Interactions between the Midbrain Superior Colliculus and the Basal Ganglia

An important component of the architecture of cortico-basal ganglia connections is the parallel, re-entrant looped projections that originate and return to specific regions of the cerebral cortex. However, such loops are unlikely to have been the first evolutionary example of a closed-loop architecture involving the basal ganglia. A phylogenetically older, series of subcortical loops can be shown to link the basal ganglia with many brainstem sensorimotor structures. While the characteristics of individual components of potential subcortical re-entrant loops have been documented, the full extent to which they represent functionally segregated parallel projecting channels remains to be determined. However, for one midbrain structure, the superior colliculus (SC), anatomical evidence for closed-loop connectivity with the basal ganglia is robust, and can serve as an example against which the loop hypothesis can be evaluated for other subcortical structures. Examination of ascending projections from the SC to the thalamus suggests there may be multiple functionally segregated systems. The SC also provides afferent signals to the other principal input nuclei of the basal ganglia, the dopaminergic neurones in substantia nigra and to the subthalamic nucleus. Recent electrophysiological investigations show that the afferent signals originating in the SC carry important information concerning the onset of biologically significant events to each of the basal ganglia input nuclei. Such signals are widely regarded as crucial for the proposed functions of selection and reinforcement learning with which the basal ganglia have so often been associated

The role of response mechanisms in determining reaction time performance: Piéron’s Law revisited

A response mechanism takes evaluations of the importance of potential actions and selects the most suitable. Response mechanism function is a nontrivial problem that has not received the attention it deserves within cognitive psychology. In this article, we make a case for the importance of considering response mechanism function as a constraint on cognitive processes and emphasized links with the wider problem of behavioral action selection. First, we show that, contrary to previous suggestions, a well–known model of the Stroop task (Cohen, Dunbar, & McClelland, 1990) relies on the response mechanism for a key feature of its results—the interference–facilitation asymmetry. Second, we examine a variety of response mechanisms (including that in the model of Cohen et al., 1990) and show that they all follow a law analogous to Piéron's law in relating their input to reaction time. In particular, this is true of a decision mechanism not designed to explain RT data but based on a proposed solution to the general problem of action selection and grounded in the neurobiology of the vertebrate basal ganglia. Finally, we show that the dynamics of simple artificial neurons also support a Piéron–like law

Dynamics of reward based decision making a computational study

International audienceWe consider a biologically plausible model of the basal gan-glia that is able to learn a probabilistic two armed bandit task using reinforcement learning. This model is able to choose the best option and to reach optimal performances after only a few trials. However, we show in this study that the influence of exogenous factors such as stimuli salience and/or timing seems to prevail over optimal decision making, hence questioning the very definition of action-selection. What are the ecological conditions for optimal action selection

I Think Then I Will: The Function of the Cortex in the Process of Decision Making and Initiating Action

Özet Karar verme, mevcut problemi çözüme kavuşturmaya odaklı bir yöneliş, karar verme eylemi ise mevcut seçenekleri değerlendirme, analiz etme ve sonuçlarını takip etme gibi spesifik etkinliklerden oluşan karmaşık bir işlevdir. Karar alabilme kabiliyeti zihinsel analiz, planlama, üst düzey düşünme (koşullardaki değişikliklere uygun olarak cevabı değiştirebilme yetisi gibi), eylemi başlatma ve yürütme gibi bilişsel süreçleri içermektedir. Bu süreç eylem seçimi kapsamında ilişkisel öğrenme, duygusal ve sosyal yönler dâhil olmak üzere çeşitli bileşenlere ayrılmaktadır. Karar vermenin bu farklı yönleri, bilişsel sinirbilim alanındaki araştırmaların odak noktası haline gelmiştir. Özellikle son yıllarda ventromedial prefrontal korteks ve ilgili yapıların karar vermede anahtar bir role sahip olmasının bilişin oldukça karmaşık yönü olduğu düşünülen korteks temelini anlamamızda yol katedildiğini göstermektedir. Bu çalışma ile orbitofrontal korteks ve ventral striatum gibi karar verme süreçlerinde etkin rol alan beyin bölgeleri hakkında yeni perspektifler kazandırmak amaçlanmaktadır. Özellikle, karar verme sürecinin bellek ve öğrenme ile sıkı bir şekilde bağlantılı olduğu giderek daha açık hale gelmektedir. Özünde karar verme, geçmişin ve gelecekteki eylemlerin hafızası arasındaki bağlantı olarak görülebilmektedir. Bu çalışma, karar verme ve eylemi başlatma sürecinde özellikle öğrenme ve hafıza bağlantılarına odaklanarak ve prefrontal korteks içindeki bölgelere özel bir vurgu yaparak bu yapıları gözden geçirmektedir.Decision-making is a complex orientation that is focused on solving the current problem, and act of decision-making is a complex function that consists of specific activities such as evaluating available options, analyzing and tracking their results. The ability to make decisions includes cognitive processes such as mental analysis, planning, higher-order thinking (such as the ability to change the response to changes in circumstances), initiating and executing action. This process is divided into various components, including associative learning, emotional and social aspects, within the scope of action selection. These different aspects of decision-making have been the focus of investigation in recent studies. Especially in recent years, the fact that the ventromedial prefrontal cortex and related structures have a key role in decision making has led to progress in our understanding of the cortex basis, which is thought to be a very complex aspect of cognition. This work has provided fresh perspectives on poorly understood brain regions, such as orbitofrontal cortex and ventral striatum. In particular, it is increasingly clear that decision-making is tightly interlinked with learning and memory. Indeed, decision-making can be seen as the link between memory of the past and future actions. This study reviews these structures in the decision-making process, with a particular focus on learning and memory connections and with a special emphasis on regions within the prefrontal corte

A basal ganglia inspired model of action selection evaluated in a robotic survival task.

The basal ganglia system has been proposed as a possible neural substrate for action selection in the vertebrate brain. We describe a robotic implementation of a model of the basal ganglia and demonstrate the capacity of this system to generate adaptive switching between several acts when embedded in a robot that has to "survive" in a laboratory environment. A comparison between this brain-inspired selection mechanism and classical "winner-takes-all" selection highlights some adaptive properties specific to the model, such as avoidance of dithering and energy-saving. These properties derive, in part, from the capacity of simulated basal ganglia-thalamo-cortical loops to generate appropriate "behavioral persistence"

Insights into Parkinson’s disease from computational models of the basal ganglia

Movement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson's disease as a case study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15-30?Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms and treatments for Parkinson's disease

Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit

The striatum, the principal input structure of the basal ganglia, is crucial to both motor control and learning. It receives convergent input from all over the neocortex, hippocampal formation, amygdala and thalamus, and is the primary recipient of dopamine in the brain. Within the striatum is a GABAergic microcircuit that acts upon these inputs, formed by the dominant medium-spiny projection neurons (MSNs) and fast-spiking interneurons (FSIs). There has been little progress in understanding the computations it performs, hampered by the non-laminar structure that prevents identification of a repeating canonical microcircuit. We here begin the identification of potential dynamically-defined computational elements within the striatum. We construct a new three-dimensional model of the striatal microcircuit's connectivity, and instantiate this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs is introduced and tuned to experimental data. We introduce a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We find that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appear, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation is strongly dependent on the simulated concentration of dopamine. We also show that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. Such small cell assemblies forming spontaneously only in the absence of dopamine may contribute to motor control problems seen in humans and animals following a loss of dopamine cells. (C) 2009 Elsevier Ltd. All rights reserved

Technical Integration of Hippocampus, Basal Ganglia and Physical Models for Spatial Navigation

Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large-scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings