mechanisms of synaptic plasticity

Konnektivität und Dynamik der Synaptischen Kontrolle des Nucleus Subthalamicus

The Subthalamic Nucleus (STN) is part of the basal ganglia, integrates glutamatergic cortical (hyperdirect) and pallidal GABAergic (indirect) inputs and projects to all output structures of the basal ganglia. As a target of deep brain stimulation (DBS), the STN is of clinical interest to treat symptoms of Parkinson’s Disease. Dynamics of neuronal synchronization in the STN have been shown to shape the nucleus` function in both health and disease and to be directly modulated by therapeutic DBS. Yet knowledge of intrinsic and afferent STN connectivity, which may underlie the synaptic control of STN neuronal synchronization, is limited. In this study, we investigate connectivity rules in the rat STN by means of simultaneous multiple-cell patch-clamp recordings in combination with extracellular electrical stimulation and neuroanatomical analysis. In terms of intrinsic connectivity, our findings suggest a lack of mutual synaptic connections between STN neurons. Analysis of afferent connectivity revealed a sparse and selective innervation of local clusters of STN neurons by both glutamatergic and GABAergic fibers. Activation of glutamatergic input in isolation resulted in highly synchronous recruitment of STN neurons, whereas co-stimulation of GABAergic input delayed and desynchronized action potential (AP) generation. While extracellular electrical stimulation at low frequencies depressed both glutamatergic and GABAergic inputs to a similar degree, DBS-like frequencies of 130 Hz resulted in a significantly stronger depression of glutamatergic inputs compared to depression of GABAergic inputs. Recovery from short-term depression was complete at both GABAergic and glutamatergic synapses within seconds. In summary, our findings indicate that STN neurons operate as parallel processing units. Hence, synchronization of local clusters of neurons in the STN is likely to depend on upstream structures, interacting with the STN via sparse and specific synaptic single fiber input. The vulnerability of glutamatergic input to synaptic depression at DBS-like frequencies suggests a DBS mechanism of action that is based on a decoupling of the STN from direct cortical synchronization and a shift to desynchronizing GABAergic input. This may contribute to the effect of electrical stimulation, counteracting exaggerated neuronal synchronization in Parkinson’s Disease. Together, the rapid time course of differential short-term depression at high stimulation frequencies and the subsequent fast synaptic recovery provide assets for a moment-to-moment control of neuronal synchrony that next-generation DBS aims for.Der Nucleus Subthalamicus (STN) ist Teil der Basalganglien, integriert glutamaterge, kortikale (hyperdirekter Pfad) und GABAerge, pallidale (indirekter Pfad) Eingänge und ist direkt mit allen Ausgangstrukturen der Basalganglien verschaltet. Als Zielstruktur der Tiefen Hirnstimulation (THS) ist er von klinischem Interesse für die symptomatische Therapie des Morbus Parkinson. Neuronale Synchronisationsdynamiken bestimmen die Funktion des STN in physiologischen und pathologischen Zuständen und werden durch therapeutisch wirksame THS direkt moduliert. Dennoch ist das Wissen über synaptische Verschaltungsprinzipien der intrinsischen und afferenten Konnektivität, die solchen Synchronisationsdynamiken zugrunde liegen, beschränkt. In dieser Studie untersuchen wir synaptische Verschaltungsprinzipien im STN der Ratte mittels simultaner multipler Patch-Clamp Ableitungen in Kombination mit extrazellulärer elektrischer Stimulation und neuroanatomischer Analyse. Bezüglich intrinsischer Konnektivität legen unsere Ergebnisse nahe, dass es keine direkten synaptischen Verbindungen zwischen STN Neuronen gibt. Die Analyse der afferenten Verschaltungsmuster zeigte eine selektive Innervation lokaler Cluster von STN-Neuronen durch glutamaterge und GABAerge Fasern. Aktivierung von glutamatergen Afferenzen in Isolation löste eine hochsynchrone Rekrutierung von STN-Neuronen aus, während eine Co-Stimulation GABAerger Eingänge zu einer Verzögerung und Desynchronisation der generierten Aktionspotentiale führte. Während die synaptische Kurzzeitdepression für glutamaterge und GABAerge Eingänge bei niedrigfrequenter extrazellulärer elektrischer Stimulation vergleichbar war, führten THS-ähnliche Stimulationsfrequenzen von 130 Hz zu einer signifikant stärkeren Kurzzeitdepression glutamaterger im Vergleich zu GABAergen Eingängen. Die synaptische Depression sowohl glutamaterger als auch GABAerger Eingänge zeigte sich innerhalb von Sekunden reversibel. Zusammenfassend legen die Ergebnisse dieser Studie nahe, dass STN Neurone als parallele Prozessierungseinheiten operieren. Somit hängt die Synchronisation lokaler Cluster von STN Neuronen mutmaßlich von vorgeschaltenen Regionen ab, die über selektive Verschaltungen mit dem STN interagieren. Die Vulnerabilität glutamaterger Transmission bei THS-ähnlichen Stimualtionsfrequenzen impliziert eine Abkopplung von direkter kortikaler Synchronisierung, während zeitgleich eine Verschiebung hin zu desynchronisierenden GABAergen Eingängen stattfindet. Dies trägt möglicherweise zu einer Suppression pathologisch erhöhter neuraler Synchronität, wie sie beim Morbus Parkinson vorkommt, bei. Zusammen stellen der rapide zeitliche Verlauf der differenziellen Kurzzeitplastizität bei hohen Stimulationsfrequenzen und die darauffolgende schnelle synaptische Erholung Voraussetzungen einer zeitlich präzisen Kontrolle neuronaler Synchronität im STN dar, die bei Weiterentwicklungen der THS angestrebt wird

Approccio modellistico del sistema di controllo motorio nella malattia di parkinson

Parkinson’s disease is a neurodegenerative disorder due to the death of the dopaminergic neurons of the substantia nigra of the basal ganglia. The process that leads to these neural alterations is still unknown. Parkinson’s disease affects most of all the motor sphere, with a wide array of impairment such as bradykinesia, akinesia, tremor, postural instability and singular phenomena such as freezing of gait. Moreover, in the last few years the fact that the degeneration in the basal ganglia circuitry induces not only motor but also cognitive alterations, not necessarily implicating dementia, and that dopamine loss induces also further implications due to dopamine-driven synaptic plasticity got more attention. At the present moment, no neuroprotective treatment is available, and even if dopamine-replacement therapies as well as electrical deep brain stimulation are able to improve the life conditions of the patients, they often present side effects on the long term, and cannot recover the neural loss, which instead continues to advance. In the present thesis both motor and cognitive aspects of Parkinson’s disease and basal ganglia circuitry were investigated, at first focusing on Parkinson’s disease sensory and balance issues by means of a new instrumented method based on inertial sensor to provide further information about postural control and postural strategies used to attain balance, then applying this newly developed approach to assess balance control in mild and severe patients, both ON and OFF levodopa replacement. Given the inability of levodopa to recover balance issues and the new physiological findings than underline the importance in Parkinson’s disease of non-dopaminergic neurotransmitters, it was therefore developed an original computational model focusing on acetylcholine, the most promising neurotransmitter according to physiology, and its role in synaptic plasticity. The rationale of this thesis is that a multidisciplinary approach could gain insight into Parkinson’s disease features still unresolved

A mathematical model of levodopa medication effect on basal ganglia in parkinson’s disease: An application to the alternate finger tapping task

Malfunctions in the neural circuitry of the basal ganglia (BG), induced by alterations in the dopaminergic system, are responsible for an array of motor disorders and milder cognitive issues in Parkinson's disease (PD). Recently Baston and Ursino (2015a) presented a new neuroscience mathematical model aimed at exploring the role of basal ganglia in action selection. The model is biologically inspired and reproduces the main BG structures and pathways, modeling explicitly both the dopaminergic and the cholinergic system. The present work aims at interfacing this neurocomputational model with a compartmental model of levodopa, to propose a general model of medicated Parkinson's disease. Levodopa effect on the striatum was simulated with a two-compartment model of pharmacokinetics in plasma joined with a motor effect compartment. The latter is characterized by the levodopa removal rate and by a sigmoidal relationship (Hill law) between concentration and effect. The main parameters of this relationship are saturation, steepness, and the half-maximum concentration. The effect of levodopa is then summed to a term representing the endogenous dopamine effect, and is used as an external input for the neurocomputation model; this allows both the temporal aspects of medication and the individual patient characteristics to be simulated. The frequency of alternate tapping is then used as the outcome of the whole model, to simulate effective clinical scores. Pharmacokinetic-pharmacodynamic modeling was preliminary performed on data of six patients with Parkinson's disease (both “stable” and “wearing-off” responders) after levodopa standardized oral dosing over 4 h. Results show that the model is able to reproduce the temporal profiles of levodopa in plasma and the finger tapping frequency in all patients, discriminating between different patterns of levodopa motor response. The more influential parameters are the Hill coefficient, related with the slope of the effect sigmoidal relationship, the drug concentration at half-maximum effect, and the drug removal rate from the effect compartment. The model can be of value to gain a deeper understanding on the pharmacokinetics and pharmacodynamics of the medication, and on the way dopamine is exploited in the neural circuitry of the basal ganglia in patients at different stages of the disease progression

A computational model of cortical-striatal mediation of speed-accuracy tradeoff and habit formation emerging from anatomical gradients in dopamine physiology and reinforcement learning

Decision making – committing to a single action from a plethora of viable alternatives – is a necessity for all motile creatures, each moving a single body to many possible destinations. Some decisions are better than others. For example, to a rat deciding between one path that will bring it to a piece of cheese and another that will bring it to the jaws of a cat, there is a clear reason for the rat to prefer one choice over the other. Two criteria for adjusting decision making for optimal outcome are to make decisions as accurately as possible – choose the course of action most likely to result in the preferred outcome – but also to decide as fast as possible. Because these criteria often conflict, decision making has an inherent “speed-accuracy tradeoff”. Presented here is a computational neural model of decision making, which incorporates neurobiological design principles that optimize this tradeoff via reward-guided transfers of control between two sensory processing systems with different speed/accuracy characteristics. This model incorporates anatomical and physiological evidence that dopamine, the key neurotransmitter in reinforcement learning, has varying effects in different sub-regions of the basal ganglia, a subcortical structure that interfaces with the neocortex to control behavior. Based on the observed differences between these sub-regions, the model proposes that gradual adaptations of synaptic links by reinforcement learning signals lead to rapid changes in the speed and accuracy of decision making, by assigning control of behavior to alternative cortical representations. Chapter one draws conceptual links from experimental data to the design of the proposed model. Chapter two applies the model to speed-accuracy tradeoffs and habit formation by simulating forced-choice paradigms. Several robust behavioral phenomena are replicated. By isolating reinforcement learning factors that control the speed and depth of habit formation, the model can help explain why all substances that strongly and synergistically affect such factors share a high potential for habit formation, or habit abatement. To illustrate such potential applications of the current model, chapter three investigates effects of varying model parameters in accord with the known neurochemical effects of some major habit-forming substances, such as cocaine and ethanol

Mean-field analysis of basal ganglia and thalamocortical dynamics

When modeling a system as complex as the brain, considerable simplifications are inevitable. The nature of these simplifications depends on the available experimental evidence, and the desired form of model predictions. A focus on the former often inspires models of networks of individual neurons, since properties of single cells are more easily measured than those of entire populations. However, if the goal is to describe the processes responsible for the electroencephalogram (EEG), such models can become unmanageable due to the large numbers of neurons involved. Mean-field models in which assemblies of neurons are represented by their average properties allow activity underlying the EEG to be captured in a tractable manner. The starting point of the results presented here is a recent physiologically-based mean-field model of the corticothalamic system, which includes populations of excitatory and inhibitory cortical neurons, and an excitatory population representing the thalamic relay nuclei, reciprocally connected with the cortex and the inhibitory thalamic reticular nucleus. The average firing rates of these populations depend nonlinearly on their membrane potentials, which are determined by afferent inputs after axonal propagation and dendritic and synaptic delays. It has been found that neuronal activity spreads in an approximately wavelike fashion across the cortex, which is modeled as a two-dimensional surface. On the basis of the literature, the EEG signal is assumed to be roughly proportional to the activity of cortical excitatory neurons, allowing physiological parameters to be extracted by inverse modeling of empirical EEG spectra. One objective of the present work is to characterize the statistical distributions of fitted model parameters in the healthy population. Variability of model parameters within and between individuals is assessed over time scales of minutes to more than a year, and compared with the variability of classical quantitative EEG (qEEG) parameters. These parameters are generally not normally distributed, and transformations toward the normal distribution are often used to facilitate statistical analysis. However, no single optimal transformation exists to render data distributions approximately normal. A uniformly applicable solution that not only yields data following the normal distribution as closely as possible, but also increases test-retest reliability, is described in Chapter 2. Specialized versions of this transformation have been known for some time in the statistical literature, but it has not previously found its way to the empirical sciences. Chapter 3 contains the study of intra-individual and inter-individual variability in model parameters, also providing a comparison of test-retest reliability with that of commonly used EEG spectral measures such as band powers and the frequency of the alpha peak. It is found that the combined model parameters provide a reliable characterization of an individual's EEG spectrum, where some parameters are more informative than others. Classical quantitative EEG measures are found to be somewhat more reproducible than model parameters. However, the latter have the advantage of providing direct connections with the underlying physiology. In addition, model parameters are complementary to classical measures in that they capture more information about spectral structure. Another conclusion from this work was that a few minutes of alert eyes-closed EEG already contain most of the individual variability likely to occur in this state on the scale of years. In Chapter 4, age trends in model parameters are investigated for a large sample of healthy subjects aged 6-86 years. Sex differences in parameter distributions and trends are considered in three age ranges, and related to the relevant literature. We also look at changes in inter-individual variance across age, and find that subjects are in many respects maximally different around adolescence. This study forms the basis for prospective comparisons with age trends in evoked response potentials (ERPs) and alpha peak morphology, besides providing a standard for the assessment of clinical data. It is the first study to report physiologically-based parameters for such a large sample of EEG data. The second main thrust of this work is toward incorporating the thalamocortical system and the basal ganglia in a unified framework. The basal ganglia are a group of gray matter structures reciprocally connected with the thalamus and cortex, both significantly influencing, and influenced by, their activity. Abnormalities in the basal ganglia are associated with various disorders, including schizophrenia, Huntington's disease, and Parkinson's disease. A model of the basal ganglia-thalamocortical system is presented in Chapter 5, and used to investigate changes in average firing rates often measured in parkinsonian patients and animal models of Parkinson's disease. Modeling results support the hypothesis that two pathways through the basal ganglia (the so-called direct and indirect pathways) are differentially affected by the dopamine depletion that is the hallmark of Parkinson's disease. However, alterations in other components of the system are also suggested by matching model predictions to experimental data. The dynamics of the model are explored in detail in Chapter 6. Electrophysiological aspects of Parkinson's disease include frequency reduction of the alpha peak, increased relative power at lower frequencies, and abnormal synchronized fluctuations in firing rates. It is shown that the same parameter variations that reproduce realistic changes in mean firing rates can also account for EEG frequency reduction by increasing the strength of the indirect pathway, which exerts an inhibitory effect on the cortex. Furthermore, even more strongly connected subcircuits in the indirect pathway can sustain limit cycle oscillations around 5 Hz, in accord with oscillations at this frequency often observed in tremulous patients. Additionally, oscillations around 20 Hz that are normally present in corticothalamic circuits can spread to the basal ganglia when both corticothalamic and indirect circuits have large gains. The model also accounts for changes in the responsiveness of the components of the basal ganglia-thalamocortical system, and increased synchronization upon dopamine depletion, which plausibly reflect the loss of specificity of neuronal signaling pathways in the parkinsonian basal ganglia. Thus, a parsimonious explanation is provided for many electrophysiological correlates of Parkinson's disease using a single set of parameter changes with respect to the healthy state. Overall, we conclude that mean-field models of brain electrophysiology possess a versatility that allows them to be usefully applied in a variety of scenarios. Such models allow information about underlying physiology to be extracted from the experimental EEG, complementing traditional measures that may be more statistically robust but do not provide a direct link with physiology. Furthermore, there is ample opportunity for future developments, extending the basic model to encompass different neuronal systems, connections, and mechanisms. The basal ganglia are an important addition, not only leading to unified explanations for many hitherto disparate phenomena, but also contributing to the validation of this form of modeling

A Dual-Process Model of Response Inhibition: Insights from a Neurocognitive Perspective

Perhaps the most critically important cognitive mechanism for survival and social cohesion is the ability to withhold an action that has been rendered maladaptive or inappropriate by altered environmental demands. There is a large body of empirical research investigating this process, which is commonly referred to as response inhibition, but which in most instances more precisely could be termed reactive inhibition because it constitutes only one element of the overall inhibition of an action. Alongside reactive inhibition, though, and certainly of at least equally import, is the capacity to recognise erroneous stimulus-response patterns in one’s own behaviour and to remediate them where they arise. This has been termed proactive inhibition and has received substantially less experimental interest until very recently, despite almost certainly contributing to overall response inhibition. Although these two cognitive mechanisms, reactive and proactive inhibition, are necessarily interdependent, they are representationally distinct and are therefore likely implemented by separate biological and cognitive processes. The basal ganglia are largely responsible for the coordination of motor control, and its neural connections to the motor and frontal cortices plan, select, and direct any intended movement, and indeed certain unintended movements also. Owing to an incomplete physiological characterisation of this circuitry until only the last decade, a critical re- evaluation of those motor functions that rely on computational cognition is germane. It is likely that reactive inhibition recruits internal basal ganglia pathways, perhaps in accordance with the classical dual-organisation model of direct and indirect pathways, because it is principally a motor function; proactive inhibition, on the other hand, requires cognitive computation, either consciously or not, and, therefore, may recruit a recently-described hyperdirect pathway that connects the basal ganglia to a prefrontal neural population that has previously been associated with overall response inhibition, but whose role has been theoretically inconsistent with motor models of inhibition because prefrontal regions are associated with higher cognitive functions and not motor function. With these limitations in mind, in this thesis, I present the experimental findings of four empirical investigations into the neurocognitive architecture of proactive inhibition using updated models in order to revise the understanding of response inhibition and, in particular, the role and underlying properties of proactive inhibition, which we operationalise as post- error slowing (PES) of reaction time. In the first study (N = 264), we investigated the role of two dopaminergic single- nucleotide polymorphisms (DRD1 rs686 and DRD2 rs1800497) which are differentially expressed along basal ganglia pathways in behavioural performance on a Go/No-Go task (the Sustained Attention to Reaction Time task, SART). We found that in those with a higher ratio of D1:D2 receptors (i.e., more rs686 A and rs1800497 T alleles) PES was engaged to a higher degree and that older age magnified this genetic effect (p < .001). In addition, we observed an interaction between age and a general factor of intelligence, g, on PES, whereby older age and lower estimates of g predicted higher recruitment of PES (p < .001). This supports the hypothesis that proactive inhibition appears to be a naturally-occurring compensatory mechanism which manifests in individuals whose reactive inhibition may be suboptimal, and indicates that the extent to which PES is engaged depends on increased dopamine D1 and decreased D2 neurotransmission. The neural generators of overall response inhibition are well described, but very little effort has been given to proactive processes. If reactive inhibition is largely motoric, then its sources can be localised using various techniques that image neural regions using haemodynamic response, but since proactive inhibition is largely cognitive, it is necessary to use other methods. To investigate the cognitive architecture of proactive inhibition we used electroencephalography (EEG). To do this, we use stimulus- and response- locked neural activity to compare the four major accounts of PES. These accounts each have wide support, explain behavioural data, and can be simulated using computational methods. We administered the SART once again to N = 100 healthy young adults and recorded their brain activity using EEG. Our results provide support for an attentional account of PES that supposes errors disturb, or disorient, attentional processing on subsequent trials indexed by the anterior N1. The N1 was significantly blunted by errors (p = .020) and the post-error N1 was correlated with magnitude of PES (p = .016). In addition, we provide additional support for our previous findings indicating an effect of age and g on PES. Here, we find that the post-error N1 diminishes with natural ageing, however, higher estimated g seemed to rescue these age-related deficits (p < .0001). These results bring into question our previous hypothesis that PES is a compensatory mechanism. Rather, it may be a consequence of disruptions to processing that incidentally improve response inhibition as a function of that disruption which offsets the initiation of response execution. Our third study was conducted to investigate the potential efficacy of neurostimulation techniques in the modulation of response inhibition and other cognitive and behavioural functions using transcranial direct current stimulation (tDCS). This study had two experiments. The first investigated whether such functions could be modulated, and the second investigated the nature of that modulation, namely, whether it could be attributed to neuroplastic induction measured by changes to motor evoked potentials using transcranial magnetic stimulation. In the first experiment, our participants (N = 56) attended three sessions, a baseline session followed the following day by single-blind, randomly allocated stimulation testing sessions separated by two days, one with a sham control, and the other with active anodal tDCS to the motor cortex. We administered a Simple and Choice Reaction Time (RT) task, the Inspection Time task, and the SART. This battery allows us to disambiguate perceptual, motor, and cognitive elements of a physical action. We observed no effect on either RT or Inspection Time and observed an effect on the proactive process on the SART (p = .002), such that PES was engaged to a smaller degree after active stimulation compared to both baseline and the sham condition. Likewise, we observed somewhat quicker RT in the SART under active stimulation (p = .073), likely because of the absence of PES, as well as more errors (p = .026), potentially indicating that PES may protect against failures of response inhibition. We attribute these results to the location of the cathode, over the right supraorbital region, roughly above the right inferior frontal gyrus. The anode in tDCS is thought to synchronise neural activity and induce long-term potentiation-like neuroplasticity, whereas the necessary cathode is thought to disrupt such synchronicity. As such, we may have disrupted prefrontal cortical functioning briefly, which in turn eroded proactive functioning. This provides reasonably strong support for frontal regions being implicated in proactive, but not necessarily reactive, inhibition, although we cannot conclude this since overall response inhibition was somewhat disrupted. The final study addresses the theoretical and conceptual limitations in existing response inhibition tasks by implementing a recent Bayesian Ψ adaptive staircase (Livesey & Livesey, 2016) in novel instantiations of two Stop-Signal Tasks (SSTs) that we developed for the purpose of directly observing behavioural proactive inhibition in two forms that are explicitly separable to the reactive process. The Ψ staircase provides an algorithm which allows for rapid estimation of SSRT in very few trials, the importance of which lies in the populations whose response inhibition and behavioural and motoric regulation are impaired due to psychopathology or neurodegeneration. Task duration is a considerable limitation on reliable estimates of performance on such tasks, and particularly in such populations. We administered four tasks (two SSTs and two Go/No-Go tasks) to N = 123 healthy young adults. We included a manipulation that cued the probability of a Stop/No-Go trial in the two SSTs and one of the Go/No-Go tasks, which was a modified form of the SART. These two probability conditions allow us to compare RT in each condition on Go trials, under the assumption that longer RT in higher p(Stop/No-Go) conditions indicates a predictive form of proactive inhibition. This is distinct from the remedial form, post-error slowing, that can still be observed in the tasks. We report two important findings. The first is that the Ψ staircase is highly successful in rapidly converging on reliable estimates of SSRT in as few as 20 stop trials, which could prove useful in designing considerably shorter tasks in the future without sacrificing reliability. Secondly, we show that predictive and remedial forms of proactive inhibition are consistently engaged in all tasks, potentially providing another avenue for thinking about proactive inhibition in the future. Thirdly, we show that estimates of SSRT, which aims to assess reactive inhibition, are robust against proactive inhibition. Taken together, the conclusions reached in this thesis represent a critical update of the neurobiology that underlies newly-discretised cognitive processes that contribute to response inhibition, as well as their psychophysiological characteristics. We have demonstrated that proactive inhibition at least partly reflects a compensatory mechanism that appears to be naturally-occurring in individuals whose reactive processes may be insufficient for psychological and biological reasons as well as individual differences in intellectual capacity. Furthermore, we present and validate a novel, theoretically cogent task paradigm to measure what we posit are discrete processes within the proactive process: remedial and predictive proactive inhibition. Given what appears to be a naturally-occurring compensatory mechanism alongside post-error slowing that corresponds to the timing of a pre-error negative inflection in electrophysiological recordings, this work raises fascinating questions about the distinction between conscious, preconscious, and subconscious brain states and their effect on behaviour.Thesis (Ph.D.) -- University of Adelaide, School of Psychology, 202

Simultaneous activation of multiple memory systems during learning : insights from electrophysiology and modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references.Parallel cortico-basal ganglia loops are thought to give rise to a diverse set of limbic, associative and motor functions, but little is known about how these loops operate and how their neural activities evolve during learning. To address these issues, single-unit activity was recorded simultaneously in dorsolateral (sensorimotor) and dorsomedial (associative) regions of the striatum as rats learned two versions of a conditional T-maze task. The results demonstrate that contrasting patterns of activity developed in these regions during task performance, and evolved with different training-related dynamics. Oscillatory activity is thought to enable memory storage and replay, and may encourage the efficient transmission of information between brain regions. In a second set of experiments, local field potentials (LFPs) were recorded simultaneously from the dorsal striatum and the CAl field of the hippocampus, as rats engaged in spontaneous and instructed behaviors in the T-maze. Two major findings are reported. First, striatal LFPs showed prominent theta-band rhythms that were strongly modulated during behavior. Second, striatal and hippocampal theta rhythms were modulated differently during T-maze performance, and in rats that successfully learned the task, became highly coherent during the choice period. To formalize the hypothesized contributions of dorsolateral and dorsomedial striatum during T-maze learning, a computational model was developed. This model localizes a model-free reinforcement learning (RL) system to the sensorimotor cortico-basal ganglia loop and localizes a model-based RL system to a network of structures including the associative cortico-basal ganglia loop and the hippocampus. Two models of dorsomedial striatal function were investigated, both of which can account for the patterns of activation observed during T-maze training. The two models make differing predictions regarding activation of the dorsomedial striatum following lesions of the model-free system, depending on whether it serves a direct role in action selection through participation in a model-based planning system or whether it participates in arbitrating between the model-free and model-based controllers. Combined, the work presented in this thesis shows that a large network of forebrain structures is engaged during procedural learning. The results suggest that coordination across regions may be required for successful learning and/or task performance, and that the different regions may contribute to behavioral performance by performing distinct RL computations.by Catherine Ann Thorn.Ph.D